From df44de8df83989cab70edbcb3da59e544f3387c5 Mon Sep 17 00:00:00 2001 From: spark-rapids automation <70000568+nvauto@users.noreply.github.com> Date: Tue, 10 Sep 2024 20:48:58 +0000 Subject: [PATCH 01/24] Update dev-version by jenkins-spark-rapids-tools-auto-release-90 Signed-off-by: spark-rapids automation <70000568+nvauto@users.noreply.github.com> --- core/pom.xml | 2 +- user_tools/src/spark_rapids_pytools/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/core/pom.xml b/core/pom.xml index ec0419865..e5f56a9f1 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -23,7 +23,7 @@ rapids-4-spark-tools_2.12 RAPIDS Accelerator for Apache Spark tools RAPIDS Accelerator for Apache Spark tools - 24.08.2-SNAPSHOT + 24.08.3-SNAPSHOT jar http://github.com/NVIDIA/spark-rapids-tools diff --git a/user_tools/src/spark_rapids_pytools/__init__.py b/user_tools/src/spark_rapids_pytools/__init__.py index a5b3c2314..094ea381e 100644 --- a/user_tools/src/spark_rapids_pytools/__init__.py +++ b/user_tools/src/spark_rapids_pytools/__init__.py @@ -16,7 +16,7 @@ from spark_rapids_pytools.build import get_version, get_spark_dep_version -VERSION = '24.08.2' +VERSION = '24.08.3' # defines the default runtime build version for the user tools environment SPARK_DEP_VERSION = '350' __version__ = get_version(VERSION) From 6a05d7e33125cc4c263f86322a8304fb96de84c6 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Mon, 16 Sep 2024 12:53:42 -0400 Subject: [PATCH 02/24] Reduce console output tree level, exclude JAR tool output files and remove incorrect logging (#1340) * Reduce tree depth level in console output and exclude JAR tool output files Signed-off-by: Partho Sarthi * Remove incorrect warning for filter apps type Signed-off-by: Partho Sarthi * Remove keyword 'savings' from console output Signed-off-by: Partho Sarthi --------- Signed-off-by: Partho Sarthi --- .../src/spark_rapids_pytools/rapids/qualification.py | 7 ------- .../resources/qualification-conf.yaml | 8 +++----- 2 files changed, 3 insertions(+), 12 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/rapids/qualification.py b/user_tools/src/spark_rapids_pytools/rapids/qualification.py index 18d0271ac..3e49a81a6 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/qualification.py +++ b/user_tools/src/spark_rapids_pytools/rapids/qualification.py @@ -141,13 +141,6 @@ def _process_offline_cluster_args(self) -> None: def __process_filter_args(self, arg_val: str) -> None: selected_filter = QualFilterApp.fromstring(arg_val) - available_filters = [filter_enum.value for filter_enum in QualFilterApp] - self.logger.warning( - 'Invalid argument filter_apps=%s.\n\t' - 'Accepted options are: [%s].\n\t' - 'Falling-back to default filter: %s', - arg_val, Utils.gen_joined_str(' | ', available_filters), - QualFilterApp.tostring(selected_filter)) self.ctxt.set_ctxt('filterApps', selected_filter) def _process_estimation_model_args(self) -> None: diff --git a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml index e23034736..4c250611f 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -111,10 +111,7 @@ local: files: summary: name: qualification_summary.csv - outputComment: "Summarized savings and speedups CSV report" - full: - name: qualification_summary_full.csv - outputComment: "Full savings and speedups CSV report" + outputComment: "Summarized speedups CSV report" statistics: name: qualification_statistics.csv outputComment: "Statistics CSV report" @@ -180,7 +177,7 @@ local: - 'Estimated GPU Speedup' treeDirectory: enabled: true - depthLevel: 4 + depthLevel: 2 indentation: ' ' excludedPatterns: directories: @@ -193,6 +190,7 @@ local: - '^(\.+).*' - '^(\$+).*' - '^.+(_\$folder\$)$' + - '^rapids_4_spark_qualification_output.*' unsupportedOperators: mask: - operator: 'NOT_EQUAL' From d3b9a5996bd33a743553a31a837fa7f3ff7f368e Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Tue, 17 Sep 2024 08:50:53 -0500 Subject: [PATCH 03/24] Add support to ArrayJoin in Qualification tool (#1345) Signed-off-by: Ahmed Hussein Fixes #1341 - Add `ArrayJoin` to supportedExprs.csv and update operators sheets - Add a unit test to verify that arrayJoin appearing in project is marked as supported --- .../operatorsScore-databricks-aws-a10G.csv | 1 + .../operatorsScore-databricks-aws-t4.csv | 1 + .../operatorsScore-databricks-azure-t4.csv | 1 + .../operatorsScore-dataproc-gke-l4.csv | 1 + .../operatorsScore-dataproc-gke-t4.csv | 1 + .../resources/operatorsScore-dataproc-l4.csv | 1 + .../operatorsScore-dataproc-serverless-l4.csv | 1 + .../resources/operatorsScore-dataproc-t4.csv | 1 + .../main/resources/operatorsScore-emr-a10.csv | 1 + .../resources/operatorsScore-emr-a10G.csv | 1 + .../main/resources/operatorsScore-emr-t4.csv | 1 + .../resources/operatorsScore-onprem-a100.csv | 1 + core/src/main/resources/supportedExprs.csv | 4 +++ .../tool/planparser/SqlPlanParserSuite.scala | 28 +++++++++++++++++++ 14 files changed, 44 insertions(+) diff --git a/core/src/main/resources/operatorsScore-databricks-aws-a10G.csv b/core/src/main/resources/operatorsScore-databricks-aws-a10G.csv index f3fe438aa..e5096e1a9 100644 --- a/core/src/main/resources/operatorsScore-databricks-aws-a10G.csv +++ b/core/src/main/resources/operatorsScore-databricks-aws-a10G.csv @@ -304,3 +304,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-databricks-aws-t4.csv b/core/src/main/resources/operatorsScore-databricks-aws-t4.csv index f3fe438aa..e5096e1a9 100644 --- a/core/src/main/resources/operatorsScore-databricks-aws-t4.csv +++ b/core/src/main/resources/operatorsScore-databricks-aws-t4.csv @@ -304,3 +304,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-databricks-azure-t4.csv b/core/src/main/resources/operatorsScore-databricks-azure-t4.csv index ee06155e7..66c738016 100644 --- a/core/src/main/resources/operatorsScore-databricks-azure-t4.csv +++ b/core/src/main/resources/operatorsScore-databricks-azure-t4.csv @@ -292,3 +292,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-dataproc-gke-l4.csv b/core/src/main/resources/operatorsScore-dataproc-gke-l4.csv index 322c5cb1a..57fc5b44d 100644 --- a/core/src/main/resources/operatorsScore-dataproc-gke-l4.csv +++ b/core/src/main/resources/operatorsScore-dataproc-gke-l4.csv @@ -286,3 +286,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-dataproc-gke-t4.csv b/core/src/main/resources/operatorsScore-dataproc-gke-t4.csv index 35745ee29..3459e64cb 100644 --- a/core/src/main/resources/operatorsScore-dataproc-gke-t4.csv +++ b/core/src/main/resources/operatorsScore-dataproc-gke-t4.csv @@ -286,3 +286,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-dataproc-l4.csv b/core/src/main/resources/operatorsScore-dataproc-l4.csv index cce31ea98..422020970 100644 --- a/core/src/main/resources/operatorsScore-dataproc-l4.csv +++ b/core/src/main/resources/operatorsScore-dataproc-l4.csv @@ -292,3 +292,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-dataproc-serverless-l4.csv b/core/src/main/resources/operatorsScore-dataproc-serverless-l4.csv index bedf0b18c..61d9e3f1a 100644 --- a/core/src/main/resources/operatorsScore-dataproc-serverless-l4.csv +++ b/core/src/main/resources/operatorsScore-dataproc-serverless-l4.csv @@ -286,3 +286,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-dataproc-t4.csv b/core/src/main/resources/operatorsScore-dataproc-t4.csv index e26958b93..10ef53900 100644 --- a/core/src/main/resources/operatorsScore-dataproc-t4.csv +++ b/core/src/main/resources/operatorsScore-dataproc-t4.csv @@ -292,3 +292,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-emr-a10.csv b/core/src/main/resources/operatorsScore-emr-a10.csv index d5406bc2a..77befd12e 100644 --- a/core/src/main/resources/operatorsScore-emr-a10.csv +++ b/core/src/main/resources/operatorsScore-emr-a10.csv @@ -292,3 +292,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-emr-a10G.csv b/core/src/main/resources/operatorsScore-emr-a10G.csv index d5406bc2a..77befd12e 100644 --- a/core/src/main/resources/operatorsScore-emr-a10G.csv +++ b/core/src/main/resources/operatorsScore-emr-a10G.csv @@ -292,3 +292,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-emr-t4.csv b/core/src/main/resources/operatorsScore-emr-t4.csv index d7266e589..3f1296c38 100644 --- a/core/src/main/resources/operatorsScore-emr-t4.csv +++ b/core/src/main/resources/operatorsScore-emr-t4.csv @@ -292,3 +292,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/operatorsScore-onprem-a100.csv b/core/src/main/resources/operatorsScore-onprem-a100.csv index 71b14613f..4dc58f0c0 100644 --- a/core/src/main/resources/operatorsScore-onprem-a100.csv +++ b/core/src/main/resources/operatorsScore-onprem-a100.csv @@ -304,3 +304,4 @@ MapFromArrays,1.5 DecimalSum,1.5 MaxBy,1.5 MinBy,1.5 +ArrayJoin,1.5 diff --git a/core/src/main/resources/supportedExprs.csv b/core/src/main/resources/supportedExprs.csv index 129abfd40..bdc93f62c 100644 --- a/core/src/main/resources/supportedExprs.csv +++ b/core/src/main/resources/supportedExprs.csv @@ -42,6 +42,10 @@ ArrayFilter,S,`filter`,None,project,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,N ArrayIntersect,S,`array_intersect`,None,project,array1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA,NA,NA ArrayIntersect,S,`array_intersect`,None,project,array2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA,NA,NA ArrayIntersect,S,`array_intersect`,None,project,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA,NA,NA +ArrayJoin,S,`array_join`,None,project,array,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,S,NA,NA,NA,NA,NA +ArrayJoin,S,`array_join`,None,project,delimiter,NA,NA,NA,NA,NA,NA,NA,NA,NA,S,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +ArrayJoin,S,`array_join`,None,project,nullReplacement,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +ArrayJoin,S,`array_join`,None,project,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,S,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA ArrayMax,S,`array_max`,None,project,input,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA,NA,NA ArrayMax,S,`array_max`,None,project,result,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,NS,NA,NS,NS,NA,NA ArrayMin,S,`array_min`,None,project,input,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA,NA,NA diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala index 2f7829abc..99eb28e5f 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala @@ -1859,4 +1859,32 @@ class SQLPlanParserSuite extends BaseTestSuite { assertSizeAndSupported(2, hashAggExecs) } } + + test("array_join is supported") { + TrampolineUtil.withTempDir { outputLoc => + TrampolineUtil.withTempDir { eventLogDir => + val (eventLog, _) = ToolTestUtils.generateEventLog(eventLogDir, "arrayjoin") { spark => + import spark.implicits._ + val df = Seq( + (List("a", "b", "c"), List("b", "c")), + (List("a", "a"), List("b", "c")), + (List("aa"), List("b", "c")) + ).toDF("x", "y") + df.write.parquet(s"$outputLoc/test_arrayjoin") + val df2 = spark.read.parquet(s"$outputLoc/test_arrayjoin") + val df3 = df2.withColumn("arr_join", array_join(col("x"), ".")) + df3 + } + val app = createAppFromEventlog(eventLog) + assert(app.sqlPlans.size == 2) + val pluginTypeChecker = new PluginTypeChecker() + val parsedPlans = app.sqlPlans.map { case (sqlID, plan) => + SQLPlanParser.parseSQLPlan(app.appId, plan, sqlID, "", pluginTypeChecker, app) + } + val allExecInfo = getAllExecsFromPlan(parsedPlans.toSeq) + val projectExecs = allExecInfo.filter(_.exec == "Project") + assertSizeAndSupported(1, projectExecs) + } + } + } } From fd3d63e4b100b883b1eac4db133294a611b0a79a Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Wed, 18 Sep 2024 12:33:01 -0500 Subject: [PATCH 04/24] Rename tag core to core_tools (#1350) Signed-off-by: Ahmed Hussein --- .github/workflows/configuration.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/configuration.json b/.github/workflows/configuration.json index b93e7a670..4c327f8d8 100644 --- a/.github/workflows/configuration.json +++ b/.github/workflows/configuration.json @@ -6,7 +6,7 @@ }, { "title": "### Core", - "labels": ["tools"] + "labels": ["core_tools"] }, { "title": "### Miscellaneous", From 9dc284e39966cbc9c669e2ea68b342a9716b7c29 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Wed, 18 Sep 2024 12:56:54 -0500 Subject: [PATCH 05/24] Add PartitionFilters and DataFilters to the dataSourceInfo table (#1346) * Add PartitionFilters and DataFilters to the dataSourceInfo table Fixes #1192 **Changes** - Output: - Adds two new columns "data_filters" and "partition_filters" to datasourceInformation table `data_source_information.csv` - If the value is truncated, it shows ellipsis - Logic: - Fixes a bug in extracting the `PushedFilters`. The legacy code used to stop on the first comma which is incorrect. - Testing: - Added new test class `ReadParserSuite` to make it easier to test parsing of read expression directly without dependency on Spark, or other end-2-end components * Fix inconsistent values extracted from V1Read metadata --------- Signed-off-by: Ahmed Hussein --- .../tool/planparser/HiveParseHelper.scala | 5 +- .../rapids/tool/planparser/ReadParser.scala | 79 ++++++++--- .../profiling/ProfileClassWarehouse.scala | 15 +- .../rapids/tool/views/DataSourceView.scala | 16 ++- .../spark/sql/rapids/tool/AppBase.scala | 22 +-- .../tool/planparser/ReadParserSuite.scala | 133 ++++++++++++++++++ 6 files changed, 231 insertions(+), 39 deletions(-) create mode 100644 core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/ReadParserSuite.scala diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/HiveParseHelper.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/HiveParseHelper.scala index daaf9f1a4..8384e39e6 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/HiveParseHelper.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/HiveParseHelper.scala @@ -25,7 +25,7 @@ case class HiveScanSerdeClasses(className: String, format: String) extends Loggi def parseReadNode(node: SparkPlanGraphNode): ReadMetaData = { logDebug(s"Parsing node as ScanHiveTable: ${node.desc}") // Schema, pushedFilters empty for now as we cannot extract them yet from eventlogs - ReadMetaData("", "HiveTableRelation", "unknown", format) + ReadMetaData("", "HiveTableRelation", format) } def accepts(str: String): Boolean = { str.equals(className) @@ -75,7 +75,8 @@ object HiveParseHelper extends Logging { // If the SerDe class does not match the lookups, it returns an "unknown" format. def parseReadNode(node: SparkPlanGraphNode): ReadMetaData = { LOADED_SERDE_CLASSES.find(k => node.desc.contains(k.className)).map( - _.parseReadNode(node)).getOrElse(ReadMetaData("", "HiveTableRelation", "unknown", "unknown")) + _.parseReadNode(node)).getOrElse( + ReadMetaData("", "HiveTableRelation", ReadParser.UNKNOWN_METAFIELD)) } // Given a "scan hive" NodeGraph, construct the MetaData of the write operation based on the diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala index eb15bea7c..7fa69b023 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala @@ -23,7 +23,13 @@ import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker import org.apache.spark.internal.Logging import org.apache.spark.sql.execution.ui.SparkPlanGraphNode -case class ReadMetaData(schema: String, location: String, filters: String, format: String) +case class ReadMetaData(schema: String, location: String, format: String, + tags: Map[String, String] = ReadParser.DEFAULT_METAFIELD_MAP) { + // Define properties to access the tags + def pushedFilters: String = tags(ReadParser.METAFIELD_TAG_PUSHED_FILTERS) + def dataFilters: String = tags(ReadParser.METAFIELD_TAG_DATA_FILTERS) + def partitionFilters: String = tags(ReadParser.METAFIELD_TAG_PARTITION_FILTERS) +} object ReadParser extends Logging { // It was found that some eventlogs could have "NativeScan" instead of "Scan" @@ -37,6 +43,17 @@ object ReadParser extends Logging { "GpuBatchScan", "JDBCRelation") + val METAFIELD_TAG_DATA_FILTERS = "DataFilters" + val METAFIELD_TAG_PUSHED_FILTERS = "PushedFilters" + val METAFIELD_TAG_PARTITION_FILTERS = "PartitionFilters" + + val UNKNOWN_METAFIELD: String = "unknown" + val DEFAULT_METAFIELD_MAP: Map[String, String] = collection.immutable.Map( + METAFIELD_TAG_DATA_FILTERS -> UNKNOWN_METAFIELD, + METAFIELD_TAG_PUSHED_FILTERS -> UNKNOWN_METAFIELD, + METAFIELD_TAG_PARTITION_FILTERS -> UNKNOWN_METAFIELD + ) + def isScanNode(nodeName: String): Boolean = { SCAN_NODE_PREFIXES.exists(nodeName.startsWith(_)) } @@ -70,6 +87,46 @@ object ReadParser extends Logging { } } + // Used to extract metadata fields from Spark GraphNode’s description. + // It is made public for testing purposes. It returns DEFAULT_METAFIELD_MAP if no tags exist. + def extractReadTags(value: String): Map[String, String] = { + // initialize the results to the default values + var result = Map[String, String]() ++ DEFAULT_METAFIELD_MAP + // For filter tags in metadata they are in the form of: + // - complete form: "TagName: [foo(arg1, arg2, argn), bar(), field00, fieldn]". + // - truncated form ends by ellipsis that can start at any position: + // "TagName: [foo(arg1, arg2, argn), bar(), field00, fieldn...". + // To parse the filter tags: for each meta tag, create a regx that matches the value of the tag + // until the first closing bracket or the first ellipsis. + val metaFieldRegexMap = result.map { case (k, _) => + (k, s"($k): \\[(.*?)(\\.\\.\\.|\\])".r) + } + metaFieldRegexMap.foreach { case (k, v) => + v.findFirstMatchIn(value).foreach { m => + // if group(3) is an ellipsis then we should append it to the result. + val ellipse = if (m.group(3).equals("...")) "..." else "" + result += (k -> s"${m.group(2) + ellipse}") + } + } + result + } + + /** + * Retrieves the tag from SparkPlanInfo.metadata and removes the opening and closing brackets to + * ensure consistency with the V2 ReadParser. + * @param tag key (example: PushedFilters, DataFilters, and PartitionFilters) + * @param metadata SparkPlanInfo.metadata + * @return the value extracted from the metadata map or "unknown: if not found. + */ + def extractTagFromV1ReadMeta(tag: String, metadata: Map[String, String]): String = { + tag match { + case METAFIELD_TAG_DATA_FILTERS | METAFIELD_TAG_PUSHED_FILTERS | + METAFIELD_TAG_PARTITION_FILTERS => + metadata.getOrElse(tag, UNKNOWN_METAFIELD).stripPrefix("[").stripSuffix("]") + case _ => metadata.getOrElse(tag, UNKNOWN_METAFIELD) + } + } + def parseReadNode(node: SparkPlanGraphNode): ReadMetaData = { if (HiveParseHelper.isHiveTableScanNode(node)) { HiveParseHelper.parseReadNode(node) @@ -97,22 +154,10 @@ object ReadParser extends Logging { val JDBCPattern = raw".*JDBCRelation\((.*)\).*".r node.name match { case JDBCPattern(tableName) => tableName - case _ => "unknown" - } - } else { - "unknown" - } - val pushedFilterTag = "PushedFilters: " - val pushedFilters = if (node.desc.contains(pushedFilterTag)) { - val stringWithBrackets = getFieldWithoutTag(node.desc, pushedFilterTag) - // Remove prepended [ from the string if exists - if (stringWithBrackets.contains("[")) { - stringWithBrackets.split("\\[", 2).last.replace("]", "") - } else { - stringWithBrackets + case _ => UNKNOWN_METAFIELD } } else { - "unknown" + UNKNOWN_METAFIELD } val formatTag = "Format: " val fileFormat = if (node.desc.contains(formatTag)) { @@ -125,9 +170,9 @@ object ReadParser extends Logging { } else if (node.name.contains("JDBCRelation")) { "JDBC" } else { - "unknown" + UNKNOWN_METAFIELD } - ReadMetaData(schema, location, pushedFilters, fileFormat) + ReadMetaData(schema, location, fileFormat, tags = extractReadTags(node.desc)) } } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala index c4ed1ce78..f0fb25ac0 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala @@ -136,22 +136,25 @@ case class RapidsJarProfileResult(appIndex: Int, jar: String) extends ProfileRe case class DataSourceProfileResult(appIndex: Int, sqlID: Long, nodeId: Long, format: String, buffer_time: Long, scan_time: Long, data_size: Long, - decode_time: Long, location: String, pushedFilters: String, schema: String) + decode_time: Long, location: String, pushedFilters: String, schema: String, + dataFilters: String, partitionFilters: String) extends ProfileResult { override val outputHeaders = Seq("appIndex", "sqlID", "nodeId", "format", "buffer_time", "scan_time", "data_size", - "decode_time", "location", "pushedFilters", "schema") + "decode_time", "location", "pushedFilters", "schema", "data_filters", "partition_filters") override def convertToSeq: Seq[String] = { Seq(appIndex.toString, sqlID.toString, nodeId.toString, format, buffer_time.toString, scan_time.toString, data_size.toString, decode_time.toString, - location, pushedFilters, schema) + location, pushedFilters, schema, dataFilters, partitionFilters) } override def convertToCSVSeq: Seq[String] = { Seq(appIndex.toString, sqlID.toString, nodeId.toString, StringUtils.reformatCSVString(format), buffer_time.toString, scan_time.toString, data_size.toString, decode_time.toString, StringUtils.reformatCSVString(location), StringUtils.reformatCSVString(pushedFilters), - StringUtils.reformatCSVString(schema)) + StringUtils.reformatCSVString(schema), + StringUtils.reformatCSVString(dataFilters), + StringUtils.reformatCSVString(partitionFilters)) } } @@ -367,7 +370,9 @@ case class DataSourceCase( format: String, location: String, pushedFilters: String, - schema: String) + schema: String, + dataFilters: String, + partitionFilters: String) case class FailedTaskProfileResults(appIndex: Int, stageId: Int, stageAttemptId: Int, taskId: Long, taskAttemptId: Int, endReason: String) extends ProfileResult { diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala index 3d92e7027..1f07bbcd4 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala @@ -38,6 +38,8 @@ object IoMetrics { val SCAN_TIME_LABEL = "scan time" val DATA_SIZE_LABEL = "size of files read" val DECODE_TIME_LABEL = "GPU decode time" + + val EMPTY_IO_METRICS: IoMetrics = IoMetrics(0, 0, 0, 0) } trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { @@ -105,15 +107,15 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { app.dataSourceInfo.map { ds => val sqlIdtoDs = dataSourceMetrics.filter( sqlAccum => sqlAccum.sqlID == ds.sqlID && sqlAccum.nodeID == ds.nodeId) - if (sqlIdtoDs.nonEmpty) { - val ioMetrics = getIoMetrics(sqlIdtoDs) - DataSourceProfileResult(index, ds.sqlID, ds.nodeId, - ds.format, ioMetrics.bufferTime, ioMetrics.scanTime, ioMetrics.dataSize, - ioMetrics.decodeTime, ds.location, ds.pushedFilters, ds.schema) + val ioMetrics = if (sqlIdtoDs.nonEmpty) { + getIoMetrics(sqlIdtoDs) } else { - DataSourceProfileResult(index, ds.sqlID, ds.nodeId, - ds.format, 0, 0, 0, 0, ds.location, ds.pushedFilters, ds.schema) + IoMetrics.EMPTY_IO_METRICS } + DataSourceProfileResult(index, ds.sqlID, ds.nodeId, + ds.format, ioMetrics.bufferTime, ioMetrics.scanTime, ioMetrics.dataSize, + ioMetrics.decodeTime, ds.location, ds.pushedFilters, ds.schema, ds.dataFilters, + ds.partitionFilters) } } diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala index 978c2a132..0e681cff1 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala @@ -358,10 +358,12 @@ abstract class AppBase( results += DataSourceCase( sqlPlanInfoGraph.sqlID, scanNode.head.id, - meta.getOrElse("Format", "unknown"), - meta.getOrElse("Location", "unknown"), - meta.getOrElse("PushedFilters", "unknown"), - readSchema + ReadParser.extractTagFromV1ReadMeta("Format", meta), + ReadParser.extractTagFromV1ReadMeta("Location", meta), + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_PUSHED_FILTERS, meta), + readSchema, + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_DATA_FILTERS, meta), + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_PARTITION_FILTERS, meta) ) } } @@ -378,8 +380,10 @@ abstract class AppBase( hiveScanNode.id, scanHiveMeta.format, scanHiveMeta.location, - scanHiveMeta.filters, - scanHiveMeta.schema + scanHiveMeta.pushedFilters, + scanHiveMeta.schema, + scanHiveMeta.dataFilters, + scanHiveMeta.partitionFilters ) } } @@ -398,8 +402,10 @@ abstract class AppBase( node.id, res.format, res.location, - res.filters, - res.schema) + res.pushedFilters, + res.schema, + res.dataFilters, + res.partitionFilters) dataSourceInfo += dsCase Some(dsCase) } else { diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/ReadParserSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/ReadParserSuite.scala new file mode 100644 index 000000000..237d50d19 --- /dev/null +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/ReadParserSuite.scala @@ -0,0 +1,133 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.nvidia.spark.rapids.tool.planparser + +import com.nvidia.spark.rapids.BaseTestSuite +import org.scalatest.Matchers.{be, convertToAnyShouldWrapper} + +// Tests the implementation of the ReadParser independently of end-2-end tests +class ReadParserSuite extends BaseTestSuite { + // Wrapper to hold the related test cases + case class MetaFieldsTestCase(testDescription: String, + nodeDescr: String, expectedMetafields: Map[String, String]) { + } + + test("Read Metadata fields from a graphNode (pushedFilters, dataFilters, partitionFilters)") { + val nodeDescrPrologue = + """FileScan parquet db.table[field_00#952L,label#953,field_02#954,field_03#958L, + |field_04#960,field_05#961L,field_06#962L,field_07#963L,field_08#964L,field_09#965L, + |field_10#966L,field_11#967L,field_12#968L,field_13#969L,field_14#970L,field_15#971L, + |field_16#972L,field_17#973L,field_18#974L,field_19#975L,field_20#976L,field_21#977L, + |field_22#978L,field_22#979L,... 36 more fields]""".stripMargin.replaceAll("\n", "") + val allTestScenarios = Seq( + MetaFieldsTestCase( + "All the 3 MetaFields are present -- terminated by closing bracket", + nodeDescrPrologue + """ Batched: true, + | DataFilters: [isnotnull(flag_00#1013L), (flag_00#1013L = 1)], + | Format: Parquet, + | Location: InMemoryFileIndex(20 paths)[hdfs://directory/subdirectory/file_1...., + | PartitionFilters: [isnotnull(date_00#1014), (date_00#1014 = 20240621)], + | PushedFilters: [IsNotNull(flag_00), EqualTo(flag_00,1)], + | ReadSchema: struct + "IsNotNull(flag_00), EqualTo(flag_00,1)", + ReadParser.METAFIELD_TAG_DATA_FILTERS -> + "isnotnull(flag_00#1013L), (flag_00#1013L = 1)", + ReadParser.METAFIELD_TAG_PARTITION_FILTERS -> + "isnotnull(date_00#1014), (date_00#1014 = 20240621)")), + MetaFieldsTestCase( + "All the 3 MetaFields are present -- terminated by closing bracket -- Order is different", + nodeDescrPrologue + """ Batched: true, + | PushedFilters: [IsNotNull(flag_00), EqualTo(flag_00,1)], + | DataFilters: [isnotnull(flag_00#1013L), (flag_00#1013L = 1)], + | Format: Parquet, + | Location: InMemoryFileIndex(20 paths)[hdfs://directory/subdirectory/file_1...., + | PartitionFilters: [isnotnull(date_00#1014), (date_00#1014 = 20240621)], + | ReadSchema: struct + "IsNotNull(flag_00), EqualTo(flag_00,1)", + ReadParser.METAFIELD_TAG_DATA_FILTERS -> + "isnotnull(flag_00#1013L), (flag_00#1013L = 1)", + ReadParser.METAFIELD_TAG_PARTITION_FILTERS -> + "isnotnull(date_00#1014), (date_00#1014 = 20240621)")), + MetaFieldsTestCase( + "Only 1 MetaField is present -- terminated by closing bracket", + nodeDescrPrologue + """ Batched: true, + | PushedFilters: [IsNotNull(flag_00), EqualTo(flag_00,1)], + | Format: Parquet, + | Location: InMemoryFileIndex(20 paths)[hdfs://directory/subdirectory/file_1...., + | ReadSchema: struct + "IsNotNull(flag_00), EqualTo(flag_00,1)", + ReadParser.METAFIELD_TAG_DATA_FILTERS -> ReadParser.UNKNOWN_METAFIELD, + ReadParser.METAFIELD_TAG_PARTITION_FILTERS -> ReadParser.UNKNOWN_METAFIELD)), + MetaFieldsTestCase( + "Metafields might be truncated (not terminated by closing bracket)", + nodeDescrPrologue + """ Batched: true, + | PushedFilters: [IsNotNull(flag_00), EqualTo(flag_00,1),..., + | DataFilters: [isnotnull(flag_00#1013L), (flag_00#1013L = 1)..., + | Format: Parquet, + | Location: InMemoryFileIndex(20 paths)[hdfs://directory/subdirectory/file_1...., + | ReadSchema: struct + "IsNotNull(flag_00), EqualTo(flag_00,1),...", + ReadParser.METAFIELD_TAG_DATA_FILTERS -> + "isnotnull(flag_00#1013L), (flag_00#1013L = 1)...", + ReadParser.METAFIELD_TAG_PARTITION_FILTERS -> + "isnotnull(date_00#1014), (date_00#1014 = 20240621)...")), + MetaFieldsTestCase( + "Metafields might be empty", + nodeDescrPrologue + """ Batched: true, + | PushedFilters: [IsNotNull(flag_00), EqualTo(flag_00,1),..., + | DataFilters: [], + | Format: Parquet, + | Location: InMemoryFileIndex(20 paths)[hdfs://directory/subdirectory/file_1...., + | ReadSchema: struct + "IsNotNull(flag_00), EqualTo(flag_00,1),...", + ReadParser.METAFIELD_TAG_DATA_FILTERS -> "", + ReadParser.METAFIELD_TAG_PARTITION_FILTERS -> + "isnotnull(date_00#1014), (date_00#1014 = 20240621)...")) + ) + for (scenario <- allTestScenarios) { + try { + ReadParser.extractReadTags(scenario.nodeDescr) should be (scenario.expectedMetafields) + } catch { + case e: Exception => + fail(s"Failed for scenario: ${scenario.testDescription}", e) + } + } + } +} From 92911a4bdaa166126d79f49e7f54e795577076a9 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Fri, 20 Sep 2024 12:36:14 -0700 Subject: [PATCH 06/24] Disable `too-many-positional-arguments` in pylintrc (#1353) Signed-off-by: Partho Sarthi --- .pylintrc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.pylintrc b/.pylintrc index 51f33c2d6..feccc41f8 100644 --- a/.pylintrc +++ b/.pylintrc @@ -56,6 +56,8 @@ disable= # R0913: Too many arguments too-many-arguments, # R0914: Too many local variables + too-many-positional-arguments, + # R0917: Too many positional arguments too-many-locals, # R0801: Similar lines in 2 files duplicate-code, From 14a4213d54d3035b974e6598a8418c01090755c0 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Mon, 23 Sep 2024 15:25:40 -0500 Subject: [PATCH 07/24] Improve AQE support by capturing SQLPlan versions (#1354) * Improve AQE support by capturing SQLPlan versions * define an optimized plan that does not track entire planInfos * capture V2 datasources --------- Signed-off-by: Ahmed Hussein --- .../tool/analysis/AppSQLPlanAnalyzer.scala | 7 +- .../analysis/SparkSQLPlanInfoVisitor.scala | 5 +- .../rapids/tool/planparser/ReadParser.scala | 3 + .../tool/profiling/CollectInformation.scala | 2 +- .../rapids/tool/profiling/GenerateDot.scala | 13 +- .../profiling/ProfSQLPlanClassifier.scala | 4 +- .../profiling/ProfileClassWarehouse.scala | 37 ++--- .../rapids/tool/views/DataSourceView.scala | 16 +- .../spark/sql/rapids/tool/AppBase.scala | 34 ++-- .../sql/rapids/tool/EventProcessorBase.scala | 6 +- .../tool/profiling/ApplicationInfo.scala | 5 +- .../tool/profiling/EventsProcessor.scala | 17 -- .../rapids/tool/store/DataSourceRecord.scala | 101 ++++++++++++ .../sql/rapids/tool/store/SQLPlanModel.scala | 115 ++++++++++++++ .../tool/store/SQLPlanModelManager.scala | 129 +++++++++++++++ .../store/SQLPlanModelWithDSCaching.scala | 48 ++++++ .../rapids/tool/store/SQLPlanVersion.scala | 149 ++++++++++++++++++ 17 files changed, 608 insertions(+), 83 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/store/DataSourceRecord.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModel.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelManager.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelWithDSCaching.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanVersion.scala diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSQLPlanAnalyzer.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSQLPlanAnalyzer.scala index 629a82a38..e32061726 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSQLPlanAnalyzer.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSQLPlanAnalyzer.scala @@ -19,7 +19,7 @@ package com.nvidia.spark.rapids.tool.analysis import scala.collection.mutable.{AbstractSet, ArrayBuffer, HashMap, LinkedHashSet} import com.nvidia.spark.rapids.tool.planparser.SQLPlanParser -import com.nvidia.spark.rapids.tool.profiling.{AccumProfileResults, DataSourceCase, SQLAccumProfileResults, SQLMetricInfoCase, SQLStageInfoProfileResult, UnsupportedSQLPlan, WholeStageCodeGenResults} +import com.nvidia.spark.rapids.tool.profiling.{AccumProfileResults, SQLAccumProfileResults, SQLMetricInfoCase, SQLStageInfoProfileResult, UnsupportedSQLPlan, WholeStageCodeGenResults} import com.nvidia.spark.rapids.tool.qualification.QualSQLPlanAnalyzer import org.apache.spark.sql.execution.SparkPlanInfo @@ -27,6 +27,7 @@ import org.apache.spark.sql.execution.ui.{SparkPlanGraph, SparkPlanGraphCluster, import org.apache.spark.sql.rapids.tool.{AppBase, RDDCheckHelper, SqlPlanInfoGraphBuffer, SqlPlanInfoGraphEntry} import org.apache.spark.sql.rapids.tool.profiling.ApplicationInfo import org.apache.spark.sql.rapids.tool.qualification.QualificationAppInfo +import org.apache.spark.sql.rapids.tool.store.DataSourceRecord import org.apache.spark.sql.rapids.tool.util.ToolsPlanGraph /** @@ -99,7 +100,7 @@ class AppSQLPlanAnalyzer(app: AppBase, appIndex: Int) extends AppAnalysisBase(ap // as RDD or DS. protected case class SQLPlanVisitorContext( sqlPIGEntry: SqlPlanInfoGraphEntry, - sqlDataSources: ArrayBuffer[DataSourceCase] = ArrayBuffer[DataSourceCase](), + sqlDataSources: ArrayBuffer[DataSourceRecord] = ArrayBuffer[DataSourceRecord](), potentialProblems: LinkedHashSet[String] = LinkedHashSet[String](), var sqlIsDsOrRDD: Boolean = false) @@ -253,7 +254,7 @@ class AppSQLPlanAnalyzer(app: AppBase, appIndex: Int) extends AppAnalysisBase(ap val nodeIds = sqlPlanNodeIdToStageIds.filter { case (_, v) => v.contains(sModel.stageInfo.stageId) }.keys.toSeq - val nodeNames = app.sqlPlans.get(j.sqlID.get).map { planInfo => + val nodeNames = app.sqlManager.applyToPlanInfo(j.sqlID.get) { planInfo => val nodes = ToolsPlanGraph(planInfo).allNodes val validNodes = nodes.filter { n => nodeIds.contains((j.sqlID.get, n.id)) diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/SparkSQLPlanInfoVisitor.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/SparkSQLPlanInfoVisitor.scala index 3d3373e6d..5e744d5d6 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/SparkSQLPlanInfoVisitor.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/SparkSQLPlanInfoVisitor.scala @@ -16,14 +16,11 @@ package com.nvidia.spark.rapids.tool.analysis -import scala.collection.mutable.Map - import org.apache.spark.sql.execution.SparkPlanInfo import org.apache.spark.sql.execution.ui.SparkPlanGraphNode import org.apache.spark.sql.rapids.tool.SqlPlanInfoGraphEntry import org.apache.spark.sql.rapids.tool.util.ToolsPlanGraph - // Class defines the SQLPlan context by implementations that walk through the SQLPlanInfo class SQLPlanInfoContext(sqlPIGEntry: SqlPlanInfoGraphEntry) { def getSQLPIGEntry: SqlPlanInfoGraphEntry = sqlPIGEntry @@ -66,7 +63,7 @@ trait SparkSQLPlanInfoVisitor[R <: SQLPlanInfoContext] { } // Walks through all the SQLPlans in the given map - def walkPlans(plans: Map[Long, SparkPlanInfo]): Unit = { + def walkPlans(plans: collection.immutable.Map[Long, SparkPlanInfo]): Unit = { for ((sqlId, planInfo) <- plans) { val planCtxt = createPlanCtxt(sqlId, planInfo) walkPlan(planCtxt) diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala index 7fa69b023..131d6b2d3 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/ReadParser.scala @@ -46,6 +46,9 @@ object ReadParser extends Logging { val METAFIELD_TAG_DATA_FILTERS = "DataFilters" val METAFIELD_TAG_PUSHED_FILTERS = "PushedFilters" val METAFIELD_TAG_PARTITION_FILTERS = "PartitionFilters" + val METAFIELD_TAG_READ_SCHEMA = "ReadSchema" + val METAFIELD_TAG_FORMAT = "Format" + val METAFIELD_TAG_LOCATION = "Location" val UNKNOWN_METAFIELD: String = "unknown" val DEFAULT_METAFIELD_MAP: Map[String, String] = collection.immutable.Map( diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/CollectInformation.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/CollectInformation.scala index 68a99b550..b9c622334 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/CollectInformation.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/CollectInformation.scala @@ -129,7 +129,7 @@ object CollectInformation extends Logging { val planFileWriter = new ToolTextFileWriter(s"$outputDir/${app.appId}", "planDescriptions.log", "SQL Plan") try { - for ((sqlID, planDesc) <- app.physicalPlanDescription.toSeq.sortBy(_._1)) { + for ((sqlID, planDesc) <- app.sqlManager.getPhysicalPlans) { planFileWriter.write("\n=============================\n") planFileWriter.write(s"Plan for SQL ID : $sqlID") planFileWriter.write("\n=============================\n") diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/GenerateDot.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/GenerateDot.scala index 850290464..fa3b32bb9 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/GenerateDot.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/GenerateDot.scala @@ -110,17 +110,14 @@ object GenerateDot { list += row(1) -> row(2) } - val sqlPlansMap = app.sqlPlans.map { case (sqlId, sparkPlanInfo) => - sqlId -> ((sparkPlanInfo, app.physicalPlanDescription(sqlId))) - } - for ((sqlID, (planInfo, physicalPlan)) <- sqlPlansMap) { + for (sqlPlan <- app.sqlManager.sqlPlans.values) { val dotFileWriter = new ToolTextFileWriter(outputDirectory, - s"query-$sqlID.dot", "Dot file") + s"query-${sqlPlan.id}.dot", "Dot file") try { - val metrics = sqlIdToMaxMetric.getOrElse(sqlID, Seq.empty).toMap + val metrics = sqlIdToMaxMetric.getOrElse(sqlPlan.id, Seq.empty).toMap GenerateDot.writeDotGraph( - QueryPlanWithMetrics(SparkPlanInfoWithStage(planInfo, accumIdToStageId), metrics), - physicalPlan, stageIdToStageMetrics, dotFileWriter, sqlID, app.appId) + QueryPlanWithMetrics(SparkPlanInfoWithStage(sqlPlan.planInfo, accumIdToStageId), metrics), + sqlPlan.physicalPlanDesc, stageIdToStageMetrics, dotFileWriter, sqlPlan.id, app.appId) } finally { dotFileWriter.close() } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfSQLPlanClassifier.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfSQLPlanClassifier.scala index 3de49c242..8911c3dcc 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfSQLPlanClassifier.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfSQLPlanClassifier.scala @@ -53,8 +53,8 @@ class SQLPlanClassifier(app: ApplicationInfo) override def visitNode(sqlPlanCtxt: SQLPlanClassifierCtxt, node: ui.SparkPlanGraphNode): Unit = { // Check if the node is a delta metadata operation - val isDeltaLog = DeltaLakeHelper.isDeltaOpNode( - sqlPlanCtxt.sqlPIGEntry, app.physicalPlanDescription(sqlPlanCtxt.getSQLPIGEntry.sqlID), node) + val isDeltaLog = DeltaLakeHelper.isDeltaOpNode(sqlPlanCtxt.sqlPIGEntry, + app.sqlManager.getPhysicalPlanById(sqlPlanCtxt.getSQLPIGEntry.sqlID).get, node) if (isDeltaLog) { // if it is a Delta operation, add it to the list of Delta operations nodes sqlPlanCtxt.deltaOpsNode += node.id diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala index f0fb25ac0..b98d36aab 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileClassWarehouse.scala @@ -134,27 +134,28 @@ case class RapidsJarProfileResult(appIndex: Int, jar: String) extends ProfileRe } } -case class DataSourceProfileResult(appIndex: Int, sqlID: Long, nodeId: Long, +case class DataSourceProfileResult(appIndex: Int, sqlID: Long, version: Int, nodeId: Long, format: String, buffer_time: Long, scan_time: Long, data_size: Long, decode_time: Long, location: String, pushedFilters: String, schema: String, - dataFilters: String, partitionFilters: String) + dataFilters: String, partitionFilters: String, fromFinalPlan: Boolean) extends ProfileResult { override val outputHeaders = - Seq("appIndex", "sqlID", "nodeId", "format", "buffer_time", "scan_time", "data_size", - "decode_time", "location", "pushedFilters", "schema", "data_filters", "partition_filters") + Seq("appIndex", "sqlID", "sql_plan_version", "nodeId", "format", "buffer_time", "scan_time", + "data_size", "decode_time", "location", "pushedFilters", "schema", "data_filters", + "partition_filters", "from_final_plan") override def convertToSeq: Seq[String] = { - Seq(appIndex.toString, sqlID.toString, nodeId.toString, format, buffer_time.toString, - scan_time.toString, data_size.toString, decode_time.toString, - location, pushedFilters, schema, dataFilters, partitionFilters) + Seq(appIndex.toString, sqlID.toString, version.toString, nodeId.toString, format, + buffer_time.toString, scan_time.toString, data_size.toString, decode_time.toString, + location, pushedFilters, schema, dataFilters, partitionFilters, fromFinalPlan.toString) } override def convertToCSVSeq: Seq[String] = { - Seq(appIndex.toString, sqlID.toString, nodeId.toString, StringUtils.reformatCSVString(format), - buffer_time.toString, scan_time.toString, data_size.toString, decode_time.toString, - StringUtils.reformatCSVString(location), StringUtils.reformatCSVString(pushedFilters), - StringUtils.reformatCSVString(schema), - StringUtils.reformatCSVString(dataFilters), - StringUtils.reformatCSVString(partitionFilters)) + Seq(appIndex.toString, sqlID.toString, version.toString, nodeId.toString, + StringUtils.reformatCSVString(format), buffer_time.toString, scan_time.toString, + data_size.toString, decode_time.toString, StringUtils.reformatCSVString(location), + StringUtils.reformatCSVString(pushedFilters), StringUtils.reformatCSVString(schema), + StringUtils.reformatCSVString(dataFilters), StringUtils.reformatCSVString(partitionFilters), + fromFinalPlan.toString) } } @@ -364,16 +365,6 @@ case class DriverAccumCase( case class UnsupportedSQLPlan(sqlID: Long, nodeID: Long, nodeName: String, nodeDesc: String, reason: String) -case class DataSourceCase( - sqlID: Long, - nodeId: Long, - format: String, - location: String, - pushedFilters: String, - schema: String, - dataFilters: String, - partitionFilters: String) - case class FailedTaskProfileResults(appIndex: Int, stageId: Int, stageAttemptId: Int, taskId: Long, taskAttemptId: Int, endReason: String) extends ProfileResult { override val outputHeaders = Seq("appIndex", "stageId", "stageAttemptId", "taskId", diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala index 1f07bbcd4..157af2d0b 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala @@ -104,7 +104,7 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { || sqlAccum.name.contains(IoMetrics.DECODE_TIME_LABEL) || sqlAccum.name.equals(IoMetrics.DATA_SIZE_LABEL)) - app.dataSourceInfo.map { ds => + val dsFromLastPlan = app.dataSourceInfo.map { ds => val sqlIdtoDs = dataSourceMetrics.filter( sqlAccum => sqlAccum.sqlID == ds.sqlID && sqlAccum.nodeID == ds.nodeId) val ioMetrics = if (sqlIdtoDs.nonEmpty) { @@ -112,15 +112,23 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { } else { IoMetrics.EMPTY_IO_METRICS } - DataSourceProfileResult(index, ds.sqlID, ds.nodeId, + DataSourceProfileResult(index, ds.sqlID, ds.version, ds.nodeId, ds.format, ioMetrics.bufferTime, ioMetrics.scanTime, ioMetrics.dataSize, ioMetrics.decodeTime, ds.location, ds.pushedFilters, ds.schema, ds.dataFilters, - ds.partitionFilters) + ds.partitionFilters, ds.isFromFinalPlan) } + val dsFromOrigPlans = app.sqlManager.getDataSourcesFromOrigPlans.map { ds => + DataSourceProfileResult(index, ds.sqlID, ds.version, ds.nodeId, ds.format, + IoMetrics.EMPTY_IO_METRICS.bufferTime, IoMetrics.EMPTY_IO_METRICS.scanTime, + IoMetrics.EMPTY_IO_METRICS.dataSize, IoMetrics.EMPTY_IO_METRICS.decodeTime, + ds.location, ds.pushedFilters, ds.schema, ds.dataFilters, ds.partitionFilters, + ds.isFromFinalPlan) + } + dsFromLastPlan ++ dsFromOrigPlans } override def sortView(rows: Seq[DataSourceProfileResult]): Seq[DataSourceProfileResult] = { - rows.sortBy(cols => (cols.appIndex, cols.sqlID, cols.location, cols.schema)) + rows.sortBy(cols => (cols.appIndex, cols.sqlID, cols.version, cols.location, cols.schema)) } } diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala index 0e681cff1..787d6c4ec 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala @@ -20,13 +20,13 @@ import java.io.InputStream import java.util.zip.GZIPInputStream import scala.collection.immutable -import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, LinkedHashSet, Map, SortedMap} +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, LinkedHashSet, Map} import com.nvidia.spark.rapids.SparkRapidsBuildInfoEvent import com.nvidia.spark.rapids.tool.{DatabricksEventLog, DatabricksRollingEventLogFilesFileReader, EventLogInfo} import com.nvidia.spark.rapids.tool.planparser.{HiveParseHelper, ReadParser} import com.nvidia.spark.rapids.tool.planparser.HiveParseHelper.isHiveTableScanNode -import com.nvidia.spark.rapids.tool.profiling.{BlockManagerRemovedCase, DataSourceCase, DriverAccumCase, JobInfoClass, ResourceProfileInfoCase, SQLExecutionInfoClass, SQLPlanMetricsCase} +import com.nvidia.spark.rapids.tool.profiling.{BlockManagerRemovedCase, DriverAccumCase, JobInfoClass, ResourceProfileInfoCase, SQLExecutionInfoClass, SQLPlanMetricsCase} import com.nvidia.spark.rapids.tool.qualification.AppSubscriber import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} @@ -36,7 +36,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.scheduler.{SparkListenerEvent, StageInfo} import org.apache.spark.sql.execution.SparkPlanInfo import org.apache.spark.sql.execution.ui.SparkPlanGraphNode -import org.apache.spark.sql.rapids.tool.store.{AccumManager, StageModel, StageModelManager, TaskModelManager} +import org.apache.spark.sql.rapids.tool.store.{AccumManager, DataSourceRecord, SQLPlanModelManager, StageModel, StageModelManager, TaskModelManager} import org.apache.spark.sql.rapids.tool.util.{EventUtils, RapidsToolsConfUtil, ToolsPlanGraph, UTF8Source} import org.apache.spark.util.Utils @@ -63,12 +63,14 @@ abstract class AppBase( var blockManagersRemoved: ArrayBuffer[BlockManagerRemovedCase] = ArrayBuffer[BlockManagerRemovedCase]() // The data source information - val dataSourceInfo: ArrayBuffer[DataSourceCase] = ArrayBuffer[DataSourceCase]() + val dataSourceInfo: ArrayBuffer[DataSourceRecord] = ArrayBuffer[DataSourceRecord]() // jobId to job info val jobIdToInfo = new HashMap[Int, JobInfoClass]() val jobIdToSqlID: HashMap[Int, Long] = HashMap.empty[Int, Long] + lazy val sqlManager = new SQLPlanModelManager() + // SQL containing any Dataset operation or RDD to DataSet/DataFrame operation val sqlIDToDataSetOrRDDCase: HashSet[Long] = HashSet[Long]() // Map (sqlID <-> String(problematic issues)) @@ -78,9 +80,6 @@ abstract class AppBase( // sqlId to sql info val sqlIdToInfo = new HashMap[Long, SQLExecutionInfoClass]() val sqlIdToStages = new HashMap[Long, ArrayBuffer[Int]]() - // sqlPlans stores HashMap (sqlID <-> SparkPlanInfo) - // SortedMap is used to keep the order of the sqlPlans since AQEs can overrides the existing ones - var sqlPlans: Map[Long, SparkPlanInfo] = SortedMap[Long, SparkPlanInfo]() var sqlPlanMetricsAdaptive: ArrayBuffer[SQLPlanMetricsCase] = ArrayBuffer[SQLPlanMetricsCase]() // accum id to task stage accum info @@ -97,6 +96,8 @@ abstract class AppBase( var sparkRapidsBuildInfo: SparkRapidsBuildInfoEvent = SparkRapidsBuildInfoEvent(immutable.Map(), immutable.Map(), immutable.Map(), immutable.Map()) + def sqlPlans: immutable.Map[Long, SparkPlanInfo] = sqlManager.getPlanInfos + // Returns the String value of the eventlog or empty if it is not defined. Note that the eventlog // won't be defined for running applications def getEventLogPath: String = { @@ -225,7 +226,7 @@ abstract class AppBase( sqlIDToDataSetOrRDDCase.remove(sqlID) sqlIDtoProblematic.remove(sqlID) sqlIdToInfo.remove(sqlID) - sqlPlans.remove(sqlID) + sqlManager.remove(sqlID) val dsToRemove = dataSourceInfo.filter(_.sqlID == sqlID) dsToRemove.foreach(dataSourceInfo -= _) @@ -336,11 +337,11 @@ abstract class AppBase( // The ReadSchema metadata is only in the eventlog for DataSource V1 readers def checkMetadataForReadSchema( - sqlPlanInfoGraph: SqlPlanInfoGraphEntry): ArrayBuffer[DataSourceCase] = { + sqlPlanInfoGraph: SqlPlanInfoGraphEntry): ArrayBuffer[DataSourceRecord] = { // check if planInfo has ReadSchema val allMetaWithSchema = AppBase.getPlanMetaWithSchema(sqlPlanInfoGraph.planInfo) val allNodes = sqlPlanInfoGraph.sparkPlanGraph.allNodes - val results = ArrayBuffer[DataSourceCase]() + val results = ArrayBuffer[DataSourceRecord]() allMetaWithSchema.foreach { plan => val meta = plan.metadata @@ -355,8 +356,9 @@ abstract class AppBase( // add it to the dataSourceInfo // Processing Photon eventlogs issue: https://github.com/NVIDIA/spark-rapids-tools/issues/251 if (scanNode.nonEmpty) { - results += DataSourceCase( + results += DataSourceRecord( sqlPlanInfoGraph.sqlID, + sqlManager.getPlanById(sqlPlanInfoGraph.sqlID).get.plan.version, scanNode.head.id, ReadParser.extractTagFromV1ReadMeta("Format", meta), ReadParser.extractTagFromV1ReadMeta("Location", meta), @@ -375,8 +377,9 @@ abstract class AppBase( val sqlGraph = ToolsPlanGraph(hiveReadPlan) val hiveScanNode = sqlGraph.allNodes.head val scanHiveMeta = HiveParseHelper.parseReadNode(hiveScanNode) - results += DataSourceCase( + results += DataSourceRecord( sqlPlanInfoGraph.sqlID, + sqlManager.getPlanById(sqlPlanInfoGraph.sqlID).get.plan.version, hiveScanNode.id, scanHiveMeta.format, scanHiveMeta.location, @@ -394,11 +397,12 @@ abstract class AppBase( // This will find scans for DataSource V2, if the schema is very large it // will likely be incomplete and have ... at the end. def checkGraphNodeForReads( - sqlID: Long, node: SparkPlanGraphNode): Option[DataSourceCase] = { + sqlID: Long, node: SparkPlanGraphNode): Option[DataSourceRecord] = { if (ReadParser.isDataSourceV2Node(node)) { val res = ReadParser.parseReadNode(node) - val dsCase = DataSourceCase( + val dsCase = DataSourceRecord( sqlID, + sqlManager.getPlanById(sqlID).get.plan.version, node.id, res.format, res.location, @@ -547,7 +551,7 @@ object AppBase { (complexTypes.filter(_.nonEmpty), nestedComplexTypes.filter(_.nonEmpty)) } - private def trimSchema(str: String): String = { + def trimSchema(str: String): String = { val index = str.lastIndexOf(",") if (index != -1 && str.contains("...")) { str.substring(0, index) diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala index e84a301a3..4620a640d 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala @@ -152,7 +152,8 @@ abstract class EventProcessorBase[T <: AppBase](app: T) extends SparkListener wi hasDatasetOrRDD = false ) app.sqlIdToInfo.put(event.executionId, sqlExecution) - app.sqlPlans += (event.executionId -> event.sparkPlanInfo) + app.sqlManager.addNewExecution(event.executionId, event.sparkPlanInfo, + event.physicalPlanDescription) } def doSparkListenerSQLExecutionEnd( @@ -183,7 +184,8 @@ abstract class EventProcessorBase[T <: AppBase](app: T) extends SparkListener wi app: T, event: SparkListenerSQLAdaptiveExecutionUpdate): Unit = { // AQE plan can override the ones got from SparkListenerSQLExecutionStart - app.sqlPlans += (event.executionId -> event.sparkPlanInfo) + app.sqlManager.addAQE(event.executionId, event.sparkPlanInfo, + event.physicalPlanDescription) } def doSparkListenerSQLAdaptiveSQLMetricUpdates( diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/ApplicationInfo.scala index c56bbe33b..83a3cbc0b 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/ApplicationInfo.scala @@ -16,7 +16,7 @@ package org.apache.spark.sql.rapids.tool.profiling -import scala.collection.{mutable, Map} +import scala.collection.Map import com.nvidia.spark.rapids.tool.EventLogInfo import com.nvidia.spark.rapids.tool.analysis.AppSQLPlanAnalyzer @@ -187,9 +187,6 @@ class ApplicationInfo( val index: Int) extends AppBase(Some(eLogInfo), Some(hadoopConf)) with Logging { - // physicalPlanDescription stores HashMap (sqlID <-> physicalPlanDescription) - var physicalPlanDescription: mutable.HashMap[Long, String] = mutable.HashMap.empty[Long, String] - private lazy val eventProcessor = new EventsProcessor(this) // Process all events diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/EventsProcessor.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/EventsProcessor.scala index edebb3548..f77b44fb5 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/EventsProcessor.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/profiling/EventsProcessor.scala @@ -22,7 +22,6 @@ import com.nvidia.spark.rapids.tool.profiling._ import org.apache.spark.internal.Logging import org.apache.spark.scheduler._ -import org.apache.spark.sql.execution.ui.{SparkListenerSQLAdaptiveExecutionUpdate, SparkListenerSQLExecutionStart} import org.apache.spark.sql.rapids.tool.EventProcessorBase import org.apache.spark.sql.rapids.tool.util.StringUtils @@ -88,28 +87,12 @@ class EventsProcessor(app: ApplicationInfo) extends EventProcessorBase[Applicati logDebug(s"App's GPU Mode = ${app.gpuMode}") } - override def doSparkListenerSQLExecutionStart( - app: ApplicationInfo, - event: SparkListenerSQLExecutionStart): Unit = { - super.doSparkListenerSQLExecutionStart(app, event) - app.physicalPlanDescription += (event.executionId -> event.physicalPlanDescription) - } - override def doSparkListenerTaskGettingResult( app: ApplicationInfo, event: SparkListenerTaskGettingResult): Unit = { logDebug("Processing event: " + event.getClass) } - override def doSparkListenerSQLAdaptiveExecutionUpdate( - app: ApplicationInfo, - event: SparkListenerSQLAdaptiveExecutionUpdate): Unit = { - logDebug("Processing event: " + event.getClass) - // AQE plan can override the ones got from SparkListenerSQLExecutionStart - app.physicalPlanDescription += (event.executionId -> event.physicalPlanDescription) - super.doSparkListenerSQLAdaptiveExecutionUpdate(app, event) - } - // To process all other unknown events override def doOtherEvent(app: ApplicationInfo, event: SparkListenerEvent): Unit = { logDebug("Skipping unhandled event: " + event.getClass) diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/DataSourceRecord.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/DataSourceRecord.scala new file mode 100644 index 000000000..7804d2072 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/DataSourceRecord.scala @@ -0,0 +1,101 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.store + +abstract class DataSourceRecord { + def sqlID: Long + def version: Int + def nodeId: Long + def format: String + def location: String + def pushedFilters: String + def schema: String + def dataFilters: String + def partitionFilters: String + def isFromFinalPlan: Boolean + def comments: String +} + +case class NonFinalDataSourceCase( + sqlID: Long, + version: Int, + nodeId: Long, + format: String, + location: String, + pushedFilters: String, + schema: String, + dataFilters: String, + partitionFilters: String) extends DataSourceRecord { + + override def isFromFinalPlan: Boolean = false + override def comments: String = DataSourceRecord.COMMENT_NON_FINAL_PLAN +} + +case class FinalDataSourceCase( + sqlID: Long, + version: Int, + nodeId: Long, + format: String, + location: String, + pushedFilters: String, + schema: String, + dataFilters: String, + partitionFilters: String) extends DataSourceRecord { + + override def isFromFinalPlan: Boolean = true + override def comments: String = DataSourceRecord.COMMENT_FINAL_PLAN +} + +object DataSourceRecord { + val COMMENT_NON_FINAL_PLAN="isFinalPlan=false" + val COMMENT_FINAL_PLAN="isFinalPlan=true" + + def apply( + sqlID: Long, + version: Int, + nodeId: Long, + format: String, + location: String, + pushedFilters: String, + schema: String, + dataFilters: String, + partitionFilters: String, + fromFinalPlan: Boolean = true): DataSourceRecord = { + fromFinalPlan match { + case true => FinalDataSourceCase( + sqlID, + version, + nodeId, + format, + location, + pushedFilters, + schema, + dataFilters, + partitionFilters) + case _ => NonFinalDataSourceCase( + sqlID, + version, + nodeId, + format, + location, + pushedFilters, + schema, + dataFilters, + partitionFilters) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModel.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModel.scala new file mode 100644 index 000000000..d6c67ec7b --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModel.scala @@ -0,0 +1,115 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.store + +import scala.collection.mutable.ArrayBuffer + +import org.apache.spark.sql.execution.SparkPlanInfo + +/** + * SQLPlanModel is a class to store the information of a SQL Plan including all its versions. + * A new instance of this class is created while handling SparkListenerSQLExecutionStart event. + * Note that keeping track of all SQLPlanInfo versions could result in a large memory overhead. + * See SQLPlanModelWithDSCaching for a more memory efficient version that only captures the + * DataSourceRecords of a planInfo. + * Therefore, it is recommended to: + * 1- Use this class iff you need to keep track of all information inside all versions of a SQLPlan. + * 2- Do some memory optimizations to reduce the overhead of storing all versions. + * @param id SqlId + */ +class SQLPlanModel(val id: Long) { + // TODO: common information related to the SQLPlan should be added as fields in this class. + // For example, the information tracked in + // AppBase.sqlIdToInfo Map[sqlId, SQLExecutionInfoClass] should belong here. + // List of all the versions of a SQL Plan. For each AQE event, the new version is added to this + // list. + private val planVersions: ArrayBuffer[SQLPlanVersion] = new ArrayBuffer[SQLPlanVersion]() + // A shortcut to the most recent version added to the list. + // This is not defined as option() because it should not happen that a SQLPlanModel is defined + // without adding a plan. This is to cache the value of planVersions.last + var plan: SQLPlanVersion = _ + // Number of versions defined for this SQL Plan (i.e., 1 + [count of AQE update events]) + var versionsCount: Int = 0 + + /** + * A shortcut to the planInfo to abstract the internal details of the SQLPlanVersion. + * @return SparkPlanInfo of the latest version of the plan. + */ + def planInfo = plan.planInfo + + /** + * A shortcut to the physicalPlanDescription to abstract the internal details of the + * SQLPlanVersion. + * @return physicalPlanDesc of the latest version of the plan. + */ + def physicalPlanDesc = plan.physicalPlanDescription + + /** + * Public method to add a new version of the plan. + * @param planInfo SparkPlanInfo for the new version of the plan. + * @param physicalPlanDescription String representation of the physical plan for the new version. + */ + def addPlan(planInfo: SparkPlanInfo, physicalPlanDescription: String): Unit = { + // By default, a new planVersion is defined as final. + val planVersion = new SQLPlanVersion(id, versionsCount, planInfo, physicalPlanDescription) + // Update references and shortcuts to the latest plan and cache previous one if any + updateVersions(planVersion) + } + + protected def updatePlanField(newPlan: SQLPlanVersion): Unit = { + planVersions += newPlan + plan = newPlan + } + + // After adding a new version, reset the previous plan if necessary + protected def resetPreviousPlan(): Unit = { + plan.resetFinalFlag() + } + + /** + * Update the planVersions list and the plan shortcut to the latest version. It caches the + * previous information if any. + * @param newPlan the latest version of the plan. + */ + private def updateVersions(newPlan: SQLPlanVersion): Unit = { + // increment the versionsCount + versionsCount += 1 + if (versionsCount > 1) { + // reset the flag of the previous Version + resetPreviousPlan() + } + // Update the shortcuts to the latest version + updatePlanField(newPlan) + } + + /** + * Get the DataSources from the most recent version of the plan. + * @return Iterable of DataSourceRecord + */ + def getDataSources: Iterable[DataSourceRecord] = { + plan.getAllReadDS + } + + /** + * Get all the DataSources from the original plans (excludes the most recent version). + * @return Iterable of DataSourceRecord + */ + def getDataSourcesFromOrigAQEPlans: Iterable[DataSourceRecord] = { + // TODO: Consider iterating on the node to add DSV2 as well. + planVersions.dropRight(1).flatMap(_.getAllReadDS) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelManager.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelManager.scala new file mode 100644 index 000000000..03b77c1cc --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelManager.scala @@ -0,0 +1,129 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.store + +import scala.collection.immutable +import scala.collection.mutable + +import org.apache.spark.sql.execution.SparkPlanInfo + +/** + * Container class to store the information about SqlPlans. + */ +class SQLPlanModelManager { + // SortedMap is used to keep the order of the sqlPlans by Id + val sqlPlans: mutable.SortedMap[Long, SQLPlanModel] = mutable.SortedMap[Long, SQLPlanModel]() + + /** + * Get the SparkPlanInfo for the given executionId. This is the last plan version added to that + * SQLPlan. + * @param id executionId of the SqlPlan + * @return SparkPlanInfo if exists + */ + def getPlanInfoById(id: Long): Option[SparkPlanInfo] = { + // returns the last plan added to that executionId if exists + getPlanById(id).map(_.planInfo) + } + + /** + * Get the SQLPlanModel for the given executionId. + * @param id executionId of the SqlPlan + * @return SQLPlanModel if exists + */ + def getPlanById(id: Long): Option[SQLPlanModel] = { + // returns the last plan added to that executionId if exists + sqlPlans.get(id) + } + + /** + * Get the physical plan description for the given executionId. This is the last plan version. + * @param id executionId of the SqlPlan + * @return physical plan description if exists + */ + def getPhysicalPlanById(id: Long): Option[String] = { + getPlanById(id).map(_.physicalPlanDesc) + } + + /** + * Add a new execution to the SQLPlanModelManager. This is called when a + * SparkListenerSQLExecutionStart is triggered. + * @param id executionId of the SqlPlan + * @param planInfo SparkPlanInfo for the new version of the plan. + * @param physicalDescription String representation of the physical plan for the new version. + */ + def addNewExecution(id: Long, planInfo: SparkPlanInfo, physicalDescription: String): Unit = { + //TODO: in future we should pass more arguments to this method to capture the common information + // of an SqlPlan (i.e., startTime,..etc)) + val planModel = sqlPlans.getOrElseUpdate(id, new SQLPlanModelWithDSCaching(id)) + planModel.addPlan(planInfo, physicalDescription) + } + + /** + * Handles the AQE event by adding a new version of the plan to the SQLPlanModelManager and update + * the references to the last version. + * @param id executionId of the SqlPlan + * @param planInfo SparkPlanInfo for the new version of the plan. + * @param physicalDescription String representation of the physical plan for the new version. + */ + def addAQE(id: Long, planInfo: SparkPlanInfo, physicalDescription: String): Unit = { + // TODO: we can verify that the id already exists, but we ignore that verification now as it is + // undefined how we should handle an AQE without the original plan. + addNewExecution(id, planInfo, physicalDescription) + } + + /** + * Apply a function to the SparkPlanInfo of the given executionId if it exists. + * @param id executionId of the SqlPlan + * @param f function to be applied + * @tparam A Type of the result of the function + * @return Some(result) if the plan exists, None otherwise + */ + def applyToPlanInfo[A](id: Long)(f: SparkPlanInfo => A): Option[A] = { + getPlanInfoById(id).map(f) + } + + /** + * Shortcuts to make the new implementation compatible with previous code that was expecting a + * Map[Long, String] + * @return map between executionId and the physical description of the last version. + */ + def getPhysicalPlans: immutable.Map[Long, String] = { + immutable.SortedMap[Long, String]() ++ sqlPlans.mapValues(_.physicalPlanDesc) + } + + def remove(id: Long): Option[SQLPlanModel] = { + sqlPlans.remove(id) + } + + /** + * When AQE is enabled, this methods is used to get the DataSources that are read by old versions + * of the SQLPlan if any + * @return Iterable of DataSourceRecord representing previous versions of the SQLPlan + */ + def getDataSourcesFromOrigPlans: Iterable[DataSourceRecord] = { + sqlPlans.values.flatMap(_.getDataSourcesFromOrigAQEPlans) + } + + /** + * Shortcut to make the new implementation compatible with previous code that was expecting a + * Map[Long, SparkPlanInfo] + * @return map between executionId and the SparkPlanVersion of the last version. + */ + def getPlanInfos: immutable.Map[Long, SparkPlanInfo] = { + immutable.SortedMap[Long, SparkPlanInfo]() ++ sqlPlans.mapValues(_.planInfo) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelWithDSCaching.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelWithDSCaching.scala new file mode 100644 index 000000000..7b21f8a19 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanModelWithDSCaching.scala @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.store + +import scala.collection.mutable.ArrayBuffer + +/** + * An extension of SQLPlanModel to only cache the DataSourceRecords from previous plan. + * @param sqlId the executionID of the sqlPlan. + */ +class SQLPlanModelWithDSCaching(sqlId: Long) extends SQLPlanModel(sqlId) { + // List that captures the DSV1Reads of previous PlanInfo versions excluding the most recent one. + // This contains only the DataSourceRecords of previous plan excluding the + private val cachedDataSources: ArrayBuffer[DataSourceRecord] = new ArrayBuffer[DataSourceRecord]() + + override def updatePlanField(newPlan: SQLPlanVersion): Unit = { + plan = newPlan + } + + override def resetPreviousPlan(): Unit = { + // reset the final flag to initialize the dataSources correctly + plan.resetFinalFlag() + // cache the datasource records from previous plan if any + cachedDataSources ++= plan.getAllReadDS + } + + /** + * Get all the DataSources from the original plans (excludes the most recent version). + * @return Iterable of DataSourceRecord + */ + override def getDataSourcesFromOrigAQEPlans: Iterable[DataSourceRecord] = { + cachedDataSources + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanVersion.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanVersion.scala new file mode 100644 index 000000000..a79fafda9 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/SQLPlanVersion.scala @@ -0,0 +1,149 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.store + +import com.nvidia.spark.rapids.tool.planparser.ReadParser + +import org.apache.spark.sql.execution.SparkPlanInfo +import org.apache.spark.sql.execution.ui.SparkPlanGraph +import org.apache.spark.sql.rapids.tool.AppBase +import org.apache.spark.sql.rapids.tool.util.ToolsPlanGraph + + +/** + * Represents a version of the SQLPlan holding the specific information related to that SQL plan. + * In an AQE event, Spark sends the new update with the updated SparkPlanInfo and + * PhysicalPlanDescription. + * @param sqlId sql plan ID. This is redundant because the SQLPlan Model has the same information. + * However, storing it here makes the code easier as we don't have to build + * (sqlId, SQLPLanVersion) tuples. + * @param version the version number of this plan. It starts at 0 and increments by 1 for each + * SparkListenerSQLAdaptiveExecutionUpdate. + * @param planInfo The instance of SparkPlanInfo for this version of the plan. + * @param physicalPlanDescription The string representation of the physical plan for this version. + * @param isFinal Flag to indicate if this plan is the final plan for the SQLPlan + */ +class SQLPlanVersion( + val sqlId: Long, + val version: Int, + val planInfo: SparkPlanInfo, + val physicalPlanDescription: String, + var isFinal: Boolean = true) { + + def resetFinalFlag(): Unit = { + // This flag depends on the AQE events sequence. + // It does not set that field using the substring of the physicalPlanDescription + // (isFinalPlan=true). + // The consequences of this is that for incomplete eventlogs, the last PlanInfo to be precessed + // is considered final. + isFinal = false + } + + /** + * Starting with the SparkPlanInfo for this version, recursively get all the SparkPlanInfo within + * the children that define a ReadSchema. + * @return Sequence of SparkPlanInfo that have a ReadSchema attached to it. + */ + def getPlansWithSchema: Seq[SparkPlanInfo] = { + SQLPlanVersion.getPlansWithSchemaRecursive(planInfo) + } + + /** + * This is used to extract the metadata of ReadV1 nodes in Spark Plan Info + * @param planGraph planGraph Optional SparkPlanGraph to use. If not provided, it will be created. + * @return all the read datasources V1 recursively that are read by this plan including. + */ + def getReadDSV1(planGraph: Option[SparkPlanGraph] = None): Iterable[DataSourceRecord] = { + val graph = planGraph.getOrElse(ToolsPlanGraph(planInfo)) + getPlansWithSchema.flatMap { plan => + val meta = plan.metadata + // TODO: Improve the extraction of ReaSchema using RegEx (ReadSchema):\s(.*?)(\.\.\.|,\s|$) + val readSchema = + ReadParser.formatSchemaStr(meta.getOrElse(ReadParser.METAFIELD_TAG_READ_SCHEMA, "")) + val scanNodes = graph.allNodes.filter(ReadParser.isScanNode).filter(node => { + // Get ReadSchema of each Node and sanitize it for comparison + val trimmedNode = AppBase.trimSchema(ReadParser.parseReadNode(node).schema) + readSchema.contains(trimmedNode) + }) + if (scanNodes.nonEmpty) { + Some(DataSourceRecord( + sqlId, + version, + scanNodes.head.id, + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_FORMAT, meta), + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_LOCATION, meta), + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_PUSHED_FILTERS, meta), + readSchema, + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_DATA_FILTERS, meta), + ReadParser.extractTagFromV1ReadMeta(ReadParser.METAFIELD_TAG_PARTITION_FILTERS, meta), + fromFinalPlan=isFinal)) + } else { + None + } + } + } + + /** + * Get all the DataSources that are read by this plan (V2). + * @param planGraph Optional SparkPlanGraph to use. If not provided, it will be created. + * @return List of DataSourceRecord for all the V2 DataSources read by this plan. + */ + def getReadDSV2(planGraph: Option[SparkPlanGraph] = None): Iterable[DataSourceRecord] = { + val graph = planGraph.getOrElse(ToolsPlanGraph(planInfo)) + graph.allNodes.filter(ReadParser.isDataSourceV2Node).map { node => + val res = ReadParser.parseReadNode(node) + DataSourceRecord( + sqlId, + version, + node.id, + res.format, + res.location, + res.pushedFilters, + res.schema, + res.dataFilters, + res.partitionFilters, + fromFinalPlan=isFinal) + } + } + + /** + * Get all the DataSources that are read by this plan (V1 and V1). + * @return Iterable of DataSourceRecord + */ + def getAllReadDS: Iterable[DataSourceRecord] = { + val planGraph = Option(ToolsPlanGraph(planInfo)) + getReadDSV1(planGraph) ++ getReadDSV2(planGraph) + } +} + +object SQLPlanVersion { + /** + * Recursive call to get all the SparkPlanInfo that have a schema attached to it. + * This is mainly used for V1 ReadSchema + * @param planInfo The SparkPlanInfo to start the search from + * @return A list of SparkPlanInfo that have a schema attached to it. + */ + private def getPlansWithSchemaRecursive(planInfo: SparkPlanInfo): Seq[SparkPlanInfo] = { + val childRes = planInfo.children.flatMap(getPlansWithSchemaRecursive) + if (planInfo.metadata != null && + planInfo.metadata.contains(ReadParser.METAFIELD_TAG_READ_SCHEMA)) { + childRes :+ planInfo + } else { + childRes + } + } +} From 80df2ea5f3db67b4722c357d9a3cc0e3185a4b26 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Tue, 24 Sep 2024 15:00:21 -0500 Subject: [PATCH 08/24] Set Spark version to 3.4.2 by default for onprem environment (#1358) Signed-off-by: Ahmed Hussein (amahussein) Followup on #1326 to set the default spark version to 3.4.2 for onPrem to avoid the bug described in #1316 without need to do something on customer side. --- .../resources/onprem-configs.json | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json b/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json index b731c1629..35eecae50 100644 --- a/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json @@ -2,7 +2,7 @@ "dependencies": { "deployMode": { "LOCAL": { - "//activeBuildVer": "Define this key in order to set the default buildVer for that platform", + "activeBuildVer": "342", "350": [ { "name": "Apache Spark", @@ -13,6 +13,16 @@ "size": 400395283 } ], + "342": [ + { + "name": "Apache Spark", + "uri": "https://archive.apache.org/dist/spark/spark-3.4.2/spark-3.4.2-bin-hadoop3.tgz", + "type": "archive", + "relativePath": "jars/*", + "sha512": "c9470a557c96fe899dd1c9ea8d0dda3310eaf0155b2bb972f70a6d97fee8cdaf838b425c30df3d5856b2c31fc2be933537c111db72d0427eabb76c6abd92c1f1", + "size": 388664780 + } + ], "333": [ { "name": "Apache Spark", From e620d17ecf05dfccfaddc36ba1e98f39c772b8c0 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Tue, 24 Sep 2024 13:59:21 -0700 Subject: [PATCH 09/24] Fix python tool picking up wrong JAR version in Fat wheel mode (#1357) * Fix python tool picking up wrong JAR version Signed-off-by: Partho Sarthi * Add conf in profiling and rename function Signed-off-by: Partho Sarthi * Make function private Signed-off-by: Partho Sarthi --------- Signed-off-by: Partho Sarthi --- user_tools/build.sh | 4 +- .../spark_rapids_pytools/rapids/tool_ctxt.py | 54 +++++++++++++++---- .../resources/profiling-conf.yaml | 1 + .../resources/qualification-conf.yaml | 1 + 4 files changed, 47 insertions(+), 13 deletions(-) diff --git a/user_tools/build.sh b/user_tools/build.sh index 824505f4d..fd339b276 100755 --- a/user_tools/build.sh +++ b/user_tools/build.sh @@ -1,5 +1,5 @@ #!/bin/bash -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -105,7 +105,7 @@ build "$build_mode" # Check build status if [ $? -eq 0 ]; then - echo "Build successful. To install, use: pip install " + echo "Build successful. To install, use: pip install dist/" else echo "Build failed." exit 1 diff --git a/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py b/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py index e7ba7ee80..320aa69c9 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py +++ b/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py @@ -15,9 +15,9 @@ """Implementation of class holding the execution context of a rapids tool""" import os +import re import tarfile from dataclasses import dataclass, field -from glob import glob from logging import Logger from typing import Type, Any, ClassVar, List @@ -86,8 +86,8 @@ def _init_fields(self): def get_deploy_mode(self) -> Any: return self.platform_opts.get('deployMode') - def is_fatwheel_mode(self) -> bool: - return self.get_ctxt('fatwheelModeEnabled') + def is_fat_wheel_mode(self) -> bool: + return self.get_ctxt('fatWheelModeEnabled') def set_ctxt(self, key: str, val: Any): self.props['wrapperCtx'][key] = val @@ -135,6 +135,21 @@ def set_local_workdir(self, parent: str): self.logger.info('Dependencies are generated locally in local disk as: %s', dep_folder) self.logger.info('Local output folder is set as: %s', exec_root_dir) + def _identify_fat_wheel_jar(self, resource_files: List[str]) -> None: + """ + Identifies the tools JAR file from resource files in fat wheel mode and sets its name in the context. + :param resource_files: List of resource files to search for the tools JAR file. + :raises AssertionError: If the number of matching files is not exactly one. + """ + tools_jar_regex_str = self.get_value('sparkRapids', 'toolsJarRegex') + tools_jar_regex = re.compile(tools_jar_regex_str) + matched_files = [f for f in resource_files if tools_jar_regex.search(f)] + assert len(matched_files) == 1, \ + (f'Expected exactly one tools JAR file, found {len(matched_files)}. ' + 'Rebuild the wheel package with the correct tools JAR file.') + # set the tools JAR file name in the context + self.set_ctxt('fatWheelModeJarFileName', FSUtil.get_resource_name(matched_files[0])) + def load_prepackaged_resources(self): """ Checks if the packaging includes the CSP dependencies. If so, it moves the dependencies @@ -142,7 +157,7 @@ def load_prepackaged_resources(self): """ if not self.are_resources_prepackaged(): return - self.set_ctxt('fatwheelModeEnabled', True) + self.set_ctxt('fatWheelModeEnabled', True) self.logger.info(Utils.gen_str_header('Fat Wheel Mode Is Enabled', ruler='_', line_width=50)) @@ -151,10 +166,12 @@ def load_prepackaged_resources(self): if os.path.isdir(res_path): # this is a directory, copy all the contents to the tmp FSUtil.copy_resource(res_path, self.get_cache_folder()) + self._identify_fat_wheel_jar(FSUtil.get_all_files(res_path)) else: # this is an archived file with tarfile.open(res_path, mode='r:*') as tar_file: tar_file.extractall(self.get_cache_folder()) + self._identify_fat_wheel_jar(tar_file.getnames()) tar_file.close() def get_output_folder(self) -> str: @@ -172,13 +189,8 @@ def get_rapids_jar_url(self) -> str: self.logger.info('Fetching the Rapids Jar URL') # get the version from the package, instead of the yaml file # jar_version = self.get_value('sparkRapids', 'version') - if self.is_fatwheel_mode(): - offline_path_regex = FSUtil.build_path(self.get_cache_folder(), 'rapids-4-spark-tools_*.jar') - matching_files = glob(offline_path_regex) - if not matching_files: - raise FileNotFoundError('In Fat Mode. No matching JAR files found.') - self.logger.info('Using jar from wheel file %s', matching_files[0]) - return matching_files[0] + if self.is_fat_wheel_mode(): + return self._get_tools_jar_in_fat_wheel_mode() mvn_base_url = self.get_value('sparkRapids', 'mvnUrl') jar_version = Utilities.get_latest_mvn_jar_from_metadata(mvn_base_url) rapids_url = self.get_value('sparkRapids', 'repoUrl').format(mvn_base_url, jar_version, jar_version) @@ -209,3 +221,23 @@ def get_platform_name(self) -> str: :return: the name of the platform of the runtime in lower_case. """ return CspEnv.pretty_print(self.platform.type_id) + + def _get_tools_jar_in_fat_wheel_mode(self) -> str: + """ + Extracts the tools JAR file from the context and returns its path from the cache folder. + """ + jar_filename = self.get_ctxt('fatWheelModeJarFileName') + if jar_filename is None: + raise ValueError( + 'In Fat Mode. Tools JAR file name not found in context. ' + 'Rebuild the wheel package or re-run without fat wheel mode.' + ) + # construct the path to the tools JAR file in the cache folder + jar_filepath = FSUtil.build_path(self.get_cache_folder(), jar_filename) + if not FSUtil.resource_exists(jar_filepath): + raise FileNotFoundError( + f'In Fat Mode. Tools JAR not found in cache folder: {jar_filepath}. ' + 'Rebuild the wheel package or re-run without fat wheel mode.' + ) + self.logger.info('Using jar from wheel file %s', jar_filepath) + return jar_filepath diff --git a/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml index d4bcd257f..877d94b42 100644 --- a/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml @@ -9,6 +9,7 @@ toolOutput: sparkRapids: mvnUrl: 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12' repoUrl: '{}/{}/rapids-4-spark-tools_2.12-{}.jar' + toolsJarRegex: 'rapids-4-spark-tools_2.12-.*.jar' mainClass: 'com.nvidia.spark.rapids.tool.profiling.ProfileMain' outputDocURL: 'https://docs.nvidia.com/spark-rapids/user-guide/latest/profiling/quickstart.html#profiling-output' enableAutoTuner: true diff --git a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml index 4c250611f..438deaddb 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -61,6 +61,7 @@ toolOutput: sparkRapids: mvnUrl: 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12' repoUrl: '{}/{}/rapids-4-spark-tools_2.12-{}.jar' + toolsJarRegex: 'rapids-4-spark-tools_2.12-.*.jar' mainClass: 'com.nvidia.spark.rapids.tool.qualification.QualificationMain' outputDocURL: 'https://docs.nvidia.com/spark-rapids/user-guide/latest/qualification/quickstart.html#qualification-output' enableAutoTuner: true From 55a04605d57adf6de813bafddc60fb470d167439 Mon Sep 17 00:00:00 2001 From: Cindy Jiang <47068112+cindyyuanjiang@users.noreply.github.com> Date: Thu, 26 Sep 2024 14:33:58 -0700 Subject: [PATCH 10/24] [FEA] Add total core seconds into top candidate view (#1342) * add total core seconds into top candidate view Signed-off-by: cindyyuanjiang * fix style error Signed-off-by: cindyyuanjiang * clean up code comments Signed-off-by: cindyyuanjiang * update comments Signed-off-by: cindyyuanjiang * address review feedback Signed-off-by: cindyyuanjiang * fix python style Signed-off-by: cindyyuanjiang * moved hard coded categories to yaml file Signed-off-by: cindyyuanjiang * address review feedback Signed-off-by: cindyyuanjiang * fix typos Signed-off-by: cindyyuanjiang --------- Signed-off-by: cindyyuanjiang --- .../rapids/qualification.py | 1 + .../resources/qualification-conf.yaml | 9 +++- .../tools/additional_heuristics.py | 48 +++++++++++++++---- .../tools/top_candidates.py | 9 ++-- 4 files changed, 53 insertions(+), 14 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/rapids/qualification.py b/user_tools/src/spark_rapids_pytools/rapids/qualification.py index 3e49a81a6..7ceb4d684 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/qualification.py +++ b/user_tools/src/spark_rapids_pytools/rapids/qualification.py @@ -13,6 +13,7 @@ # limitations under the License. """Implementation class representing wrapper around the RAPIDS acceleration Qualification tool.""" + import json import re from dataclasses import dataclass, field diff --git a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml index 438deaddb..54496464e 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -32,6 +32,7 @@ toolOutput: - SQL Stage Durations Sum - Unsupported Operators Stage Duration - Unsupported Operators Stage Duration Percent + - Total Core Seconds mapColumns: groupColumns: enabled: false @@ -227,12 +228,15 @@ local: - 'Qualified Cluster Recommendation' - 'Full Cluster Config Recommendations*' - 'GPU Config Recommendation Breakdown*' + ineligibleCategory: + - 'Not Recommended' eligibleCategories: - 'Small' - 'Medium' - 'Large' sortingColumns: - - 'Estimated GPU Speedup' + - 'Speedup Category Order' + - 'Total Core Seconds' - 'Unsupported Operators Stage Duration Percent' joinColumns: - 'App ID' @@ -274,6 +278,9 @@ local: - 'App ID' - 'Skip by Heuristics' - 'Reason' + totalCoreSecBased: + # This is total core seconds of an 8-core machine running for 24 hours + totalCoreSecThreshold: 691200 spillBased: stageAggMetrics: fileName: 'stage_level_aggregated_task_metrics.csv' diff --git a/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py b/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py index 1f4591f82..18b207357 100644 --- a/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py +++ b/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py @@ -22,6 +22,7 @@ import pandas as pd from spark_rapids_pytools.common.prop_manager import JSONPropertiesContainer +from spark_rapids_pytools.common.sys_storage import FSUtil from spark_rapids_pytools.common.utilities import ToolLogging from spark_rapids_tools.tools.qualx.util import find_paths, RegexPattern from spark_rapids_tools.utils import Utilities @@ -36,6 +37,8 @@ class AdditionalHeuristics: props: JSONPropertiesContainer = field(default=None, init=False) tools_output_dir: str = field(default=None, init=False) output_file: str = field(default=None, init=False) + # Contains apps info needed for applying heuristics + all_apps: pd.DataFrame = field(default=None, init=False) def __init__(self, props: dict, tools_output_dir: str, output_file: str): self.props = JSONPropertiesContainer(props, file_load=False) @@ -43,6 +46,12 @@ def __init__(self, props: dict, tools_output_dir: str, output_file: str): self.output_file = output_file self.logger = ToolLogging.get_and_setup_logger(f'rapids.tools.{self.__class__.__name__}') + def _get_all_heuristics_functions(self) -> list: + """ + Returns a list of heuristics functions to apply to each application. + """ + return [self.heuristics_based_on_total_core_seconds, self.heuristics_based_on_spills] + def _apply_heuristics(self, app_ids: list) -> pd.DataFrame: """ Apply additional heuristics to applications to determine if they can be accelerated on GPU. @@ -68,18 +77,36 @@ def _apply_heuristics(self, app_ids: list) -> pd.DataFrame: else: for app_id in app_ids: app_id_path = os.path.join(metrics_path, app_id) - try: - # Apply heuristics and determine if the application should be skipped. - # Note: `should_skip` flag can be a combination of multiple heuristic checks. - should_skip, reason = self.heuristics_based_on_spills(app_id_path) - except Exception as e: # pylint: disable=broad-except - should_skip = False - reason = f'Cannot apply heuristics for qualification. Reason - {type(e).__name__}:{e}' - self.logger.error(reason) - result_arr.append([app_id, should_skip, reason]) + # Apply a list of heuristics and determine if the application should be skipped. + should_skip_overall = False + reasons = [] + for heuristic_func in self._get_all_heuristics_functions(): + try: + should_skip, reason = heuristic_func(app_id_path) + except Exception as e: # pylint: disable=broad-except + should_skip = False + reason = f' Cannot apply heuristics for qualification. Reason - {type(e).__name__}:{e}.' + self.logger.error(reason) + should_skip_overall = should_skip_overall or should_skip + reasons.append(reason) + result_arr.append([app_id, should_skip_overall, ' '.join(reasons)]) return pd.DataFrame(result_arr, columns=self.props.get_value('resultCols')) + def heuristics_based_on_total_core_seconds(self, app_id_path: str) -> (bool, str): + """ + Apply heuristics based on total core seconds to determine if the app can be accelerated on GPU. + """ + # Load app total core seconds from qualification summary dataframe + app_id = FSUtil.get_resource_name(app_id_path) + app_qual_output = self.all_apps[self.all_apps['App ID'] == app_id] # type: ignore + total_core_seconds = app_qual_output['Total Core Seconds'].astype(int).iloc[0] + total_core_seconds_threshold = self.props.get_value('totalCoreSecBased', 'totalCoreSecThreshold') + if total_core_seconds <= total_core_seconds_threshold: + return True, f'Skipping due to total core seconds = {total_core_seconds} lower than ' + \ + f'{total_core_seconds_threshold}.' + return False, '' + def heuristics_based_on_spills(self, app_id_path: str) -> (bool, str): """ Apply heuristics based on spills to determine if the app can be accelerated on GPU. @@ -113,12 +140,13 @@ def heuristics_based_on_spills(self, app_id_path: str) -> (bool, str): if not relevant_stages_with_spills.empty: stages_str = '; '.join(relevant_stages_with_spills['stageId'].astype(str)) spill_threshold_human_readable = Utilities.bytes_to_human_readable(spill_threshold_bytes) - reason = f'Skipping due to spills in stages [{stages_str}] exceeding {spill_threshold_human_readable}' + reason = f'Skipping due to spills in stages [{stages_str}] exceeding {spill_threshold_human_readable}.' return True, reason return False, '' def apply_heuristics(self, all_apps: pd.DataFrame) -> pd.DataFrame: try: + self.all_apps = all_apps heuristics_df = self._apply_heuristics(all_apps['App ID'].unique()) # Save the heuristics results to a file and drop the reason column heuristics_df.to_csv(self.output_file, index=False) diff --git a/user_tools/src/spark_rapids_tools/tools/top_candidates.py b/user_tools/src/spark_rapids_tools/tools/top_candidates.py index 49b93db48..850508c96 100644 --- a/user_tools/src/spark_rapids_tools/tools/top_candidates.py +++ b/user_tools/src/spark_rapids_tools/tools/top_candidates.py @@ -85,12 +85,15 @@ def _generate_output_table_internal(self, output_df: pd.DataFrame) -> pd.DataFra """ Internal implementation to prepare the output table. This can be overridden by the child classes. """ + # Create and append 'Speedup Category Order' column to output_df for sorting order + speedup_category_order = self.props.get('ineligibleCategory') + self.props.get('eligibleCategories') + output_df['Speedup Category Order'] = \ + output_df['Estimated GPU Speedup Category'].map({name: i for i, name in enumerate(speedup_category_order)}) + # Sort columns and select output columns output_columns = self.props.get('outputColumns') sorting_columns = self.props.get('sortingColumns') valid_output_columns = list(output_df.columns.intersection(output_columns)) - valid_sorting_columns = list(output_df.columns.intersection(sorting_columns)) - # Sort columns and select output columns - res_df = output_df.sort_values(by=valid_sorting_columns, ascending=False)[valid_output_columns] + res_df = output_df.sort_values(by=sorting_columns, ascending=False)[valid_output_columns] # this is a bit weird since hardcoding, but we don't want this to have ** for csv output if 'Estimated GPU Speedup Category' in res_df: res_df.rename(columns={'Estimated GPU Speedup Category': 'Estimated GPU Speedup Category**'}, From de7c3a0206e05480ed81bb54cf8d0839bdbfdef9 Mon Sep 17 00:00:00 2001 From: Lee Yang Date: Mon, 30 Sep 2024 09:37:11 -0700 Subject: [PATCH 11/24] Isolate dataset for qualx plugin invocations (#1361) * Isolate dataset for plugin invocations Signed-off-by: Lee Yang * Use f-strings Signed-off-by: Lee Yang * Preserve dtypes after pd.DataFrame.update() Signed-off-by: Lee Yang --------- Signed-off-by: Lee Yang --- user_tools/docs/qualx.md | 19 ++++++-- .../spark_rapids_tools/tools/qualx/model.py | 43 ++++++++++++++++--- .../tools/qualx/preprocess.py | 21 ++++++--- .../tools/qualx/qualx_main.py | 6 +-- 4 files changed, 70 insertions(+), 19 deletions(-) diff --git a/user_tools/docs/qualx.md b/user_tools/docs/qualx.md index 814625d1a..be349ba65 100644 --- a/user_tools/docs/qualx.md +++ b/user_tools/docs/qualx.md @@ -189,16 +189,27 @@ any of the following functions: import pandas as pd def load_profiles_hook(profile_df: pd.DataFrame) -> pd.DataFrame: - """Custom post processing on the load_profiles dataframe.""" + """Custom post processing on the load_profiles dataframe. + + Notes: + - profile_df contains "raw" features for the target dataset, extracted from the Profiler tool's output CSV files. + - profile_df is a slice of the original dataframe, so the original indices must be preserved, + and any new columns will be ignored/dropped. + """ # Insert custom code to modify the profile_df as needed. - # Note: profile_df contains "raw" features extracted from the Profiler tool's output CSV files. return profile_df def split_function(cpu_aug_tbl: pd.DataFrame) -> pd.DataFrame: - """Custom train/test/val split function.""" + """Custom train/test/val split function. + + Notes: + - cpu_aug_tbl contains the "model" features for the target dataset, which will be used in training. + - cpu_aug_tbl is a slice of the original dataframe, so the original indices must be preserved, + and any new columns will be ignored/dropped. + - if not supplied, the default split function will randomly split the data by ratios of 60/20/20. + """ # Insert custom code to set cpu_aug_tbl['split'] to 'train', 'test', or 'val'. - # Note: the default split function randomly splits the data by ratios of 60/20/20. return cpu_aug_tbl ``` diff --git a/user_tools/src/spark_rapids_tools/tools/qualx/model.py b/user_tools/src/spark_rapids_tools/tools/qualx/model.py index a64299c3d..26ae3641d 100644 --- a/user_tools/src/spark_rapids_tools/tools/qualx/model.py +++ b/user_tools/src/spark_rapids_tools/tools/qualx/model.py @@ -14,7 +14,7 @@ """ Model training and prediction functions for QualX """ -from typing import Callable, Optional, List, Tuple +from typing import Callable, Mapping, Optional, List, Tuple import json import random import shap @@ -217,7 +217,7 @@ def predict( def extract_model_features( - df: pd.DataFrame, split_fn: List[Callable[[pd.DataFrame], pd.DataFrame]] = None + df: pd.DataFrame, split_functions: Mapping[str, Callable[[pd.DataFrame], pd.DataFrame]] = None ) -> Tuple[pd.DataFrame, List[str], str]: """Extract model features from raw features.""" missing = expected_raw_features - set(df.columns) @@ -314,11 +314,42 @@ def extract_model_features( logger.warning('Input data has extra features (removed): %s', extra) feature_cols = [c for c in feature_cols if c not in extra] - # add train/val/test split column, if split function provided - if split_fn: - for fn in split_fn: - cpu_aug_tbl = fn(cpu_aug_tbl) + # add train/val/test split column, if split function(s) provided + if split_functions: + # ensure 'split' column in cpu_aug_tbl + if 'split' not in cpu_aug_tbl.columns: + cpu_aug_tbl['split'] = pd.Series(dtype='str') + # save schema, since df.update() defaults all dtypes to floats + df_schema = cpu_aug_tbl.dtypes + + # extract default split function, if present + default_split_fn = split_functions.pop('default') if 'default' in split_functions else None + + # handle all other dataset-specific split functions + for ds_name, split_fn in split_functions.items(): + dataset_df = cpu_aug_tbl.loc[ + (cpu_aug_tbl.appName == ds_name) | (cpu_aug_tbl.appName.str.startswith(f'{ds_name}:')) + ] + modified_df = split_fn(dataset_df) + if modified_df.index.equals(dataset_df.index): + cpu_aug_tbl.update(modified_df) + cpu_aug_tbl.astype(df_schema) + else: + raise ValueError(f'Plugin: split_function for {ds_name} unexpectedly modified row indices.') + cpu_aug_tbl.update(dataset_df) + + # handle default split function + if default_split_fn: + default_df = cpu_aug_tbl.loc[~cpu_aug_tbl.appName.isin(split_functions.keys())] + for ds_name in split_functions.keys(): + default_df = default_df.loc[~default_df.appName.str.startswith(f'{ds_name}:')] + modified_default_df = default_split_fn(default_df) + if modified_default_df.index.equals(default_df.index): + cpu_aug_tbl.update(default_df) + cpu_aug_tbl.astype(df_schema) + else: + raise ValueError('Default split_function unexpectedly modified row indices.') return cpu_aug_tbl, feature_cols, label_col diff --git a/user_tools/src/spark_rapids_tools/tools/qualx/preprocess.py b/user_tools/src/spark_rapids_tools/tools/qualx/preprocess.py index 302a34c87..0aadd6974 100644 --- a/user_tools/src/spark_rapids_tools/tools/qualx/preprocess.py +++ b/user_tools/src/spark_rapids_tools/tools/qualx/preprocess.py @@ -278,7 +278,7 @@ def infer_app_meta(eventlogs: List[str]) -> Mapping[str, Mapping]: } return app_meta_inner - plugins = [] + plugins = {} all_raw_features = [] # get list of csv files from each profile for ds_name, ds_meta in datasets.items(): @@ -287,7 +287,7 @@ def infer_app_meta(eventlogs: List[str]) -> Mapping[str, Mapping]: platform = ds_meta.get('platform', 'onprem') scale_factor_meta = ds_meta.get('scaleFactorFromSqlIDRank', None) if 'load_profiles_hook' in ds_meta: - plugins.append(ds_meta['load_profiles_hook']) + plugins[ds_name] = ds_meta['load_profiles_hook'] if not app_meta: # if no 'app_meta' key provided, infer app_meta from directory structure of eventlogs @@ -380,10 +380,19 @@ def infer_app_meta(eventlogs: List[str]) -> Mapping[str, Mapping]: ) # run any plugin hooks on profile_df - for p in plugins: - plugin = load_plugin(p) - profile_df = plugin.load_profiles_hook(profile_df) - + for ds_name, plugin_path in plugins.items(): + plugin = load_plugin(plugin_path) + if plugin: + df_schema = profile_df.dtypes + dataset_df = profile_df.loc[ + (profile_df.appName == ds_name) | (profile_df.appName.str.startswith(f'{ds_name}:')) + ] + modified_dataset_df = plugin.load_profiles_hook(dataset_df) + if modified_dataset_df.index.equals(dataset_df.index): + profile_df.update(modified_dataset_df) + profile_df.astype(df_schema) + else: + raise ValueError(f'Plugin: load_profiles_hook for {ds_name} unexpectedly modified row indices.') return profile_df diff --git a/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py b/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py index 880fe7714..d14849cfc 100644 --- a/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py +++ b/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py @@ -250,7 +250,7 @@ def _predict( else 'raw' ) logger.info('Predicting dataset (%s): %s', filter_str, dataset) - features, feature_cols, label_col = extract_model_features(input_df, [split_fn]) + features, feature_cols, label_col = extract_model_features(input_df, {'default': split_fn}) # note: dataset name is already stored in the 'appName' field try: results = predict_model(xgb_model, features, feature_cols, label_col) @@ -462,13 +462,13 @@ def train( 'Training data contained datasets: %s, expected: %s', profile_datasets, dataset_list ) - split_functions = [split_train_val] + split_functions = {'default': split_train_val} for ds_name, ds_meta in datasets.items(): if 'split_function' in ds_meta: plugin_path = ds_meta['split_function'] logger.info('Using split function for %s dataset from plugin: %s', ds_name, plugin_path) plugin = load_plugin(plugin_path) - split_functions.append(plugin.split_function) + split_functions[ds_name] = plugin.split_function features, feature_cols, label_col = extract_model_features(profile_df, split_functions) From 7fce503761041e7e4f01b3613d901730b766c9d6 Mon Sep 17 00:00:00 2001 From: Lee Yang Date: Tue, 1 Oct 2024 15:22:18 -0700 Subject: [PATCH 12/24] Update models for updated LHA datasets and latest code (#1365) Signed-off-by: Lee Yang --- .../qualx/models/xgboost/combined.cfg | 2 +- .../qualx/models/xgboost/combined.json | 2 +- .../qualx/models/xgboost/combined.metrics | 178 ++++++++-------- .../qualx/models/xgboost/databricks-aws.cfg | 2 +- .../qualx/models/xgboost/databricks-aws.json | 2 +- .../models/xgboost/databricks-aws.metrics | 190 +++++++++--------- .../models/xgboost/databricks-azure.json | 2 +- .../qualx/models/xgboost/dataproc.json | 2 +- .../resources/qualx/models/xgboost/emr.json | 2 +- .../qualx/models/xgboost/onprem.json | 2 +- 10 files changed, 192 insertions(+), 192 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.cfg b/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.cfg index b36006474..7ee346b93 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.cfg +++ b/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.cfg @@ -1 +1 @@ -{"learner":{"generic_param":{"device":"cpu","fail_on_invalid_gpu_id":"0","n_jobs":"0","nthread":"0","random_state":"0","seed":"0","seed_per_iteration":"0","validate_parameters":"1"},"gradient_booster":{"gbtree_model_param":{"num_parallel_tree":"1","num_trees":"94"},"gbtree_train_param":{"process_type":"default","tree_method":"auto","updater":"grow_quantile_histmaker","updater_seq":"grow_quantile_histmaker"},"name":"gbtree","specified_updater":false,"tree_train_param":{"alpha":"0","cache_opt":"1","colsample_bylevel":"1","colsample_bynode":"1","colsample_bytree":"1","eta":"0.0449656211","gamma":"0.0091063315","grow_policy":"depthwise","interaction_constraints":"","lambda":"1","learning_rate":"0.0449656211","max_bin":"256","max_cat_threshold":"64","max_cat_to_onehot":"4","max_delta_step":"0","max_depth":"7","max_leaves":"0","min_child_weight":"3","min_split_loss":"0.0091063315","monotone_constraints":"()","refresh_leaf":"1","reg_alpha":"0","reg_lambda":"1","sampling_method":"uniform","sketch_ratio":"2","sparse_threshold":"0.20000000000000001","subsample":"0.627439141"},"updater":[{"hist_train_param":{"debug_synchronize":"0","max_cached_hist_node":"65536"},"name":"grow_quantile_histmaker"}]},"learner_model_param":{"base_score":"7.489082E-1","boost_from_average":"1","num_class":"0","num_feature":"108","num_target":"1"},"learner_train_param":{"booster":"gbtree","disable_default_eval_metric":"0","multi_strategy":"one_output_per_tree","objective":"reg:squarederror"},"metrics":[{"name":"mae"},{"name":"mape"}],"objective":{"name":"reg:squarederror","reg_loss_param":{"scale_pos_weight":"1"}}},"version":[2,0,3]} \ No newline at end of file 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\ No newline at end of file diff --git a/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.json b/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.json index 26b625d84..6a7e50e81 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.json +++ b/user_tools/src/spark_rapids_pytools/resources/qualx/models/xgboost/combined.json @@ -1 +1 @@ -{"learner":{"attributes":{},"feature_names":["numTasks_sum","duration_sum","duration_max","duration_min","duration_mean","input_recordsRead_sum","output_recordsWritten_sum","peakExecutionMemory_max","resultSerializationTime_sum","resultSize_max","sr_localBlocksFetched_sum","sr_remoteBlocksFetched_sum","sw_recordsWritten_sum","sqlOp_ColumnarToRow","sqlOp_Exchange","sqlOp_Execute InsertIntoHadoopFsRelationCommand","sqlOp_Scan parquet 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\ No newline at end of file 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\ No newline at end of file From 6cb7fa008b97dc65ec5334d63cffa0f87eb7de08 Mon Sep 17 00:00:00 2001 From: Niranjan Artal <50492963+nartal1@users.noreply.github.com> Date: Thu, 3 Oct 2024 13:47:47 -0700 Subject: [PATCH 13/24] Generate log files for Python Profiling cli (#1366) Signed-off-by: Niranjan Artal --- user_tools/src/spark_rapids_pytools/common/utilities.py | 7 ++----- user_tools/src/spark_rapids_pytools/rapids/rapids_job.py | 3 ++- user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py | 3 +++ .../src/spark_rapids_pytools/resources/profiling-conf.yaml | 2 ++ .../spark_rapids_pytools/resources/qualification-conf.yaml | 1 + user_tools/src/spark_rapids_tools/cmdli/argprocessor.py | 3 ++- 6 files changed, 12 insertions(+), 7 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/common/utilities.py b/user_tools/src/spark_rapids_pytools/common/utilities.py index b867a89a0..d40ec13ab 100644 --- a/user_tools/src/spark_rapids_pytools/common/utilities.py +++ b/user_tools/src/spark_rapids_pytools/common/utilities.py @@ -231,7 +231,7 @@ def get_and_setup_logger(cls, type_label: str, debug_mode: bool = False): return logger @classmethod - def modify_log4j_properties(cls, prop_file_path: str, new_log_dir: str) -> str: + def modify_log4j_properties(cls, prop_file_path: str, new_log_file: str) -> str: """ Modifies the log file path in a log4j properties file to redirect logging output to a new location. @@ -242,15 +242,12 @@ def modify_log4j_properties(cls, prop_file_path: str, new_log_dir: str) -> str: :param prop_file_path: The file path to the original log4j.properties file. This file should contain configurations for the log4j logging utility. - :param new_log_dir: The base output directory where the new log file will be created. - The actual log file named 'rapids_4_spark_qualification_stderr.log' - will be placed in this directory. + :param new_log_file: The file path where the logging output is saved. :return str: The file path to the temporary modified log4j.properties file. This temporary file retains the modifications and can be accessed until explicitly deleted after the java process is completed. """ - new_log_file = f'{new_log_dir}/rapids_4_spark_qualification_stderr.log' with open(prop_file_path, 'r', encoding='utf-8') as file: lines = file.readlines() diff --git a/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py b/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py index 49cdb3209..f095daa3f 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py +++ b/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py @@ -192,8 +192,9 @@ def _build_jvm_args(self): if jvm_k.startswith('D'): if jvm_k == 'Dlog4j.configuration': rapids_output_folder = self.exec_ctxt.get_rapids_output_folder() + log4j_file_name = self.exec_ctxt.get_log4j_properties_file() jvm_arg = ToolLogging.modify_log4j_properties( - jvm_arg, f'{rapids_output_folder}') + jvm_arg, f'{rapids_output_folder}/{log4j_file_name}') self.exec_ctxt.set_local('tmp_log4j', jvm_arg) val = f'-{jvm_k}={jvm_arg}' else: diff --git a/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py b/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py index 320aa69c9..e0756784b 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py +++ b/user_tools/src/spark_rapids_pytools/rapids/tool_ctxt.py @@ -215,6 +215,9 @@ def get_rapids_output_folder(self) -> str: return root_dir return FSUtil.build_path(root_dir, rapids_subfolder) + def get_log4j_properties_file(self) -> str: + return self.get_value_silent('toolOutput', 'textFormat', 'log4jFileName') + def get_platform_name(self) -> str: """ This used to get the lower case of the platform of the runtime. diff --git a/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml index 877d94b42..847e33914 100644 --- a/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml @@ -1,5 +1,7 @@ toolOutput: subFolder: rapids_4_spark_profile + textFormat: + log4jFileName: rapids_4_spark_profile_stderr.log recommendations: fileName: profile.log headers: diff --git a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml index 54496464e..694696c7d 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -4,6 +4,7 @@ toolOutput: textFormat: summaryLog: fileName: rapids_4_spark_qualification_output.log + log4jFileName: rapids_4_spark_qualification_stderr.log csv: unsupportedOperatorsReport: fileName: rapids_4_spark_qualification_output_unsupportedOperators.csv diff --git a/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py b/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py index bb7da1def..e578d23b6 100644 --- a/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py +++ b/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py @@ -617,7 +617,8 @@ def build_tools_args(self) -> dict: 'remoteFolder': None, 'platformArgs': { 'jvmMaxHeapSize': self.p_args['toolArgs']['jvmMaxHeapSize'], - 'jvmGC': self.p_args['toolArgs']['jvmGC'] + 'jvmGC': self.p_args['toolArgs']['jvmGC'], + 'Dlog4j.configuration': self.p_args['toolArgs']['log4jPath'] }, 'jobResources': self.p_args['toolArgs']['jobResources'] }, From b053680948e5e711ae6f696c4ce96a3c168a8041 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Mon, 7 Oct 2024 09:13:01 -0500 Subject: [PATCH 14/24] Add Reflection to support custom Spark Implementation at Runtime (#1362) * Add Reflection to support custom Spark Implementation at Runtime Signed-off-by: Ahmed Hussein Fixes #1360 Adds a workaorund to run against open source Spark and custom Spark implementation that overrides the constructors of Graph objects. * Address the PR comments Signed-off-by: Ahmed Hussein * Remove extra unecessary comment Signed-off-by: Ahmed Hussein * Fix typo Signed-off-by: Ahmed Hussein --------- Signed-off-by: Ahmed Hussein --- .../rapids/SparkRapidsBuildInfoEvent.scala | 8 +- .../spark/scheduler/SparkListenerEvent.scala | 40 +++ .../ToolsListenerEventExtraAPIs.scala | 34 +++ .../tool/annotation/ToolsReflection.scala | 32 +++ .../sql/rapids/tool/util/ToolsPlanGraph.scala | 257 +++--------------- .../stubs/DefaultGraphReflectionAPI.scala | 51 ++++ .../tool/util/stubs/GraphReflectionAPI.scala | 45 +++ .../util/stubs/GraphReflectionAPIHelper.scala | 60 ++++ .../util/stubs/GraphReflectionEntry.scala | 61 +++++ .../util/stubs/bd/BDGraphClusterStub.scala | 37 +++ .../tool/util/stubs/bd/BDGraphNodeStub.scala | 35 +++ .../util/stubs/bd/BDGraphReflectionAPI.scala | 42 +++ .../util/stubs/db/DBGraphClusterStub.scala | 47 ++++ .../tool/util/stubs/db/DBGraphEdgeStub.scala | 41 +++ .../tool/util/stubs/db/DBGraphNodeStub.scala | 53 ++++ .../util/stubs/db/DBGraphReflectionAPI.scala | 59 ++++ .../util/stubs/db/DBGraphSQLMetricStub.scala | 45 +++ .../sql/rapids/tool/util/stubs/package.scala | 25 ++ 18 files changed, 755 insertions(+), 217 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/scheduler/SparkListenerEvent.scala create mode 100644 core/src/main/scala/org/apache/spark/scheduler/ToolsListenerEventExtraAPIs.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/annotation/ToolsReflection.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/DefaultGraphReflectionAPI.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPI.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPIHelper.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionEntry.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphClusterStub.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphNodeStub.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphReflectionAPI.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphClusterStub.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphEdgeStub.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphNodeStub.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphReflectionAPI.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphSQLMetricStub.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/package.scala diff --git a/core/src/main/scala/com/nvidia/spark/rapids/SparkRapidsBuildInfoEvent.scala b/core/src/main/scala/com/nvidia/spark/rapids/SparkRapidsBuildInfoEvent.scala index 019ed8bf4..3ece8ce3e 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/SparkRapidsBuildInfoEvent.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/SparkRapidsBuildInfoEvent.scala @@ -17,6 +17,7 @@ package com.nvidia.spark.rapids import org.apache.spark.scheduler.SparkListenerEvent +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection /** @@ -30,4 +31,9 @@ case class SparkRapidsBuildInfoEvent( sparkRapidsJniBuildInfo: Map[String, String], cudfBuildInfo: Map[String, String], sparkRapidsPrivateBuildInfo: Map[String, String] -) extends SparkListenerEvent +) extends SparkListenerEvent { + @ToolsReflection("BD-3.2.1", "Ignore") + override val eventTime: Long = 0 + @ToolsReflection("BD-3.2.1", "Ignore") + override val eventType: String = "" +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerEvent.scala new file mode 100644 index 000000000..7b209e55a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerEvent.scala @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import com.fasterxml.jackson.annotation.JsonTypeInfo + +import org.apache.spark.annotation.DeveloperApi + +/** + * This code is mostly copied from org.apache.spark.scheduler.SparkListenerEvent + * to make it compatible at runtime with custom Spark implementation that defines abstract methods + * in the trait. + * + * This class is packaged due to a bug in Scala 2.12 that links the method + * to the abstract trait, which might not exist in the classpath. + * See the related Scala issues: + * https://github.com/scala/bug/issues/10477 + * https://github.com/scala/scala-dev/issues/219 + * https://github.com/scala/scala-dev/issues/268 + */ +@DeveloperApi +@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "Event") +trait SparkListenerEvent extends ToolsListenerEventExtraAPIs { + /* Whether output this event to the event log */ + protected[spark] def logEvent: Boolean = true +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/ToolsListenerEventExtraAPIs.scala b/core/src/main/scala/org/apache/spark/scheduler/ToolsListenerEventExtraAPIs.scala new file mode 100644 index 000000000..b45a3ea63 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/ToolsListenerEventExtraAPIs.scala @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection + +/** + * Base trait for events related to SparkRapids build info. This used to add extra APIs that are + * not defined in the base Spark trait. This is a work around to be compatible in + * runtime with custom Spark implementations that define abstract methods in the trait. + * see https://github.com/NVIDIA/spark-rapids-tools/issues/1360 + */ +trait ToolsListenerEventExtraAPIs { + @ToolsReflection("BD-3.2.1", + "Ignore the implementation: Definition for an abstract field in the SparkListenerEvent.") + val eventTime: Long = 0 + @ToolsReflection("BD-3.2.1", + "Ignore the implementation: Definition for an abstract field in the SparkListenerEvent.") + val eventType: String = "" +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/annotation/ToolsReflection.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/annotation/ToolsReflection.scala new file mode 100644 index 000000000..95a74b697 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/annotation/ToolsReflection.scala @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.annotation + +import scala.annotation.StaticAnnotation +import scala.annotation.meta.{beanGetter, beanSetter, field, getter, param, setter} + + +/** + * This code is mostly copied from org.apache.spark.annotation.Since + * Reason is copied here because it is being private to Spark packages which makes it + * inaccessible for Non-Spark packages. + * + * A Scala annotation that indicates entities that are used for reeflection in Tools to match + * different Spark runtime APIs + */ +@param @field @getter @setter @beanGetter @beanSetter +class ToolsReflection(source: String, comment: String) extends StaticAnnotation diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala index a112da0b9..1e4243595 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala @@ -19,229 +19,59 @@ package org.apache.spark.sql.rapids.tool.util import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable -import scala.reflect.runtime.universe._ import org.apache.spark.sql.execution.SparkPlanInfo import org.apache.spark.sql.execution.ui.{SparkPlanGraph, SparkPlanGraphCluster, SparkPlanGraphEdge, SparkPlanGraphNode, SQLPlanMetric} import org.apache.spark.sql.rapids.tool.store.AccumNameRef - -class DBReflectionEntry[T](mirror: Mirror, className: String, paramsSize: Option[Int] = None) { - // Get the class symbol - private val classSymbol = mirror.staticClass(className) - private val reflectiveClass = mirror.reflectClass(classSymbol) - // Get the constructor method symbol - val constr: MethodSymbol = createConstructor(classSymbol, paramsSize) - - // If the paramsCount is defined, we select the the constructor that has parameters size equal to - // that value - private def createConstructor(symbol: ClassSymbol, paramsCount: Option[Int]): MethodSymbol = { - paramsCount match { - case None => - // return the primary constructor - symbol.primaryConstructor.asMethod - case Some(count) => - // return the constructor with given parameter size - val constructors = symbol.info.decls.filter(_.isConstructor) - .map(_.asMethod) - .filter(_.paramLists.flatten.size == count) - val constructor = constructors.headOption.getOrElse { - throw new IllegalArgumentException( - s"No constructor found with exactly $count parameters for class[$className]") - } - constructor - } - } - - def createInstanceFromList(args: List[_]): T = { - reflectiveClass - .reflectConstructor(constr)(args: _*) - .asInstanceOf[T] - } -} - -case class DBGraphNodeStub(m: Mirror) - extends DBReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphNode]( - m, "org.apache.spark.sql.execution.ui.SparkPlanGraphNode") { - // DataBricks has different constructor of the sparkPlanGraphNode - // [(long,java.lang.String,java.lang.String,scala.collection.Seq,java.lang.String, - // boolean,scala.Option,scala.Option)] and - // [final long id, final java.lang.String name, final java.lang.String desc, - // final scala.collection.Seq metrics, - // final java.lang.String rddScopeId, final boolean started, - // final scala.Option estRowCount) - - // For 10.4 --> only 1 constructor and has 6 arguments (not 7) - // (final long id, final java.lang.String name, final java.lang.String desc, - // final scala.collection.Seq metrics, - // final java.lang.String rddScopeId, final scala.Option estRowCount - - // DB10.4 has constructor with 6 arguments. - private val isDB104OrOlder: Boolean = constr.paramLists.flatten.size < 7 - - def createInstance(id: Long, name: String, desc: String, - metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { - // Define argument values - val argValues = if (isDB104OrOlder) { - List(id, name, desc, metrics, "", None) - } else { - List(id, name, desc, metrics, "", false, None, None) - } - createInstanceFromList(argValues) - } -} - -case class DBGraphSQLMetricStub(m: Mirror) - extends DBReflectionEntry[org.apache.spark.sql.execution.ui.SQLPlanMetric]( - m, "org.apache.spark.sql.execution.ui.SQLPlanMetric") { - // DataBricks has different constructor of the sparkPlanGraphNode - //Array(final java.lang.String name, final long accumulatorId, - // final java.lang.String metricType, final boolean experimental) - - // for 10.4 it is only one constructor with 3 arguments. - // final java.lang.String name, final long accumulatorId, final java.lang.String metricType - private val isDB104OrOlder: Boolean = constr.paramLists.flatten.size < 4 - def createInstance(name: String, - accumulatorId: Long, - metricType: String): SQLPlanMetric = { - val argValues = if (isDB104OrOlder) { - List(name, accumulatorId, metricType) - } else { - List(name, accumulatorId, metricType, false) - } - createInstanceFromList(argValues) - } -} - -case class DBGraphClusterStub(m: Mirror) - extends DBReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphCluster]( - m, "org.apache.spark.sql.execution.ui.SparkPlanGraphCluster") { - // DataBricks has different constructor of the sparkPlanGraphNode - // (final long id, final java.lang.String name, final java.lang.String desc, - // final ArrayBuffer nodes, - // final scala.collection.Seq metrics, - // final java.lang.String rddScopeId) - - // 10.4 is the same as other versions - // (final long id, final java.lang.String name, final java.lang.String desc, - // final ArrayBuffer nodes, - // final Seq metrics, - // final java.lang.String rddScopeId - def createInstance(id: Long, - name: String, - desc: String, - nodes: mutable.ArrayBuffer[SparkPlanGraphNode], - metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { - val argValues = List(id, name, desc, nodes, metrics, "") - createInstanceFromList(argValues) - } -} - -// All versions in DB accept 2 parameters for constructor. So we stick to that version -// by passing (2) to the parent class -case class DBGraphEdgeStub(m: Mirror) - extends DBReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphEdge]( - m, "org.apache.spark.sql.execution.ui.SparkPlanGraphEdge", Option(2)) { - // DataBricks has different constructor of the sparkPlanGraphNode - // (final long fromId, final long toId, - // final scala.Option numOutputRowsId) - // - // for 10.4 only one constructor with two arguments - // final long fromId, final long toId) - - def createInstance(fromId: Long, toId: Long): SparkPlanGraphEdge = { - val argValues = List(fromId, toId) - createInstanceFromList(argValues) - } -} - -// Container class to hold snapshot of the reflection fields instead of recalculating them every -// time we call the constructor -case class DBReflectionContainer() { - private val mirror = runtimeMirror(getClass.getClassLoader) - private val nodeStub = DBGraphNodeStub(mirror) - private val clusterStub = DBGraphClusterStub(mirror) - private val edgeStub = DBGraphEdgeStub(mirror) - private val metricStub = DBGraphSQLMetricStub(mirror) - - def constructNode(id: Long, name: String, desc: String, - metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { - nodeStub.createInstance(id, name, desc, metrics) - } - - def constructSQLPlanMetric(name: String, - accumulatorId: Long, - metricType: String): SQLPlanMetric = { - metricStub.createInstance(name, accumulatorId, metricType) - } - - def constructCluster(id: Long, - name: String, - desc: String, - nodes: mutable.ArrayBuffer[SparkPlanGraphNode], - metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { - clusterStub.createInstance(id, name, desc, nodes, metrics) - } - - def constructEdge(fromId: Long, toId: Long): SparkPlanGraphEdge = { - edgeStub.createInstance(fromId, toId) - } -} +import org.apache.spark.sql.rapids.tool.util.stubs.{GraphReflectionAPI, GraphReflectionAPIHelper} /** * This code is mostly copied from org.apache.spark.sql.execution.ui.SparkPlanGraph * with changes to handle GPU nodes. Without this special handle, the default SparkPlanGraph - * would not be able to recognize reused/exchange nodes leading to duplicating nodes. + * would not recognize reused/exchange nodes leading to duplicating nodes. * * Build a SparkPlanGraph from the root of a SparkPlan tree. */ object ToolsPlanGraph { - // TODO: We should have a util to detect if the runtime is Databricks. - // This can be achieved by checking for spark properties - // spark.databricks.clusterUsageTags.clusterAllTags - private lazy val dbRuntimeReflection = DBReflectionContainer() - // By default call the Spark constructor. If this fails, we fall back to the DB constructor + // Captures the API loaded at runtime if any. + var api: GraphReflectionAPI = _ + + // The actual code used to build the graph. If the API is not available, then fallback to the + // Spark default API. + private lazy val graphBuilder: SparkPlanInfo => SparkPlanGraph = { + GraphReflectionAPIHelper.api match { + case Some(_) => + // set the api to the available one + api = GraphReflectionAPIHelper.api.get + (planInfo: SparkPlanInfo) => { + val nodeIdGenerator = new AtomicLong(0) + val nodes = mutable.ArrayBuffer[SparkPlanGraphNode]() + val edges = mutable.ArrayBuffer[SparkPlanGraphEdge]() + val exchanges = mutable.HashMap[SparkPlanInfo, SparkPlanGraphNode]() + buildSparkPlanGraphNode(planInfo, nodeIdGenerator, nodes, edges, null, null, exchanges) + new SparkPlanGraph(nodes, edges) + } + case _ => + // Fallback to the default SparkPlanGraph + (planInfo: SparkPlanInfo) => { + SparkPlanGraph(planInfo) + } + } + } + + // used for testing purpose def constructGraphNode(id: Long, name: String, desc: String, metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { - try { - new SparkPlanGraphNode(id, name, desc, metrics) - } catch { - case _: java.lang.NoSuchMethodError => - dbRuntimeReflection.constructNode(id, name, desc, metrics) - } + GraphReflectionAPIHelper.api.get.constructNode(id, name, desc, metrics) } + // Normalize the accumName before creating it. private def constructSQLPlanMetric(name: String, accumulatorId: Long, metricType: String): SQLPlanMetric = { val accNameRef = AccumNameRef.getOrCreateAccumNameRef(name) - try { - SQLPlanMetric(accNameRef.value, accumulatorId, metricType) - } catch { - case _: java.lang.NoSuchMethodError => - dbRuntimeReflection.constructSQLPlanMetric( - accNameRef.value, accumulatorId, metricType) - } - } - - private def constructCluster(id: Long, - name: String, - desc: String, - nodes: mutable.ArrayBuffer[SparkPlanGraphNode], - metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { - try { - new SparkPlanGraphCluster(id, name, desc, nodes, metrics) - } catch { - case _: java.lang.NoSuchMethodError => - dbRuntimeReflection.constructCluster(id, name, desc, nodes, metrics) - } - } - private def constructEdge(fromId: Long, toId: Long): SparkPlanGraphEdge = { - try { - SparkPlanGraphEdge(fromId, toId) - } catch { - case _: java.lang.NoSuchMethodError => - dbRuntimeReflection.constructEdge(fromId, toId) - } + GraphReflectionAPIHelper.api.get.constructSQLPlanMetric(accNameRef.value, accumulatorId, + metricType) } /** @@ -249,15 +79,10 @@ object ToolsPlanGraph { */ def apply(planInfo: SparkPlanInfo): SparkPlanGraph = { try { - val nodeIdGenerator = new AtomicLong(0) - val nodes = mutable.ArrayBuffer[SparkPlanGraphNode]() - val edges = mutable.ArrayBuffer[SparkPlanGraphEdge]() - val exchanges = mutable.HashMap[SparkPlanInfo, SparkPlanGraphNode]() - buildSparkPlanGraphNode(planInfo, nodeIdGenerator, nodes, edges, null, null, exchanges) - new SparkPlanGraph(nodes, edges) + graphBuilder(planInfo) } catch { // If the construction of the graph fails due to NoSuchMethod, then it is possible the - // runtime is DB and we fallback to the loaded runtime jars + // runtime is not compatible with the constructors and we fallback to the loaded runtime jars case _ : java.lang.NoSuchMethodError | _ : java.lang.IllegalArgumentException => SparkPlanGraph(planInfo) } @@ -285,7 +110,7 @@ object ToolsPlanGraph { constructSQLPlanMetric(metric.name, metric.accumulatorId, metric.metricType) } - val cluster = constructCluster( + val cluster = api.constructCluster( nodeIdGenerator.getAndIncrement(), planInfo.nodeName, planInfo.simpleString, @@ -302,7 +127,7 @@ object ToolsPlanGraph { if (exchanges.contains(planInfo.children.head)) { // Point to the re-used exchange val node = exchanges(planInfo.children.head) - edges += constructEdge(node.id, parent.id) + edges += api.constructEdge(node.id, parent.id) } else { buildSparkPlanGraphNode( planInfo.children.head, nodeIdGenerator, nodes, edges, parent, null, exchanges) @@ -316,7 +141,7 @@ object ToolsPlanGraph { case "Subquery" | "SubqueryBroadcast" if exchanges.contains(planInfo) => // Point to the re-used subquery val node = exchanges(planInfo) - edges += constructEdge(node.id, parent.id) + edges += api.constructEdge(node.id, parent.id) case "ReusedSubquery" => // Re-used subquery might appear before the original subquery, so skip this node and let // the previous `case` make sure the re-used and the original point to the same node. @@ -325,12 +150,12 @@ object ToolsPlanGraph { case "ReusedExchange" if exchanges.contains(planInfo.children.head) => // Point to the re-used exchange val node = exchanges(planInfo.children.head) - edges += constructEdge(node.id, parent.id) + edges += api.constructEdge(node.id, parent.id) case name => val metrics = planInfo.metrics.map { metric => constructSQLPlanMetric(metric.name, metric.accumulatorId, metric.metricType) } - val node = constructGraphNode(nodeIdGenerator.getAndIncrement(), + val node = api.constructNode(nodeIdGenerator.getAndIncrement(), planInfo.nodeName, planInfo.simpleString, metrics) if (subgraph == null) { nodes += node @@ -342,7 +167,7 @@ object ToolsPlanGraph { } if (parent != null) { - edges += constructEdge(node.id, parent.id) + edges += api.constructEdge(node.id, parent.id) } planInfo.children.foreach( buildSparkPlanGraphNode(_, nodeIdGenerator, nodes, edges, node, subgraph, exchanges)) diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/DefaultGraphReflectionAPI.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/DefaultGraphReflectionAPI.scala new file mode 100644 index 000000000..9f0a956e2 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/DefaultGraphReflectionAPI.scala @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs + +import scala.collection.mutable + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphEdge, SparkPlanGraphNode, SQLPlanMetric} + +/** + * Implementation that uses the SparkPlanGraphNode, SparkPlanGraphCluster, SparkPlanGraphEdge and + * SQLPlanMetric classes from the Spark codebase. This API is used to reflectively construct these + * classes at runtime. + */ +class DefaultGraphReflectionAPI extends GraphReflectionAPI { + def constructNode(id: Long, name: String, desc: String, + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { + new SparkPlanGraphNode(id, name, desc, metrics) + } + + def constructSQLPlanMetric(name: String, + accumulatorId: Long, + metricType: String): SQLPlanMetric = { + SQLPlanMetric(name, accumulatorId, metricType) + } + + def constructCluster(id: Long, + name: String, + desc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { + new SparkPlanGraphCluster(id, name, desc, nodes, metrics) + } + + def constructEdge(fromId: Long, toId: Long): SparkPlanGraphEdge = { + SparkPlanGraphEdge(fromId, toId) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPI.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPI.scala new file mode 100644 index 000000000..8e6324707 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPI.scala @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs + +import scala.collection.mutable +import scala.reflect.runtime.universe.runtimeMirror + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphEdge, SparkPlanGraphNode, SQLPlanMetric} + +/** + * API to define methods used to construct SparkPlanGraphNode, SparkPlanGraphCluster, + * SparkPlanGraphEdge and SQLPlanMetric objects at runtime. + */ +trait GraphReflectionAPI { + protected val mirror = runtimeMirror(getClass.getClassLoader) + + def constructNode(id: Long, name: String, desc: String, + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode + + def constructSQLPlanMetric(name: String, + accumulatorId: Long, + metricType: String): SQLPlanMetric + + def constructCluster(id: Long, + name: String, + desc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster + + def constructEdge(fromId: Long, toId: Long): SparkPlanGraphEdge +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPIHelper.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPIHelper.scala new file mode 100644 index 000000000..871e9630f --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionAPIHelper.scala @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.rapids.tool.util.stubs.bd.BDGraphReflectionAPI +import org.apache.spark.sql.rapids.tool.util.stubs.db.DBGraphReflectionAPI + +/** + * Helper object to load the correct runtime GraphReflectionAPI implementation. + * This is used to improve performance by avoiding try-catch blocks on every single component. + */ +object GraphReflectionAPIHelper extends Logging { + /** + * Iterate through the available GraphReflectionAPI implementations and return the first one that + * can be used to construct a SparkPlan Graph Node. + * @return the first GraphReflectionAPI that is compatible with the current Spark runtime. + */ + private def loadRuntimeGraphAPI(): Option[GraphReflectionAPI] = { + // defines the available GraphReflectionAPI implementations + val allAPIS = Map( + "Default Graph API" -> new DefaultGraphReflectionAPI(), + "BD Graph API" -> BDGraphReflectionAPI(), + "DB Graph API" -> DBGraphReflectionAPI() + ) + // Finds the first compatible API by creating a dummy node. + val res = allAPIS.find { entry => + try { + // Create a dummy node and captures the exception if the API is not compatible + entry._2.constructNode(0, "node1", "descr", Seq.empty) + true + } catch { + case _: Throwable => false + } + } + // Log this information or show an error to be aware of incompatible runtimes. + if (res.isDefined) { + logInfo(s"Using runtime API [${res.get._1}] to Construct SparkPlan Graph") + } else { + logError("No runtime Graph API found. Falling to the spark runtime constructor") + } + res.map(_._2) + } + // caches the API to avoid re-creating it multiple times. + lazy val api: Option[GraphReflectionAPI] = loadRuntimeGraphAPI() +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionEntry.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionEntry.scala new file mode 100644 index 000000000..7681fb870 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/GraphReflectionEntry.scala @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs + +import scala.reflect.runtime.universe.{ClassSymbol, MethodSymbol, Mirror} + +/** + * A reflection entry for a class that can be instantiated at runtime. + * @param mirror The mirror to use for reflection (created once per classloader) + * @param className The name of the class to instantiate + * @param paramsSize The number of parameters to the constructor, if known. Some Spark releases + * like Databricks have different overrides that differ by count of arguments. + * @tparam T The class to instantiate + */ +class GraphReflectionEntry[T](mirror: Mirror, className: String, paramsSize: Option[Int] = None) { + // Get the class symbol + private val classSymbol = mirror.staticClass(className) + private val reflectiveClass = mirror.reflectClass(classSymbol) + // Get the constructor method symbol + val constr: MethodSymbol = createConstructor(classSymbol, paramsSize) + + // If the paramsCount is defined, we select the constructor that has parameters size equal to + // that value + private def createConstructor(symbol: ClassSymbol, paramsCount: Option[Int]): MethodSymbol = { + paramsCount match { + case None => + // return the primary constructor + symbol.primaryConstructor.asMethod + case Some(count) => + // return the constructor with given parameter size + val constructors = symbol.info.decls.filter(_.isConstructor) + .map(_.asMethod) + .filter(_.paramLists.flatten.size == count) + val constructor = constructors.headOption.getOrElse { + throw new IllegalArgumentException( + s"No constructor found with exactly $count parameters for class[$className]") + } + constructor + } + } + + def createInstanceFromList(args: List[_]): T = { + reflectiveClass + .reflectConstructor(constr)(args: _*) + .asInstanceOf[T] + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphClusterStub.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphClusterStub.scala new file mode 100644 index 000000000..30657a277 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphClusterStub.scala @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.bd + +import scala.collection.mutable +import scala.reflect.runtime.universe.Mirror + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphNode, SQLPlanMetric} +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection +import org.apache.spark.sql.rapids.tool.util.stubs.GraphReflectionEntry + +case class BDGraphClusterStub(m: Mirror) + extends GraphReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphCluster]( + m, "org.apache.spark.sql.execution.ui.SparkPlanGraphCluster") { + @ToolsReflection("BD-3.2.1", "Defines an extra argument planId: Int in the constructor") + def createInstance(id: Long, + name: String, + desc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { + createInstanceFromList(List(id, name, desc, nodes, metrics, 0)) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphNodeStub.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphNodeStub.scala new file mode 100644 index 000000000..dda259be8 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphNodeStub.scala @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.bd + +import scala.reflect.runtime.universe.Mirror + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphNode, SQLPlanMetric} +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection +import org.apache.spark.sql.rapids.tool.util.stubs.GraphReflectionEntry + +case class BDGraphNodeStub(m: Mirror) + extends GraphReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphNode]( + m, "org.apache.spark.sql.execution.ui.SparkPlanGraphNode") { + + @ToolsReflection("BD-3.2.1", "Defines an extra argument planId: Int in the constructor") + def createInstance(id: Long, name: String, desc: String, + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { + // Define argument values + createInstanceFromList(List(id, name, desc, metrics, 0)) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphReflectionAPI.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphReflectionAPI.scala new file mode 100644 index 000000000..f67a20c83 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/bd/BDGraphReflectionAPI.scala @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.bd + +import scala.collection.mutable + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphNode, SQLPlanMetric} +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection +import org.apache.spark.sql.rapids.tool.util.stubs.DefaultGraphReflectionAPI + +@ToolsReflection("BD-3.2.1", "Reflection to add extra arguments to the Node/Cluster constructors") +case class BDGraphReflectionAPI() extends DefaultGraphReflectionAPI { + private val nodeStub = BDGraphNodeStub(mirror) + private val clusterStub = BDGraphClusterStub(mirror) + + override def constructNode(id: Long, name: String, desc: String, + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { + nodeStub.createInstance(id, name, desc, metrics) + } + + override def constructCluster(id: Long, + name: String, + desc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { + clusterStub.createInstance(id, name, desc, nodes, metrics) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphClusterStub.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphClusterStub.scala new file mode 100644 index 000000000..162947d14 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphClusterStub.scala @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.db + +import scala.collection.mutable +import scala.reflect.runtime.universe.Mirror + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphNode, SQLPlanMetric} +import org.apache.spark.sql.rapids.tool.util.stubs.GraphReflectionEntry + +case class DBGraphClusterStub(m: Mirror) + extends GraphReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphCluster]( + m, "org.apache.spark.sql.execution.ui.SparkPlanGraphCluster") { + // DataBricks has different constructor of the sparkPlanGraphNode + // (final long id, final java.lang.String name, final java.lang.String desc, + // final ArrayBuffer nodes, + // final scala.collection.Seq metrics, + // final java.lang.String rddScopeId) + + // 10.4 is the same as other versions + // (final long id, final java.lang.String name, final java.lang.String desc, + // final ArrayBuffer nodes, + // final Seq metrics, + // final java.lang.String rddScopeId + def createInstance(id: Long, + name: String, + desc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { + val argValues = List(id, name, desc, nodes, metrics, "") + createInstanceFromList(argValues) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphEdgeStub.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphEdgeStub.scala new file mode 100644 index 000000000..4d36b4e95 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphEdgeStub.scala @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.db + +import scala.reflect.runtime.universe.Mirror + +import org.apache.spark.sql.execution.ui.SparkPlanGraphEdge +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection +import org.apache.spark.sql.rapids.tool.util.stubs.GraphReflectionEntry + +@ToolsReflection("DataBricks", + "All DB accept 2 parameters. We enforce that constructor by passing (2) to the parent class") +case class DBGraphEdgeStub(m: Mirror) + extends GraphReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphEdge]( + m, "org.apache.spark.sql.execution.ui.SparkPlanGraphEdge", Option(2)) { + // DataBricks has different constructor of the sparkPlanGraphEdge + // (final long fromId, final long toId, + // final scala.Option numOutputRowsId) + // + // for 10.4 only one constructor with two arguments + // final long fromId, final long toId) + + def createInstance(fromId: Long, toId: Long): SparkPlanGraphEdge = { + val argValues = List(fromId, toId) + createInstanceFromList(argValues) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphNodeStub.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphNodeStub.scala new file mode 100644 index 000000000..cba08bb25 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphNodeStub.scala @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.db + +import scala.reflect.runtime.universe.Mirror + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphNode, SQLPlanMetric} +import org.apache.spark.sql.rapids.tool.util.stubs.GraphReflectionEntry + +case class DBGraphNodeStub(m: Mirror) + extends GraphReflectionEntry[org.apache.spark.sql.execution.ui.SparkPlanGraphNode]( + m, "org.apache.spark.sql.execution.ui.SparkPlanGraphNode") { + // DataBricks has different constructor of the sparkPlanGraphNode + // [(long,java.lang.String,java.lang.String,scala.collection.Seq,java.lang.String, + // boolean,scala.Option,scala.Option)] and + // [final long id, final java.lang.String name, final java.lang.String desc, + // final scala.collection.Seq metrics, + // final java.lang.String rddScopeId, final boolean started, + // final scala.Option estRowCount) + + // For 10.4 --> only 1 constructor and has 6 arguments (not 7) + // (final long id, final java.lang.String name, final java.lang.String desc, + // final scala.collection.Seq metrics, + // final java.lang.String rddScopeId, final scala.Option estRowCount + + // DB10.4 has constructor with 6 arguments. + private val isDB104OrOlder: Boolean = constr.paramLists.flatten.size < 7 + + def createInstance(id: Long, name: String, desc: String, + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { + // Define argument values + val argValues = if (isDB104OrOlder) { + List(id, name, desc, metrics, "", None) + } else { + List(id, name, desc, metrics, "", false, None, None) + } + createInstanceFromList(argValues) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphReflectionAPI.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphReflectionAPI.scala new file mode 100644 index 000000000..42716d8d9 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphReflectionAPI.scala @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.db + +import scala.collection.mutable + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphEdge, SparkPlanGraphNode, SQLPlanMetric} +import org.apache.spark.sql.rapids.tool.annotation.ToolsReflection +import org.apache.spark.sql.rapids.tool.util.stubs.DefaultGraphReflectionAPI + +/** + * Implementation that uses the SparkPlanGraphNode, SparkPlanGraphCluster, SparkPlanGraphEdge and + * SQLPlanMetric classes from the DataBricks codeBase. + * This API is used to reflectively construct these classes at runtime. + */ +@ToolsReflection("DataBricks", "DB defines different constructors for SparkGraphComponents") +case class DBGraphReflectionAPI() extends DefaultGraphReflectionAPI { + private val nodeStub = DBGraphNodeStub(mirror) + private val clusterStub = DBGraphClusterStub(mirror) + private val edgeStub = DBGraphEdgeStub(mirror) + private val metricStub = DBGraphSQLMetricStub(mirror) + + override def constructNode(id: Long, name: String, desc: String, + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphNode = { + nodeStub.createInstance(id, name, desc, metrics) + } + + override def constructSQLPlanMetric(name: String, + accumulatorId: Long, + metricType: String): SQLPlanMetric = { + metricStub.createInstance(name, accumulatorId, metricType) + } + + override def constructCluster(id: Long, + name: String, + desc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]): SparkPlanGraphCluster = { + clusterStub.createInstance(id, name, desc, nodes, metrics) + } + + override def constructEdge(fromId: Long, toId: Long): SparkPlanGraphEdge = { + edgeStub.createInstance(fromId, toId) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphSQLMetricStub.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphSQLMetricStub.scala new file mode 100644 index 000000000..b2591ad7d --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/db/DBGraphSQLMetricStub.scala @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.stubs.db + +import scala.reflect.runtime.universe.Mirror + +import org.apache.spark.sql.execution.ui.SQLPlanMetric +import org.apache.spark.sql.rapids.tool.util.stubs.GraphReflectionEntry + +case class DBGraphSQLMetricStub(m: Mirror) + extends GraphReflectionEntry[org.apache.spark.sql.execution.ui.SQLPlanMetric]( + m, "org.apache.spark.sql.execution.ui.SQLPlanMetric") { + // DataBricks has different constructor of the sparkPlanGraphNode + //Array(final java.lang.String name, final long accumulatorId, + // final java.lang.String metricType, final boolean experimental) + + // for 10.4 it is only one constructor with 3 arguments. + // final java.lang.String name, final long accumulatorId, final java.lang.String metricType + private val isDB104OrOlder: Boolean = constr.paramLists.flatten.size < 4 + + def createInstance(name: String, + accumulatorId: Long, + metricType: String): SQLPlanMetric = { + val argValues = if (isDB104OrOlder) { + List(name, accumulatorId, metricType) + } else { + List(name, accumulatorId, metricType, false) + } + createInstanceFromList(argValues) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/package.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/package.scala new file mode 100644 index 000000000..5174763b0 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/stubs/package.scala @@ -0,0 +1,25 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util + +/** + * Stubs used for reflection at runtime to work with different Spark implementations. + * This will include stubs created for each Spark flavor when needed. + */ +package object stubs { + +} From eabb6c153ca53a6d5c9aa169c89446d426c62edc Mon Sep 17 00:00:00 2001 From: Cindy Jiang <47068112+cindyyuanjiang@users.noreply.github.com> Date: Wed, 9 Oct 2024 16:27:47 -0700 Subject: [PATCH 15/24] Remove total core second heuristic and filter apps only in top candidate view (#1376) * remove totalo core seconds from heuristic, only filter in stdout table Signed-off-by: cindyyuanjiang * filter totcal core sec only in top candidate view Signed-off-by: cindyyuanjiang * fix python style Signed-off-by: cindyyuanjiang * address review feedback Signed-off-by: cindyyuanjiang * rename variable Signed-off-by: cindyyuanjiang --------- Signed-off-by: cindyyuanjiang --- .../resources/qualification-conf.yaml | 6 +++--- .../tools/additional_heuristics.py | 17 +---------------- .../spark_rapids_tools/tools/top_candidates.py | 15 ++++++++++++++- 3 files changed, 18 insertions(+), 20 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml index 694696c7d..74c138a11 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -247,6 +247,9 @@ local: compactWidth: true timeUnits: 's' columnWidth: 14 + totalCoreSecCol: 'Total Core Seconds' + # This is total core seconds of an 8-core machine running for 24 hours + totalCoreSecThreshold: 691200 speedupCategories: speedupColumnName: 'Estimated GPU Speedup' categoryColumnName: 'Estimated GPU Speedup Category' @@ -279,9 +282,6 @@ local: - 'App ID' - 'Skip by Heuristics' - 'Reason' - totalCoreSecBased: - # This is total core seconds of an 8-core machine running for 24 hours - totalCoreSecThreshold: 691200 spillBased: stageAggMetrics: fileName: 'stage_level_aggregated_task_metrics.csv' diff --git a/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py b/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py index 18b207357..0396957e2 100644 --- a/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py +++ b/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py @@ -22,7 +22,6 @@ import pandas as pd from spark_rapids_pytools.common.prop_manager import JSONPropertiesContainer -from spark_rapids_pytools.common.sys_storage import FSUtil from spark_rapids_pytools.common.utilities import ToolLogging from spark_rapids_tools.tools.qualx.util import find_paths, RegexPattern from spark_rapids_tools.utils import Utilities @@ -50,7 +49,7 @@ def _get_all_heuristics_functions(self) -> list: """ Returns a list of heuristics functions to apply to each application. """ - return [self.heuristics_based_on_total_core_seconds, self.heuristics_based_on_spills] + return [self.heuristics_based_on_spills] def _apply_heuristics(self, app_ids: list) -> pd.DataFrame: """ @@ -93,20 +92,6 @@ def _apply_heuristics(self, app_ids: list) -> pd.DataFrame: return pd.DataFrame(result_arr, columns=self.props.get_value('resultCols')) - def heuristics_based_on_total_core_seconds(self, app_id_path: str) -> (bool, str): - """ - Apply heuristics based on total core seconds to determine if the app can be accelerated on GPU. - """ - # Load app total core seconds from qualification summary dataframe - app_id = FSUtil.get_resource_name(app_id_path) - app_qual_output = self.all_apps[self.all_apps['App ID'] == app_id] # type: ignore - total_core_seconds = app_qual_output['Total Core Seconds'].astype(int).iloc[0] - total_core_seconds_threshold = self.props.get_value('totalCoreSecBased', 'totalCoreSecThreshold') - if total_core_seconds <= total_core_seconds_threshold: - return True, f'Skipping due to total core seconds = {total_core_seconds} lower than ' + \ - f'{total_core_seconds_threshold}.' - return False, '' - def heuristics_based_on_spills(self, app_id_path: str) -> (bool, str): """ Apply heuristics based on spills to determine if the app can be accelerated on GPU. diff --git a/user_tools/src/spark_rapids_tools/tools/top_candidates.py b/user_tools/src/spark_rapids_tools/tools/top_candidates.py index 850508c96..1572aa538 100644 --- a/user_tools/src/spark_rapids_tools/tools/top_candidates.py +++ b/user_tools/src/spark_rapids_tools/tools/top_candidates.py @@ -59,7 +59,7 @@ def get_filtered_apps_count(self) -> int: def _filter_apps(self) -> None: """ - Filters the applications based on the eligible categories (Small/Medium/Large) + Filters the applications based on the eligible categories (Small/Medium/Large) and total core seconds threshold. """ if not self._has_tools_processed_apps(): self.filtered_apps = pd.DataFrame(columns=self.tools_processed_apps.columns) @@ -67,7 +67,18 @@ def _filter_apps(self) -> None: category_col_name = self.props.get('categoryColumnName') eligible_categories = self.props.get('eligibleCategories') + + # Note: `filter_condition` can be a combination of multiple filters + # Filter based on eligible categories (Small/Medium/Large) filter_condition = self.tools_processed_apps[category_col_name].isin(eligible_categories) + + # Filter based on total core seconds threshold + total_core_sec_col = self.props.get('totalCoreSecCol') + total_core_sec_threshold = self.props.get('totalCoreSecThreshold') + total_core_sec_condition = self.tools_processed_apps[total_core_sec_col] > total_core_sec_threshold + filter_condition = filter_condition & total_core_sec_condition + + # Apply all filter conditions to get top candidate view apps self.filtered_apps = self.tools_processed_apps[filter_condition] def _generate_output_table(self, output_df: pd.DataFrame) -> str: @@ -75,6 +86,8 @@ def _generate_output_table(self, output_df: pd.DataFrame) -> str: Generic method to generate the output table from the output dataframe """ res_df = self._generate_output_table_internal(output_df) + if res_df.empty: + return '' # squeeze the header titles if enabled squeeze_header_enabled = self.props.get('summaryReport', {}).get('compactWidth', False) header_width = self.props.get('summaryReport', {}).get('columnWidth', 0) if squeeze_header_enabled else 0 From 98a862fc839a46b9a09ba6644a11e98bcf81e84e Mon Sep 17 00:00:00 2001 From: Thomas Graves Date: Tue, 15 Oct 2024 08:06:55 -0500 Subject: [PATCH 16/24] Cluster information should handle dynamic allocation and nodes being removed and added (#1369) * Properly handle multi-tenant clusters and dynamic allocation in the cluster information. * Fix threading issues with storing cluster information in Platform. Make platform per app. --------- Signed-off-by: Thomas Graves --- core/README.md | 11 ++ .../nvidia/spark/rapids/tool/Platform.scala | 130 ++++++++++++------ .../rapids/tool/profiling/AutoTuner.scala | 6 +- .../rapids/tool/profiling/Profiler.scala | 5 +- .../qualification/PluginTypeChecker.scala | 2 +- .../tool/qualification/QualOutputWriter.scala | 44 +++++- .../tool/qualification/Qualification.scala | 17 ++- .../qualification/QualificationMain.scala | 14 +- .../RunningQualificationApp.scala | 7 +- .../tool/tuning/QualificationAutoTuner.scala | 7 +- .../rapids/tool/tuning/TunerContext.scala | 14 +- .../spark/sql/rapids/tool/AppBase.scala | 26 ++++ .../sql/rapids/tool/ClassWarehouse.scala | 9 ++ .../sql/rapids/tool/EventProcessorBase.scala | 3 + .../qualification/QualificationAppInfo.scala | 50 +++---- .../eventlog_4nodes_8cores_dynamic_alloc | 42 ------ .../eventlog_4nodes_8cores_dynamic_alloc.zstd | Bin 0 -> 114796 bytes .../tool/planparser/SqlPlanParserSuite.scala | 5 +- .../tool/profiling/AutoTunerSuite.scala | 3 +- .../qualification/QualificationSuite.scala | 47 ++++--- .../features/installation_checks.feature | 4 +- 21 files changed, 277 insertions(+), 169 deletions(-) delete mode 100644 core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc create mode 100644 core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc.zstd diff --git a/core/README.md b/core/README.md index 0a3bae83b..3e17fa78f 100644 --- a/core/README.md +++ b/core/README.md @@ -32,6 +32,17 @@ mvn -Dbuildver=351 clean package Run `mvn help:all-profiles` to list supported Spark versions. +### Running tests + +The unit tests are run by default when building unless they are explicitly skipped by specifying `-DskipTests`. + +To run an individual test the `-Dsuites` option can be specified: + +```bash +mvn test -Dsuites=com.nvidia.spark.rapids.tool.qualification.QualificationSuite +``` + + ### Setting up an Integrated Development Environment Before proceeding with importing spark-rapids-tools into IDEA or switching to a different Spark release diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/Platform.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/Platform.scala index 2504fc326..6bf62a537 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/Platform.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/Platform.scala @@ -51,6 +51,8 @@ object PlatformNames { ) } +case class DynamicAllocationInfo(enabled: Boolean, max: String, min: String, initial: String) + // resource information and name of the CSP instance types, or for onprem its // the executor information since we can't recommend node types case class InstanceInfo(cores: Int, memoryMB: Long, name: String, numGpus: Int) @@ -211,7 +213,6 @@ abstract class Platform(var gpuDevice: Option[GpuDevice], */ def getRetainedSystemProps: Set[String] = Set.empty - def getExecutorHeapMemoryMB(sparkProperties: Map[String, String]): Long = { // Potentially enhance this to handle if no config then check the executor // added or resource profile added events for the heap size @@ -288,28 +289,20 @@ abstract class Platform(var gpuDevice: Option[GpuDevice], math.max(1, gpus) } - // Get the number of nodes that were used in the source cluster. - def getSourceNumNodes(): Int = { - if (clusterProperties.isDefined) { - Math.max(1, clusterProperties.get.system.numWorkers) - } else if (clusterInfoFromEventLog.isDefined) { - clusterInfoFromEventLog.get.numWorkerNodes - } else { - 1 - } - } - // we want to keep the number of executors used between runs the same def getNumExecutorInstances(sparkProperties: Map[String, String]): Int = { + val dynamicAllocationEnabled = Platform.isDynamicAllocationEnabled(sparkProperties) val execInstFromProps = sparkProperties.get("spark.executor.instances") + // If the cluster properties were specified make sure to use those and not + // the eventlog inference. This is broken in my mind but is backwards compatible, + // or maybe use number gpus per node as an improvement. if (clusterProperties.isDefined) { val numWorkers = Math.max(1, clusterProperties.get.system.numWorkers) this.numGpus * numWorkers - } else if (execInstFromProps.isDefined) { + } else if (execInstFromProps.isDefined && !dynamicAllocationEnabled) { execInstFromProps.get.toInt } else if (clusterInfoFromEventLog.isDefined) { - val clusterInfo = clusterInfoFromEventLog.get - clusterInfo.numWorkerNodes * clusterInfo.numExecsPerNode + clusterInfoFromEventLog.get.numExecutors } else { // not sure so don't set it 0 @@ -338,24 +331,28 @@ abstract class Platform(var gpuDevice: Option[GpuDevice], def createClusterInfo(coresPerExecutor: Int, numExecsPerNode: Int, + numExecs: Int, numWorkerNodes: Int, sparkProperties: Map[String, String], systemProperties: Map[String, String]): ExistingClusterInfo = { val driverHost = sparkProperties.get("spark.driver.host") val executorHeapMem = getExecutorHeapMemoryMB(sparkProperties) - ExistingClusterInfo(platformName, coresPerExecutor, numExecsPerNode, numWorkerNodes, - executorHeapMem, driverHost = driverHost) + val dynamicAllocSettings = Platform.getDynamicAllocationSettings(sparkProperties) + ExistingClusterInfo(platformName, coresPerExecutor, numExecsPerNode, numExecs, numWorkerNodes, + executorHeapMem, dynamicAllocSettings.enabled, dynamicAllocSettings.max, + dynamicAllocSettings.min, dynamicAllocSettings.initial, driverHost = driverHost) } // set the cluster information for this platform based on what we found in the // eventlog def configureClusterInfoFromEventLog(coresPerExecutor: Int, execsPerNode: Int, + numExecs: Int, numExecutorNodes: Int, sparkProperties: Map[String, String], systemProperties: Map[String, String]): Unit = { clusterInfoFromEventLog = Some(createClusterInfo(coresPerExecutor, execsPerNode, - numExecutorNodes, sparkProperties, systemProperties)) + numExecs, numExecutorNodes, sparkProperties, systemProperties)) } override def toString: String = { @@ -383,17 +380,30 @@ abstract class Platform(var gpuDevice: Option[GpuDevice], */ def getGPUInstanceTypeRecommendation( sparkProperties: Map[String, String]): Option[RecommendedClusterInfo] = { - val initialNumExecInstances = getNumExecutorInstances(sparkProperties) + val vendor = clusterInfoFromEventLog.map(_.vendor).getOrElse("") + val numExecs = getNumExecutorInstances(sparkProperties) // If the cluster properties were specified make sure to use those and not // the eventlog inference. This is broken in my mind but is backwards compatible, // or maybe use number gpus per node as an improvement. + val origClusterNumExecsPerNode = clusterInfoFromEventLog.map(_.numExecsPerNode).getOrElse(1) val numExecsPerNode = if (clusterProperties.isEmpty) { - clusterInfoFromEventLog.map(_.numExecsPerNode).getOrElse(1) + // numExecsPerNode can be -1 if dynamic allocation so just make it 1 for + // this set of calculations. However if we are on a CSP then we want to recommend + // the best size machine so use the number of GPUs as proxy to be the number of executors + // we could put on a node. + if (origClusterNumExecsPerNode == -1) { + maxGpusSupported + } else { + origClusterNumExecsPerNode + } } else { 1 } + // onprem yarn multi-tenant vs yarn static cluster (dataproc) for just that application + // should be handled automatically unless heterogeneous nodes val gpusToUse = Math.max(this.numGpus, Math.min(numExecsPerNode, maxGpusSupported)) + // update the global numGpus based on the instance type we are using this.numGpus = gpusToUse val nodeCores = if (clusterProperties.isDefined) { @@ -424,11 +434,6 @@ abstract class Platform(var gpuDevice: Option[GpuDevice], logWarning("cluster information from event log is missing, executor cores set to 0!") 0 } - val numExecsPerNode = if (clusterInfoFromEventLog.isDefined) { - clusterInfoFromEventLog.get.numExecsPerNode - } else { - 1 - } val nodeCoresToUse = execCores * gpusToUse val nodeMemMB = getMemoryMBPerNode(sparkProperties) // It's possible if a cpu run was used, it could run with multiple executors, but @@ -454,29 +459,37 @@ abstract class Platform(var gpuDevice: Option[GpuDevice], } else { instanceInfoOpt } - val numExistingNodes = getSourceNumNodes - // check if instance type supports that number of gpus, if not we add extra executors - val (numExecs, numNodes) = if (finalInstanceInfo.get.numGpus >= numExecsPerNode) { - // TODO - really if instance has more GPUs we should calculate the other way to - // recommend less nodes but leave that open for now - (initialNumExecInstances, numExistingNodes) - } else { - // just flatten to use 1 but we should really see if multiples - val numGpusLeft = numExecsPerNode / finalInstanceInfo.get.numGpus - (initialNumExecInstances, numExistingNodes * numGpusLeft) - } + // note this is going over as for instance if you have 4 gpus per node but only need + // 10 executors, this would tell you to allocate enough to fit 12. + val numNodes = math.ceil(numExecs.toDouble / finalInstanceInfo.get.numGpus).toInt val coresPerExec = if (finalInstanceInfo.isDefined) { - finalInstanceInfo.get.cores / finalInstanceInfo.get.numGpus + // We may not be able to match instance type up exactly, this means the number of + // cores per executor could come out to be more then the original application. + // For now we want the cores per executor to stay the same as original app so if + // that is set, use it first. + if (clusterInfoFromEventLog.isDefined) { + clusterInfoFromEventLog.get.coresPerExecutor + } else { + finalInstanceInfo.get.cores / finalInstanceInfo.get.numGpus + } } else { 1 } + val finalNumNodes = if (vendor == PlatformNames.ONPREM) { + // if its onprem we really have no idea of the size of the cluster + -1 + } else { + numNodes + } if (numExecs > 0) { - val vendor = clusterInfoFromEventLog.map(_.vendor).getOrElse("") val instanceName = finalInstanceInfo.map(_.name).getOrElse("") val numGpus = finalInstanceInfo.map(_.numGpus).getOrElse(1) + val dynamicAllocSettings = Platform.getDynamicAllocationSettings(sparkProperties) // Num of executors per node is the number of GPUs recommendedClusterInfo = Some(RecommendedClusterInfo(vendor, coresPerExec, - numNodes, numGpus, numExecs, gpuDevice = getGpuOrDefault.toString, + finalNumNodes, numGpus, numExecs, gpuDevice = getGpuOrDefault.toString, + dynamicAllocSettings.enabled, dynamicAllocSettings.max, + dynamicAllocSettings.min, dynamicAllocSettings.initial, workerNodeType = Some(instanceName))) recommendedNodeInstanceInfo = finalInstanceInfo recommendedClusterInfo @@ -505,6 +518,7 @@ abstract class DatabricksPlatform(gpuDevice: Option[GpuDevice], override def createClusterInfo(coresPerExecutor: Int, numExecsPerNode: Int, + numExecs: Int, numWorkerNodes: Int, sparkProperties: Map[String, String], systemProperties: Map[String, String]): ExistingClusterInfo = { @@ -514,8 +528,11 @@ abstract class DatabricksPlatform(gpuDevice: Option[GpuDevice], val driverHost = sparkProperties.get("spark.driver.host") val clusterName = sparkProperties.get(DatabricksParseHelper.PROP_TAG_CLUSTER_NAME_KEY) val executorHeapMem = getExecutorHeapMemoryMB(sparkProperties) - ExistingClusterInfo(platformName, coresPerExecutor, numExecsPerNode, numWorkerNodes, - executorHeapMem, driverNodeType, workerNodeType, driverHost, clusterId, clusterName) + val dynamicAllocSettings = Platform.getDynamicAllocationSettings(sparkProperties) + ExistingClusterInfo(platformName, coresPerExecutor, numExecsPerNode, numExecs, numWorkerNodes, + executorHeapMem, dynamicAllocSettings.enabled, dynamicAllocSettings.max, + dynamicAllocSettings.min, dynamicAllocSettings.initial, driverNodeType, + workerNodeType, driverHost, clusterId, clusterName) } } @@ -629,14 +646,18 @@ class EmrPlatform(gpuDevice: Option[GpuDevice], override def createClusterInfo(coresPerExecutor: Int, numExecsPerNode: Int, + numExecs: Int, numWorkerNodes: Int, sparkProperties: Map[String, String], systemProperties: Map[String, String]): ExistingClusterInfo = { val clusterId = systemProperties.get("EMR_CLUSTER_ID") val driverHost = sparkProperties.get("spark.driver.host") val executorHeapMem = getExecutorHeapMemoryMB(sparkProperties) - ExistingClusterInfo(platformName, coresPerExecutor, numExecsPerNode, numWorkerNodes, - executorHeapMem, clusterId = clusterId, driverHost = driverHost) + val dynamicAllocSettings = Platform.getDynamicAllocationSettings(sparkProperties) + ExistingClusterInfo(platformName, coresPerExecutor, numExecsPerNode, numExecs, + numWorkerNodes, executorHeapMem, dynamicAllocSettings.enabled, dynamicAllocSettings.max, + dynamicAllocSettings.min, dynamicAllocSettings.initial, clusterId = clusterId, + driverHost = driverHost) } override def getInstanceByResources( @@ -672,6 +693,29 @@ class OnPremPlatform(gpuDevice: Option[GpuDevice], override def maxGpusSupported: Int = 1 } +object Platform { + def isDynamicAllocationEnabled(sparkProperties: Map[String, String]): Boolean = { + sparkProperties.getOrElse("spark.dynamicAllocation.enabled", "false").toBoolean + } + + def getDynamicAllocationSettings(sparkProperties: Map[String, String]): DynamicAllocationInfo = { + val dynamicAllocationEnabled = isDynamicAllocationEnabled(sparkProperties) + if (dynamicAllocationEnabled) { + val dynamicAllocationMax = sparkProperties. + getOrElse("spark.dynamicAllocation.maxExecutors", Int.MaxValue.toString) + val dynamicAllocationMin = sparkProperties. + getOrElse("spark.dynamicAllocation.minExecutors", "0") + val dynamicAllocationInit = sparkProperties. + getOrElse("spark.dynamicAllocation.initialExecutors", sparkProperties. + getOrElse("spark.executor.instances", dynamicAllocationMin)) + DynamicAllocationInfo(dynamicAllocationEnabled, dynamicAllocationMax, + dynamicAllocationMin, dynamicAllocationInit) + } else { + DynamicAllocationInfo(dynamicAllocationEnabled, "N/A", "N/A", "N/A") + } + } +} + /** * Factory for creating instances of different platforms. * This factory supports various platforms and provides methods for creating diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/AutoTuner.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/AutoTuner.scala index 3cbef68b2..6d1a2c165 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/AutoTuner.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/AutoTuner.scala @@ -1393,14 +1393,12 @@ object AutoTuner extends Logging { } def buildAutoTuner( - workerInfoFilePath: String, singleAppProvider: AppSummaryInfoBaseProvider, - platform: Platform = PlatformFactory.createInstance(clusterProperties = None), + platform: Platform, driverInfoProvider: DriverLogInfoProvider = BaseDriverLogInfoProvider.noneDriverLog ): AutoTuner = { try { - val clusterPropsOpt = loadClusterProps(workerInfoFilePath) - val autoT = new AutoTuner(clusterPropsOpt.getOrElse(new ClusterProperties()), + val autoT = new AutoTuner(platform.clusterProperties.getOrElse(new ClusterProperties()), singleAppProvider, platform, driverInfoProvider) autoT } catch { diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/Profiler.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/Profiler.scala index a3e6139b0..bad5524e3 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/Profiler.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/Profiler.scala @@ -409,10 +409,9 @@ class Profiler(hadoopConf: Configuration, appArgs: ProfileArgs, enablePB: Boolea if (appInfo.isDefined && appInfo.get.appInfo.head.pluginEnabled) { val appInfoProvider = AppSummaryInfoBaseProvider.fromAppInfo(appInfo) val workerInfoPath = appArgs.workerInfo.getOrElse(AutoTuner.DEFAULT_WORKER_INFO_PATH) - val platform = appArgs.platform() val clusterPropsOpt = loadClusterProps(workerInfoPath) - val autoTuner: AutoTuner = AutoTuner.buildAutoTuner(workerInfoPath, appInfoProvider, - PlatformFactory.createInstance(platform, clusterPropsOpt), driverInfoProvider) + val autoTuner: AutoTuner = AutoTuner.buildAutoTuner(appInfoProvider, + PlatformFactory.createInstance(appArgs.platform(), clusterPropsOpt), driverInfoProvider) // The autotuner allows skipping some properties, // e.g., getRecommendedProperties(Some(Seq("spark.executor.instances"))) skips the diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/PluginTypeChecker.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/PluginTypeChecker.scala index d1958f572..f04ddcac7 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/PluginTypeChecker.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/PluginTypeChecker.scala @@ -83,7 +83,7 @@ object OpSuppLevel extends Enumeration { * by the plugin which lists the formats and types supported. * The class also supports a custom speedup factor file as input. */ -class PluginTypeChecker(val platform: Platform = PlatformFactory.createInstance(), +class PluginTypeChecker(platform: Platform = PlatformFactory.createInstance(), speedupFactorFile: Option[String] = None) extends Logging { private val NONE = "None" diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualOutputWriter.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualOutputWriter.scala index 4d1bf70fb..14035e1cc 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualOutputWriter.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualOutputWriter.scala @@ -451,12 +451,23 @@ object QualOutputWriter { val STATUS_REPORT_APP_ID = "AppID" val STATUS_REPORT_DESC_STR = "Description" val VENDOR = "Vendor" + val RECOMMENDED_VENDOR = "Recommended Vendor" val DRIVER_HOST = "Driver Host" val CLUSTER_ID_STR = "Cluster Id" // Different from ClusterId used for Databricks Tags val CLUSTER_NAME = "Cluster Name" val RECOMMENDED_NUM_GPUS = "Recommended Num GPUs Per Node" val RECOMMENDED_GPU_DEVICE = "Recommended GPU Device" val NUM_EXECS_PER_NODE = "Num Executors Per Node" + val NUM_EXECS = "Num Executors" + val EXECUTOR_HEAP_MEMORY = "Executor Heap Memory" + val DYN_ALLOC_ENABLED = "Dynamic Allocation Enabled" + val DYN_ALLOC_MAX = "Dynamic Allocation Max Executors" + val DYN_ALLOC_MIN = "Dynamic Allocation Min Executors" + val DYN_ALLOC_INIT = "Dynamic Allocation Initial Executors" + val RECOMMENDED_DYN_ALLOC_ENABLED = "Recommended Dynamic Allocation Enabled" + val RECOMMENDED_DYN_ALLOC_MAX = "Recommended Dynamic Allocation Max Executors" + val RECOMMENDED_DYN_ALLOC_MIN = "Recommended Dynamic Allocation Min Executors" + val RECOMMENDED_DYN_ALLOC_INIT = "Recommended Dynamic Allocation Initial Executors" val RECOMMENDED_NUM_EXECS = "Recommended Num Executors" val NUM_WORKER_NODES = "Num Worker Nodes" val RECOMMENDED_NUM_WORKER_NODES = "Recommended Num Worker Nodes" @@ -800,10 +811,13 @@ object QualOutputWriter { private def getClusterInfoHeaderStrings: mutable.LinkedHashMap[String, Int] = { val headersAndFields = Seq( - APP_ID_STR, APP_NAME_STR, VENDOR, DRIVER_HOST, CLUSTER_ID_STR, CLUSTER_NAME, - WORKER_NODE_TYPE, DRIVER_NODE_TYPE, NUM_WORKER_NODES, NUM_EXECS_PER_NODE, CORES_PER_EXEC, - RECOMMENDED_WORKER_NODE_TYPE, RECOMMENDED_NUM_EXECS, RECOMMENDED_NUM_WORKER_NODES, - RECOMMENDED_CORES_PER_EXEC, RECOMMENDED_GPU_DEVICE, RECOMMENDED_NUM_GPUS).map { + APP_ID_STR, APP_NAME_STR, STATUS_REPORT_PATH_STR, VENDOR, DRIVER_HOST, CLUSTER_ID_STR, + CLUSTER_NAME, WORKER_NODE_TYPE, DRIVER_NODE_TYPE, NUM_WORKER_NODES, NUM_EXECS_PER_NODE, + NUM_EXECS, EXECUTOR_HEAP_MEMORY, DYN_ALLOC_ENABLED, DYN_ALLOC_MAX, DYN_ALLOC_MIN, + DYN_ALLOC_INIT, CORES_PER_EXEC, RECOMMENDED_WORKER_NODE_TYPE, RECOMMENDED_NUM_EXECS, + RECOMMENDED_NUM_WORKER_NODES, RECOMMENDED_CORES_PER_EXEC, RECOMMENDED_GPU_DEVICE, + RECOMMENDED_NUM_GPUS, RECOMMENDED_VENDOR, RECOMMENDED_DYN_ALLOC_ENABLED, + RECOMMENDED_DYN_ALLOC_MAX, RECOMMENDED_DYN_ALLOC_MIN, RECOMMENDED_DYN_ALLOC_INIT).map { key => (key, key.length) } mutable.LinkedHashMap(headersAndFields: _*) @@ -827,6 +841,7 @@ object QualOutputWriter { val data = ListBuffer( refactorCSVFuncWithOption(Some(sumInfo.clusterSummary.appId), APP_ID_STR), refactorCSVFuncWithOption(Some(sumInfo.clusterSummary.appName), APP_NAME_STR), + refactorCSVFuncWithOption(sumInfo.clusterSummary.eventLogPath, STATUS_REPORT_PATH_STR), refactorCSVFuncWithOption(clusterInfo.map(_.vendor), VENDOR), refactorCSVFuncWithOption(clusterInfo.flatMap(_.driverHost), DRIVER_HOST), refactorCSVFuncWithOption(clusterInfo.flatMap(_.clusterId), CLUSTER_ID_STR), @@ -835,6 +850,15 @@ object QualOutputWriter { refactorCSVFuncWithOption(clusterInfo.flatMap(_.driverNodeType), DRIVER_NODE_TYPE), refactorCSVFuncWithOption(clusterInfo.map(_.numWorkerNodes.toString), NUM_WORKER_NODES), refactorCSVFuncWithOption(clusterInfo.map(_.numExecsPerNode.toString), NUM_EXECS_PER_NODE), + refactorCSVFuncWithOption(clusterInfo.map(_.numExecutors.toString), NUM_EXECS), + refactorCSVFuncWithOption(clusterInfo.map(_.executorHeapMemory.toString), + EXECUTOR_HEAP_MEMORY), + refactorCSVFuncWithOption(clusterInfo.map(_.dynamicAllocationEnabled.toString), + DYN_ALLOC_ENABLED), + refactorCSVFuncWithOption(clusterInfo.map(_.dynamicAllocationMaxExecutors), DYN_ALLOC_MAX), + refactorCSVFuncWithOption(clusterInfo.map(_.dynamicAllocationMinExecutors), DYN_ALLOC_MIN), + refactorCSVFuncWithOption(clusterInfo.map(_.dynamicAllocationInitialExecutors), + DYN_ALLOC_INIT), refactorCSVFuncWithOption(clusterInfo.map(_.coresPerExecutor.toString), CORES_PER_EXEC), refactorCSVFuncWithOption(recClusterInfo.flatMap(_.workerNodeType), RECOMMENDED_WORKER_NODE_TYPE), @@ -845,8 +869,16 @@ object QualOutputWriter { refactorCSVFuncWithOption(recClusterInfo.map(_.coresPerExecutor.toString), RECOMMENDED_CORES_PER_EXEC), refactorCSVFuncWithOption(recClusterInfo.map(_.gpuDevice), RECOMMENDED_GPU_DEVICE), - refactorCSVFuncWithOption(recClusterInfo.map(_.numGpus.toString), RECOMMENDED_NUM_GPUS) - + refactorCSVFuncWithOption(recClusterInfo.map(_.numGpus.toString), RECOMMENDED_NUM_GPUS), + refactorCSVFuncWithOption(recClusterInfo.map(_.vendor), RECOMMENDED_VENDOR), + refactorCSVFuncWithOption(recClusterInfo.map(_.dynamicAllocationEnabled.toString), + RECOMMENDED_DYN_ALLOC_ENABLED), + refactorCSVFuncWithOption(recClusterInfo.map(_.dynamicAllocationMaxExecutors), + RECOMMENDED_DYN_ALLOC_MAX), + refactorCSVFuncWithOption(recClusterInfo.map(_.dynamicAllocationMinExecutors), + RECOMMENDED_DYN_ALLOC_MIN), + refactorCSVFuncWithOption(recClusterInfo.map(_.dynamicAllocationInitialExecutors), + RECOMMENDED_DYN_ALLOC_INIT) ) constructOutputRow(data, delimiter, prettyPrint) :: Nil } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/Qualification.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/Qualification.scala index 6889116ea..5e35b32c0 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/Qualification.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/Qualification.scala @@ -20,7 +20,8 @@ import java.util.concurrent.{ConcurrentHashMap, TimeUnit} import scala.collection.JavaConverters._ -import com.nvidia.spark.rapids.tool.{EventLogInfo, FailedEventLog, ToolBase} +import com.nvidia.spark.rapids.tool.{EventLogInfo, FailedEventLog, PlatformFactory, ToolBase} +import com.nvidia.spark.rapids.tool.profiling.AutoTuner import com.nvidia.spark.rapids.tool.qualification.QualOutputWriter.DEFAULT_JOB_FREQUENCY import com.nvidia.spark.rapids.tool.tuning.TunerContext import com.nvidia.spark.rapids.tool.views.QualRawReportGenerator @@ -37,7 +38,7 @@ class Qualification(outputPath: String, numRows: Int, hadoopConf: Configuration, printStdout: Boolean, enablePB: Boolean, reportSqlLevel: Boolean, maxSQLDescLength: Int, mlOpsEnabled:Boolean, penalizeTransitions: Boolean, tunerContext: Option[TunerContext], - clusterReport: Boolean) extends ToolBase(timeout) { + clusterReport: Boolean, platformArg: String, workerInfoPath: String) extends ToolBase(timeout) { override val simpleName: String = "qualTool" override val outputDir = s"$outputPath/rapids_4_spark_qualification_output" @@ -144,8 +145,14 @@ class Qualification(outputPath: String, numRows: Int, hadoopConf: Configuration, case _ => // No action needed for other cases } val startTime = System.currentTimeMillis() + // we need a platform per application because it's storing cluster information which could + // vary between applications, especially when using dynamic allocation + val platform = { + val clusterPropsOpt = AutoTuner.loadClusterProps(workerInfoPath) + PlatformFactory.createInstance(platformArg, clusterPropsOpt) + } val appResult = QualificationAppInfo.createApp(path, hadoopConf, pluginTypeChecker, - reportSqlLevel, mlOpsEnabled, penalizeTransitions) + reportSqlLevel, mlOpsEnabled, penalizeTransitions, platform) val qualAppResult = appResult match { case Left(FailureApp("skipped", errorMessage)) => // Case to be skipped, e.g. encountered Databricks Photon event log @@ -174,14 +181,14 @@ class Qualification(outputPath: String, numRows: Int, hadoopConf: Configuration, // Note that we call the autotuner anyway without checking the aggregate results // because the Autotuner can still make some recommendations based on the information // enclosed by the QualificationInfo object - tuner.tuneApplication(app, qualSumInfo, appIndex, dsInfo) + tuner.tuneApplication(app, qualSumInfo, appIndex, dsInfo, platform) } } if (qualSumInfo.isDefined) { // add the recommend cluster info into the summary val tempSummary = qualSumInfo.get val newClusterSummary = tempSummary.clusterSummary.copy( - recommendedClusterInfo = pluginTypeChecker.platform.recommendedClusterInfo) + recommendedClusterInfo = platform.recommendedClusterInfo) AppSubscriber.withSafeValidAttempt(app.appId, app.attemptId) { () => val newQualSummary = tempSummary.copy(clusterSummary = newClusterSummary) // check if the app is already in the map diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualificationMain.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualificationMain.scala index 6ed646787..743f95de8 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualificationMain.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/QualificationMain.scala @@ -68,7 +68,13 @@ object QualificationMain extends Logging { val recursiveSearchEnabled = !appArgs.noRecursion() val hadoopConf = RapidsToolsConfUtil.newHadoopConf - val platform = try { + // Note we need the platform to get the correct files we use in the PluginTypeChecker. + // Here we create one dummy global one just to get those files as the platform should + // be the same for all applications but then later we create a per application one for + // properly tracking the cluster info and recommended cluster info per application. + // This platform instance should not be used for anything other then referencing the + // files for this particular Platform. + val referencePlatform = try { val clusterPropsOpt = loadClusterProps(appArgs.workerInfo()) PlatformFactory.createInstance(appArgs.platform(), clusterPropsOpt) } catch { @@ -79,7 +85,7 @@ object QualificationMain extends Logging { val pluginTypeChecker = try { new PluginTypeChecker( - platform, + referencePlatform, appArgs.speedupFactorFile.toOption) } catch { case ie: IllegalStateException => @@ -109,14 +115,14 @@ object QualificationMain extends Logging { } // create the AutoTuner context object val tunerContext = if (appArgs.autoTuner()) { - TunerContext(platform, appArgs.workerInfo(), outputDirectory, Option(hadoopConf)) + TunerContext(outputDirectory, Option(hadoopConf)) } else { None } val qual = new Qualification(outputDirectory, numOutputRows, hadoopConf, timeout, nThreads, order, pluginTypeChecker, reportReadSchema, printStdout, enablePB, reportSqlLevel, maxSQLDescLength, mlOpsEnabled, penalizeTransitions, - tunerContext, appArgs.clusterReport()) + tunerContext, appArgs.clusterReport(), appArgs.platform(), appArgs.workerInfo()) val res = qual.qualifyApps(filteredLogs) (0, res) } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/RunningQualificationApp.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/RunningQualificationApp.scala index 1c1e4e030..d18d584e3 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/RunningQualificationApp.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/qualification/RunningQualificationApp.scala @@ -16,6 +16,7 @@ package com.nvidia.spark.rapids.tool.qualification +import com.nvidia.spark.rapids.tool.{Platform, PlatformFactory} import com.nvidia.spark.rapids.tool.analysis.AppSQLPlanAnalyzer import com.nvidia.spark.rapids.tool.planparser.SQLPlanParser import com.nvidia.spark.rapids.tool.qualification.QualOutputWriter.SQL_DESC_STR @@ -71,8 +72,10 @@ import org.apache.spark.sql.rapids.tool.qualification._ */ class RunningQualificationApp( perSqlOnly: Boolean = false, - pluginTypeChecker: PluginTypeChecker = new PluginTypeChecker()) - extends QualificationAppInfo(None, None, pluginTypeChecker, reportSqlLevel=false, perSqlOnly) { + pluginTypeChecker: PluginTypeChecker = new PluginTypeChecker(), + platform: Platform = PlatformFactory.createInstance()) + extends QualificationAppInfo(None, None, pluginTypeChecker, reportSqlLevel=false, + perSqlOnly, platform = platform) { // note we don't use the per sql reporting providing by QualificationAppInfo so we always // send down false for it diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/QualificationAutoTuner.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/QualificationAutoTuner.scala index 0bd6568fe..4344f0a5d 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/QualificationAutoTuner.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/QualificationAutoTuner.scala @@ -18,7 +18,7 @@ package com.nvidia.spark.rapids.tool.tuning import scala.util.{Failure, Success, Try} -import com.nvidia.spark.rapids.tool.{AppSummaryInfoBaseProvider, ToolTextFileWriter} +import com.nvidia.spark.rapids.tool.{AppSummaryInfoBaseProvider, Platform, ToolTextFileWriter} import com.nvidia.spark.rapids.tool.analysis.AggRawMetricsResult import com.nvidia.spark.rapids.tool.profiling.{AutoTuner, DataSourceProfileResult, Profiler} import org.apache.hadoop.conf.Configuration @@ -64,9 +64,8 @@ class QualificationAutoTuner(val appInfoProvider: QualAppSummaryInfoProvider, } } - def runAutoTuner(): TuningResult = { - val autoTuner: AutoTuner = AutoTuner.buildAutoTuner( - tunerContext.workerInfoPath, appInfoProvider, tunerContext.platform) + def runAutoTuner(platform: Platform): TuningResult = { + val autoTuner: AutoTuner = AutoTuner.buildAutoTuner(appInfoProvider, platform) val (recommendations, comments) = autoTuner.getRecommendedProperties(showOnlyUpdatedProps = filterByUpdatedPropsEnabled) // Combine the GPU recommendations with all others. diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/TunerContext.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/TunerContext.scala index 55e72d5c7..c795d0434 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/TunerContext.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/tuning/TunerContext.scala @@ -36,14 +36,10 @@ case class TuningResult( /** * Container which holds metadata and arguments specific to the execution of the AutoTuner. * TODO: we need to use the same class in constructing the AutoTuner in the Profiling tools. - * @param platform object representing the host platform on which the application was executed. - * @param workerInfoPath the path of the GPU workers * @param outputRootDir the output directory to dump the recommendation/comments. * @param hadoopConf optional configuration to access the remote storage. */ case class TunerContext ( - platform: Platform, - workerInfoPath: String, outputRootDir: String, hadoopConf: Configuration) extends Logging { @@ -55,12 +51,13 @@ case class TunerContext ( appInfo: QualificationAppInfo, appAggStats: Option[QualificationSummaryInfo], appIndex: Int = 1, - dsInfo: Seq[DataSourceProfileResult]): Option[TuningResult] = { + dsInfo: Seq[DataSourceProfileResult], + platform: Platform): Option[TuningResult] = { val rawAggMetrics = QualSparkMetricsAnalyzer.getAggRawMetrics(appInfo, appIndex) QualificationAutoTuner(appInfo, appAggStats, this, rawAggMetrics, dsInfo).collect { case qualTuner => Try { - qualTuner.runAutoTuner() + qualTuner.runAutoTuner(platform) } match { case Success(r) => r case Failure(e) => @@ -72,13 +69,12 @@ case class TunerContext ( } object TunerContext extends Logging { - def apply(platform: Platform, - workerInfoPath: String, + def apply( outputRootDir: String, hadoopConf: Option[Configuration] = None): Option[TunerContext] = { Try { val hConf = hadoopConf.getOrElse(RapidsToolsConfUtil.newHadoopConf()) - TunerContext(platform, workerInfoPath, outputRootDir, hConf) + TunerContext(outputRootDir, hConf) } match { case Success(c) => Some(c) case Failure(e) => diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala index 787d6c4ec..e3313b832 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/AppBase.scala @@ -56,6 +56,13 @@ abstract class AppBase( lazy val attemptId: Int = appMetaData.map(_.attemptId).getOrElse(1) + // This is to keep track of the high water mark for maximum number of executors + // active at any point in time. + // Dynamic allocation and failures could change the number of + // executors over time so we are tracking what was the maximum used. + var maxNumExecutorsRunning = 0 + // high water mark for maximum number of nodes in use at any point in time + var maxNumNodesRunning = 0 // Store map of executorId to executor info val executorIdToInfo = new HashMap[String, ExecutorInfoClass]() // resourceProfile id to resource profile info @@ -198,6 +205,25 @@ abstract class AppBase( }) } + // this is to keep track of the high water mark for number of executors + // active at anyone instant in time + def updateMaxExecutors(): Unit = { + val numActiveExecutors = executorIdToInfo.values.filter(_.isActive).size + if (numActiveExecutors > maxNumExecutorsRunning) { + maxNumExecutorsRunning = numActiveExecutors + } + } + + // this is to keep track of the high water mark for number of nodes + // active at anyone instant in time + def updateMaxNodes(): Unit = { + // make this a set to make it dedup nodes + val numActiveNodes = executorIdToInfo.values.filter(_.isActive).map(_.host).toSet.size + if (numActiveNodes > maxNumNodesRunning) { + maxNumNodesRunning = numActiveNodes + } + } + def getOrCreateStage(info: StageInfo): StageModel = { val stage = stageManager.addStageInfo(info) stage diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/ClassWarehouse.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/ClassWarehouse.scala index e6451862f..b4dc0bc43 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/ClassWarehouse.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/ClassWarehouse.scala @@ -55,8 +55,13 @@ case class ExistingClusterInfo( vendor: String, coresPerExecutor: Int, numExecsPerNode: Int, + numExecutors: Int, // note that with dynamic allocation this is high water mark numWorkerNodes: Int, executorHeapMemory: Long, + dynamicAllocationEnabled: Boolean, + dynamicAllocationMaxExecutors: String, + dynamicAllocationMinExecutors: String, + dynamicAllocationInitialExecutors: String, driverNodeType: Option[String] = None, workerNodeType: Option[String] = None, driverHost: Option[String] = None, @@ -70,6 +75,10 @@ case class RecommendedClusterInfo( numGpus: Int, numExecutors: Int, gpuDevice: String, + dynamicAllocationEnabled: Boolean, + dynamicAllocationMaxExecutors: String, + dynamicAllocationMinExecutors: String, + dynamicAllocationInitialExecutors: String, driverNodeType: Option[String] = None, workerNodeType: Option[String] = None) extends ClusterInfo { // The number of executors per node is the same as the number of GPUs diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala index 4620a640d..b4dc8ce3f 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/EventProcessorBase.scala @@ -331,6 +331,9 @@ abstract class EventProcessorBase[T <: AppBase](app: T) extends SparkListener wi val rpId = event.executorInfo.resourceProfileId exec.resources = event.executorInfo.resourcesInfo exec.resourceProfileId = rpId + // make sure updateMaxExecutorsIfNeeded is called after added executor information + app.updateMaxExecutors() + app.updateMaxNodes() } override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = { diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala index d0bce04f2..2b01f4fef 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.rapids.tool.qualification import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.collection.mutable -import com.nvidia.spark.rapids.tool.EventLogInfo +import com.nvidia.spark.rapids.tool.{EventLogInfo, Platform} import com.nvidia.spark.rapids.tool.planparser.{ExecInfo, PlanInfo, SQLPlanParser} import com.nvidia.spark.rapids.tool.qualification._ import com.nvidia.spark.rapids.tool.qualification.QualOutputWriter.DEFAULT_JOB_FREQUENCY @@ -39,7 +39,8 @@ class QualificationAppInfo( reportSqlLevel: Boolean, val perSqlOnly: Boolean = false, mlOpsEnabled: Boolean = false, - penalizeTransitions: Boolean = true) + penalizeTransitions: Boolean = true, + platform: Platform) extends AppBase(eventLogInfo, hadoopConf) with Logging { var lastJobEndTime: Option[Long] = None @@ -66,7 +67,7 @@ class QualificationAppInfo( * any additional properties based on platform. */ override def getRetainedSystemProps: Set[String] = - super.getRetainedSystemProps ++ pluginTypeChecker.platform.getRetainedSystemProps + super.getRetainedSystemProps ++ platform.getRetainedSystemProps /** * Get the event listener the qualification tool uses to process Spark events. @@ -631,7 +632,7 @@ class QualificationAppInfo( mlFuncReportInfo.mlWallClockDur, unSupportedExecs, unSupportedExprs, allClusterTagsMap) val clusterSummary = ClusterSummary(info.appName, appId, - eventLogInfo.map(_.eventLog.toString), pluginTypeChecker.platform.clusterInfoFromEventLog, + eventLogInfo.map(_.eventLog.toString), platform.clusterInfoFromEventLog, None) QualificationSummaryInfo(info.appName, appId, problems, @@ -848,33 +849,33 @@ class QualificationAppInfo( * and extracts executor and driver instance types (databricks only) */ override def buildClusterInfo: Unit = { - // TODO: Handle dynamic allocation when determining the number of nodes. - sparkProperties.get("spark.dynamicAllocation.enabled").foreach { value => - if (value.toBoolean) { - logWarning(s"Application $appId: Dynamic allocation is not supported. " + - s"Cluster information may be inaccurate.") - } - } // try to figure out number of executors per node based on the executor info // Group by host name, find max executors per host val execsPerNodeList = executorIdToInfo.values.groupBy(_.host).mapValues(_.size).values - val numExecsPerNode = execsPerNodeList.reduceOption(_ max _).getOrElse(0) - val activeExecInfo = executorIdToInfo.values.collect { - case execInfo if execInfo.isActive => (execInfo.host, execInfo.totalCores) + // if we have different number of execs per node, then we blank it out to indicate + // not applicable (like when dynamic allocation is on in multi-tenant cluster) + // Since with dynamic allocation you could end up with more executors on a node then it + // has slots, just always set numExecsPerNode to -1 when its enabled. + val dynamicAllocEnabled = Platform.isDynamicAllocationEnabled(sparkProperties) + val numExecsPerNode = if (!dynamicAllocEnabled && execsPerNodeList.size > 0 && + execsPerNodeList.forall(_ == execsPerNodeList.head)) { + execsPerNodeList.head + } else { + -1 } - if (activeExecInfo.nonEmpty) { - val (activeHosts, coresPerExecutor) = activeExecInfo.unzip - if (coresPerExecutor.toSet.size != 1) { + val execCoreCounts = executorIdToInfo.values.map(_.totalCores) + if (execCoreCounts.size > 0) { + if (execCoreCounts.toSet.size != 1) { logWarning(s"Application $appId: Cluster with variable executor cores detected. " + s"Using maximum value.") } // Create cluster information based on platform type - pluginTypeChecker.platform.configureClusterInfoFromEventLog(coresPerExecutor.max, - numExecsPerNode, activeHosts.toSet.size, sparkProperties, systemProperties) + platform.configureClusterInfoFromEventLog(execCoreCounts.max, + numExecsPerNode, maxNumExecutorsRunning, maxNumNodesRunning, + sparkProperties, systemProperties) } else { - // if no executors do we want to qualify at all? maybe not, else we could look at - // properties like spark.executor.cores - logWarning("Active executor info is empty so can't build existing cluster information!") + logWarning("Could not determine if any executors were allocated or the number of cores " + + "used per executor. Can't build existing cluster information!") } } @@ -1096,10 +1097,11 @@ object QualificationAppInfo extends Logging { pluginTypeChecker: PluginTypeChecker, reportSqlLevel: Boolean, mlOpsEnabled: Boolean, - penalizeTransitions: Boolean): Either[FailureApp, QualificationAppInfo] = { + penalizeTransitions: Boolean, + platform: Platform): Either[FailureApp, QualificationAppInfo] = { try { val app = new QualificationAppInfo(Some(path), Some(hadoopConf), pluginTypeChecker, - reportSqlLevel, false, mlOpsEnabled, penalizeTransitions) + reportSqlLevel, false, mlOpsEnabled, penalizeTransitions, platform) if (!app.isAppMetaDefined) { throw IncorrectAppStatusException() } diff --git a/core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc b/core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc deleted file mode 100644 index 08f85c3b4..000000000 --- a/core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc +++ /dev/null @@ -1,42 +0,0 @@ -{"Event":"SparkListenerLogStart","Spark Version":"3.1.3"} -{"Event":"SparkListenerResourceProfileAdded","Resource Profile Id":0,"Executor Resource Requests":{"cores":{"Resource Name":"cores","Amount":8,"Discovery Script":"","Vendor":""},"memory":{"Resource Name":"memory","Amount":26742,"Discovery Script":"","Vendor":""},"offHeap":{"Resource Name":"offHeap","Amount":0,"Discovery Script":"","Vendor":""}},"Task Resource Requests":{"cpus":{"Resource Name":"cpus","Amount":1.0}}} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"driver","Host":"test-cpu-cluster-m","Port":43705},"Maximum Memory":8401295769,"Timestamp":1718390704685,"Maximum Onheap Memory":8401295769,"Maximum Offheap Memory":0} -{"Event":"SparkListenerEnvironmentUpdate","JVM Information":{"Java Home":"/usr/lib/jvm/java-8-openjdk-amd64/jre","Java Version":"1.8.0_312 (Private Build)","Scala Version":"version 2.12.10"},"Spark Properties":{"spark.driver.host":"test-cpu-cluster-m","spark.eventLog.enabled":"true","spark.driver.port":"41345","spark.repl.class.uri":"spark://test-cpu-cluster-m:41345/classes","spark.jars":"","spark.repl.class.outputDir":"/tmp/spark-7f3a4b49-f48c-4e53-8ea3-a16861c74338/repl-01d6fffe-ba55-49e7-afdb-bbf08996d7f1","spark.app.name":"Spark shell","spark.scheduler.mode":"FIFO","spark.submit.pyFiles":"","spark.ui.showConsoleProgress":"true","spark.app.startTime":"1651187224195","spark.executor.id":"driver","spark.submit.deployMode":"client","spark.master":"local[*]","spark.home":"/home/user1/spark-3.1.1/spark-3.1.1-bin-hadoop3.2","spark.eventLog.dir":"/home/user1/new_eventlogs","spark.sql.catalogImplementation":"hive","spark.app.id":"local-1651187225439"},"Hadoop 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Cores":8,"Log Urls":{"stdout":"http://test-host-w2:8042/node/containerlogs/container_1718382712696_0008_01_000003/test_user/stdout?start=-4096","stderr":"http://test-host-w2:8042/node/containerlogs/container_1718382712696_0008_01_000003/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w2:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w2","CONTAINER_ID":"container_1718382712696_0008_01_000003"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerExecutorAdded","Timestamp":1718390707572,"Executor ID":"2","Executor Info":{"Host":"test-host-w3","Total Cores":8,"Log Urls":{"stdout":"http://test-host-w3:8042/node/containerlogs/container_1718382712696_0008_01_000002/test_user/stdout?start=-4096","stderr":"http://test-host-w3:8042/node/containerlogs/container_1718382712696_0008_01_000002/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w3:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w3","CONTAINER_ID":"container_1718382712696_0008_01_000002"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerExecutorAdded","Timestamp":1718390707616,"Executor ID":"1","Executor Info":{"Host":"test-host-w1","Total Cores":8,"Log Urls":{"stdout":"http://test-host-w1:8042/node/containerlogs/container_1718382712696_0008_01_000001/test_user/stdout?start=-4096","stderr":"http://test-host-w1:8042/node/containerlogs/container_1718382712696_0008_01_000001/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w1:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w1","CONTAINER_ID":"container_1718382712696_0008_01_000001"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"2","Host":"test-host-w3","Port":46169},"Maximum Memory":14766676377,"Timestamp":1718390707665,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"3","Host":"test-host-w2","Port":36449},"Maximum Memory":14766676377,"Timestamp":1718390707665,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"1","Host":"test-host-w1","Port":32957},"Maximum Memory":14766676377,"Timestamp":1718390707704,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerExecutorAdded","Timestamp":1718390708103,"Executor ID":"8","Executor Info":{"Host":"test-host-w0","Total Cores":8,"Log Urls":{"stdout":"http://test-host-w0:8042/node/containerlogs/container_1718382712696_0008_01_000008/test_user/stdout?start=-4096","stderr":"http://test-host-w0:8042/node/containerlogs/container_1718382712696_0008_01_000008/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w0:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w0","CONTAINER_ID":"container_1718382712696_0008_01_000008"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"8","Host":"test-host-w0","Port":35523},"Maximum Memory":14766676377,"Timestamp":1718390708180,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerExecutorAdded","Timestamp":1718390708319,"Executor ID":"7","Executor Info":{"Host":"test-host-w2","Total Cores":8,"Log Urls":{"stdout":"http://test-host-w2:8042/node/containerlogs/container_1718382712696_0008_01_000007/test_user/stdout?start=-4096","stderr":"http://test-host-w2:8042/node/containerlogs/container_1718382712696_0008_01_000007/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w2:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w2","CONTAINER_ID":"container_1718382712696_0008_01_000007"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerExecutorAdded","Timestamp":1718390708326,"Executor ID":"6","Executor Info":{"Host":"test-host-w3","Total Cores":8,"Log Urls":{"stdout":"http://test-host-w3:8042/node/containerlogs/container_1718382712696_0008_01_000006/test_user/stdout?start=-4096","stderr":"http://test-host-w3:8042/node/containerlogs/container_1718382712696_0008_01_000006/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w3:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w3","CONTAINER_ID":"container_1718382712696_0008_01_000006"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerExecutorAdded","Timestamp":1718390708356,"Executor ID":"5","Executor Info":{"Host":"test-host-w1","Total Cores":8,"Log Urls":{"stdout":"http://test-host-w1:8042/node/containerlogs/container_1718382712696_0008_01_000005/test_user/stdout?start=-4096","stderr":"http://test-host-w1:8042/node/containerlogs/container_1718382712696_0008_01_000005/test_user/stderr?start=-4096"},"Attributes":{"NM_HTTP_ADDRESS":"test-host-w1:8042","YARN_LOG_SERVER_URL":"http://test-cpu-cluster-m:19888/jobhistory/logs","USER":"test_user","LOG_FILES":"stderr,stdout","NM_HTTP_PORT":"8042","CLUSTER_ID":"","NM_PORT":"8026","HTTP_SCHEME":"http://","NM_HOST":"test-host-w1","CONTAINER_ID":"container_1718382712696_0008_01_000005"},"Resources":{},"Resource Profile Id":0}} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"7","Host":"test-host-w2","Port":37615},"Maximum Memory":14766676377,"Timestamp":1718390708401,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"6","Host":"test-host-w3","Port":32883},"Maximum Memory":14766676377,"Timestamp":1718390708417,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"5","Host":"test-host-w1","Port":45663},"Maximum Memory":14766676377,"Timestamp":1718390708442,"Maximum Onheap Memory":14766676377,"Maximum Offheap Memory":0} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"2","Host":"test-host-w3","Port":46169},"Timestamp":1718390771091} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"5","Host":"test-host-w1","Port":45663},"Timestamp":1718390771092} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"1","Host":"test-host-w1","Port":32957},"Timestamp":1718390771092} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771093,"Executor ID":"2","Removed Reason":"Executor killed by driver."} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771094,"Executor ID":"5","Removed Reason":"Executor killed by driver."} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771095,"Executor ID":"1","Removed Reason":"Executor killed by driver."} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"3","Host":"test-host-w2","Port":36449},"Timestamp":1718390771095} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"7","Host":"test-host-w2","Port":37615},"Timestamp":1718390771096} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"4","Host":"test-host-w0","Port":33361},"Timestamp":1718390771096} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771096,"Executor ID":"3","Removed Reason":"Executor killed by driver."} -{"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"8","Host":"test-host-w0","Port":35523},"Timestamp":1718390771096} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771097,"Executor ID":"8","Removed Reason":"Executor killed by driver."} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771098,"Executor ID":"4","Removed Reason":"Executor killed by driver."} -{"Event":"SparkListenerExecutorRemoved","Timestamp":1718390771098,"Executor ID":"7","Removed Reason":"Executor killed by driver."} -{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":0,"description":"csv at :24","details":"org.apache.spark.sql.DataFrameWriter.csv(DataFrameWriter.scala:979)\n$line26.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:24)\n$line26.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:28)\n$line26.$read$$iw$$iw$$iw$$iw$$iw$$iw.(:30)\n$line26.$read$$iw$$iw$$iw$$iw$$iw.(:32)\n$line26.$read$$iw$$iw$$iw$$iw.(:34)\n$line26.$read$$iw$$iw$$iw.(:36)\n$line26.$read$$iw$$iw.(:38)\n$line26.$read$$iw.(:40)\n$line26.$read.(:42)\n$line26.$read$.(:46)\n$line26.$read$.()\n$line26.$eval$.$print$lzycompute(:7)\n$line26.$eval$.$print(:6)\n$line26.$eval.$print()\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\nscala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:747)","physicalPlanDescription":"== Physical Plan ==\nExecute InsertIntoHadoopFsRelationCommand (16)\n+- AdaptiveSparkPlan (15)\n +- Sort (14)\n +- Exchange (13)\n +- HashAggregate (12)\n +- Exchange (11)\n +- HashAggregate (10)\n +- Project (9)\n +- BroadcastHashJoin Inner BuildRight (8)\n :- Project (5)\n : +- BroadcastHashJoin Inner BuildRight (4)\n : :- LocalTableScan (1)\n : +- BroadcastExchange (3)\n : +- LocalTableScan (2)\n +- BroadcastExchange (7)\n +- LocalTableScan (6)\n\n\n(1) LocalTableScan\nOutput [3]: [id#8, group_id#10, category_id#11]\nArguments: [id#8, group_id#10, category_id#11]\n\n(2) LocalTableScan\nOutput [2]: [id#22, value#23]\nArguments: [id#22, value#23]\n\n(3) BroadcastExchange\nInput [2]: [id#22, value#23]\nArguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#43]\n\n(4) BroadcastHashJoin\nLeft keys [1]: [id#8]\nRight keys [1]: [id#22]\nJoin condition: None\n\n(5) Project\nOutput [4]: [id#8, group_id#10, category_id#11, value#23]\nInput [5]: [id#8, group_id#10, category_id#11, id#22, value#23]\n\n(6) LocalTableScan\nOutput [2]: [id#34, detail_id#36]\nArguments: [id#34, detail_id#36]\n\n(7) BroadcastExchange\nInput [2]: [id#34, detail_id#36]\nArguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#47]\n\n(8) BroadcastHashJoin\nLeft keys [1]: [id#8]\nRight keys [1]: [id#34]\nJoin condition: None\n\n(9) Project\nOutput [4]: [group_id#10, category_id#11, value#23, detail_id#36]\nInput [6]: [id#8, group_id#10, category_id#11, value#23, id#34, detail_id#36]\n\n(10) HashAggregate\nInput [4]: [group_id#10, category_id#11, value#23, detail_id#36]\nKeys [3]: [group_id#10, category_id#11, detail_id#36]\nFunctions [5]: [partial_sum(cast(value#23 as bigint)), partial_avg(cast(value#23 as bigint)), partial_max(value#23), partial_min(value#23), partial_count(1)]\nAggregate Attributes [6]: [sum#89L, sum#90, count#91L, max#92, min#93, count#94L]\nResults [9]: [group_id#10, category_id#11, detail_id#36, sum#95L, sum#96, count#97L, max#98, min#99, count#100L]\n\n(11) Exchange\nInput [9]: [group_id#10, category_id#11, detail_id#36, sum#95L, sum#96, count#97L, max#98, min#99, count#100L]\nArguments: hashpartitioning(group_id#10, category_id#11, detail_id#36, 200), ENSURE_REQUIREMENTS, [id=#52]\n\n(12) HashAggregate\nInput [9]: [group_id#10, category_id#11, detail_id#36, sum#95L, sum#96, count#97L, max#98, min#99, count#100L]\nKeys [3]: [group_id#10, category_id#11, detail_id#36]\nFunctions [5]: [sum(cast(value#23 as bigint)), avg(cast(value#23 as bigint)), max(value#23), min(value#23), count(1)]\nAggregate Attributes [5]: [sum(cast(value#23 as bigint))#62L, avg(cast(value#23 as bigint))#64, max(value#23)#66, min(value#23)#68, count(1)#70L]\nResults [8]: [group_id#10, category_id#11, detail_id#36, sum(cast(value#23 as bigint))#62L AS total_value#63L, avg(cast(value#23 as bigint))#64 AS avg_value#65, max(value#23)#66 AS max_value#67, min(value#23)#68 AS min_value#69, count(1)#70L AS count#72L]\n\n(13) Exchange\nInput [8]: [group_id#10, category_id#11, detail_id#36, total_value#63L, avg_value#65, max_value#67, min_value#69, count#72L]\nArguments: rangepartitioning(total_value#63L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#55]\n\n(14) Sort\nInput [8]: [group_id#10, category_id#11, detail_id#36, total_value#63L, avg_value#65, max_value#67, min_value#69, count#72L]\nArguments: [total_value#63L DESC NULLS LAST], true, 0\n\n(15) AdaptiveSparkPlan\nOutput [8]: [group_id#10, category_id#11, detail_id#36, total_value#63L, avg_value#65, max_value#67, min_value#69, count#72L]\nArguments: isFinalPlan=false\n\n(16) Execute InsertIntoHadoopFsRelationCommand\nInput [8]: [group_id#10, category_id#11, detail_id#36, total_value#63L, avg_value#65, max_value#67, min_value#69, count#72L]\nArguments: hdfs://test-cpu-cluster-m/tmp/complex_large_job_output, false, CSV, [header=true, path=/tmp/complex_large_job_output], ErrorIfExists, [group_id, category_id, detail_id, total_value, avg_value, max_value, min_value, count]\n\n","sparkPlanInfo":{"nodeName":"Execute InsertIntoHadoopFsRelationCommand","simpleString":"Execute InsertIntoHadoopFsRelationCommand hdfs://test-cpu-cluster-m/tmp/complex_large_job_output, false, CSV, [header=true, path=/tmp/complex_large_job_output], ErrorIfExists, [group_id, category_id, detail_id, total_value, avg_value, max_value, min_value, count]","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Sort","simpleString":"Sort [total_value#63L DESC NULLS LAST], true, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange rangepartitioning(total_value#63L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#55]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[group_id#10, category_id#11, detail_id#36], functions=[sum(cast(value#23 as bigint)), avg(cast(value#23 as bigint)), max(value#23), min(value#23), count(1)])","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(group_id#10, category_id#11, detail_id#36, 200), ENSURE_REQUIREMENTS, [id=#52]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[group_id#10, category_id#11, detail_id#36], functions=[partial_sum(cast(value#23 as bigint)), partial_avg(cast(value#23 as bigint)), partial_max(value#23), partial_min(value#23), partial_count(1)])","children":[{"nodeName":"Project","simpleString":"Project [group_id#10, category_id#11, value#23, detail_id#36]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [id#8], [id#34], Inner, BuildRight, false","children":[{"nodeName":"Project","simpleString":"Project [id#8, group_id#10, category_id#11, value#23]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [id#8], [id#22], Inner, BuildRight, false","children":[{"nodeName":"LocalTableScan","simpleString":"LocalTableScan [id#8, group_id#10, category_id#11]","children":[],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":41,"metricType":"sum"}]},{"nodeName":"BroadcastExchange","simpleString":"BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#43]","children":[{"nodeName":"LocalTableScan","simpleString":"LocalTableScan [id#22, value#23]","children":[],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":47,"metricType":"sum"}]}],"metadata":{},"metrics":[{"name":"time to broadcast","accumulatorId":46,"metricType":"timing"},{"name":"time to build","accumulatorId":45,"metricType":"timing"},{"name":"time to collect","accumulatorId":44,"metricType":"timing"},{"name":"number of output rows","accumulatorId":43,"metricType":"sum"},{"name":"data size","accumulatorId":42,"metricType":"size"}]}],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":40,"metricType":"sum"}]}],"metadata":{},"metrics":[]},{"nodeName":"BroadcastExchange","simpleString":"BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#47]","children":[{"nodeName":"LocalTableScan","simpleString":"LocalTableScan [id#34, detail_id#36]","children":[],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":53,"metricType":"sum"}]}],"metadata":{},"metrics":[{"name":"time to broadcast","accumulatorId":52,"metricType":"timing"},{"name":"time to build","accumulatorId":51,"metricType":"timing"},{"name":"time to collect","accumulatorId":50,"metricType":"timing"},{"name":"number of output rows","accumulatorId":49,"metricType":"sum"},{"name":"data size","accumulatorId":48,"metricType":"size"}]}],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":39,"metricType":"sum"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[{"name":"spill size","accumulatorId":36,"metricType":"size"},{"name":"time in aggregation build","accumulatorId":37,"metricType":"timing"},{"name":"peak memory","accumulatorId":35,"metricType":"size"},{"name":"number of output rows","accumulatorId":34,"metricType":"sum"},{"name":"avg hash probe bucket list iters","accumulatorId":38,"metricType":"average"}]}],"metadata":{},"metrics":[{"name":"shuffle records written","accumulatorId":9,"metricType":"sum"},{"name":"shuffle write time","accumulatorId":10,"metricType":"nsTiming"},{"name":"records read","accumulatorId":7,"metricType":"sum"},{"name":"local bytes read","accumulatorId":5,"metricType":"size"},{"name":"fetch wait time","accumulatorId":6,"metricType":"timing"},{"name":"remote bytes read","accumulatorId":3,"metricType":"size"},{"name":"local blocks read","accumulatorId":2,"metricType":"sum"},{"name":"remote blocks read","accumulatorId":1,"metricType":"sum"},{"name":"data size","accumulatorId":0,"metricType":"size"},{"name":"remote bytes read to disk","accumulatorId":4,"metricType":"size"},{"name":"shuffle bytes written","accumulatorId":8,"metricType":"size"}]}],"metadata":{},"metrics":[{"name":"spill size","accumulatorId":31,"metricType":"size"},{"name":"time in aggregation build","accumulatorId":32,"metricType":"timing"},{"name":"peak memory","accumulatorId":30,"metricType":"size"},{"name":"number of output rows","accumulatorId":29,"metricType":"sum"},{"name":"avg hash probe bucket list iters","accumulatorId":33,"metricType":"average"}]}],"metadata":{},"metrics":[{"name":"shuffle records written","accumulatorId":20,"metricType":"sum"},{"name":"shuffle write time","accumulatorId":21,"metricType":"nsTiming"},{"name":"records read","accumulatorId":18,"metricType":"sum"},{"name":"local bytes read","accumulatorId":16,"metricType":"size"},{"name":"fetch wait time","accumulatorId":17,"metricType":"timing"},{"name":"remote bytes read","accumulatorId":14,"metricType":"size"},{"name":"local blocks read","accumulatorId":13,"metricType":"sum"},{"name":"remote blocks read","accumulatorId":12,"metricType":"sum"},{"name":"data size","accumulatorId":11,"metricType":"size"},{"name":"remote bytes read to disk","accumulatorId":15,"metricType":"size"},{"name":"shuffle bytes written","accumulatorId":19,"metricType":"size"}]}],"metadata":{},"metrics":[{"name":"sort time","accumulatorId":26,"metricType":"timing"},{"name":"peak memory","accumulatorId":27,"metricType":"size"},{"name":"spill size","accumulatorId":28,"metricType":"size"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[{"name":"number of written files","accumulatorId":22,"metricType":"sum"},{"name":"written output","accumulatorId":23,"metricType":"size"},{"name":"number of output rows","accumulatorId":24,"metricType":"sum"},{"name":"number of dynamic part","accumulatorId":25,"metricType":"sum"}]},"time":1718390856188} -{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":0,"time":1718390856380} -{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":1,"description":"show at :24","details":"org.apache.spark.sql.Dataset.show(Dataset.scala:794)\n$line34.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:24)\n$line34.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:28)\n$line34.$read$$iw$$iw$$iw$$iw$$iw$$iw.(:30)\n$line34.$read$$iw$$iw$$iw$$iw$$iw.(:32)\n$line34.$read$$iw$$iw$$iw$$iw.(:34)\n$line34.$read$$iw$$iw$$iw.(:36)\n$line34.$read$$iw$$iw.(:38)\n$line34.$read$$iw.(:40)\n$line34.$read.(:42)\n$line34.$read$.(:46)\n$line34.$read$.()\n$line34.$eval$.$print$lzycompute(:7)\n$line34.$eval$.$print(:6)\n$line34.$eval.$print()\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\nscala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:747)","physicalPlanDescription":"== Physical Plan ==\nLocalTableScan (1)\n\n\n(1) LocalTableScan\nOutput [2]: [Name#111, Value#112]\nArguments: [Name#111, Value#112]\n\n","sparkPlanInfo":{"nodeName":"LocalTableScan","simpleString":"LocalTableScan [Name#111, Value#112]","children":[],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":54,"metricType":"sum"}]},"time":1718392657665} -{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":1,"time":1718392657754} -{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":2,"description":"show at :24","details":"org.apache.spark.sql.Dataset.show(Dataset.scala:794)\n$line36.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:24)\n$line36.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:28)\n$line36.$read$$iw$$iw$$iw$$iw$$iw$$iw.(:30)\n$line36.$read$$iw$$iw$$iw$$iw$$iw.(:32)\n$line36.$read$$iw$$iw$$iw$$iw.(:34)\n$line36.$read$$iw$$iw$$iw.(:36)\n$line36.$read$$iw$$iw.(:38)\n$line36.$read$$iw.(:40)\n$line36.$read.(:42)\n$line36.$read$.(:46)\n$line36.$read$.()\n$line36.$eval$.$print$lzycompute(:7)\n$line36.$eval$.$print(:6)\n$line36.$eval.$print()\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\nscala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:747)","physicalPlanDescription":"== Physical Plan ==\nLocalTableScan (1)\n\n\n(1) LocalTableScan\nOutput [3]: [Name#125, Value#126, ValueSquared#127]\nArguments: [Name#125, Value#126, ValueSquared#127]\n\n","sparkPlanInfo":{"nodeName":"LocalTableScan","simpleString":"LocalTableScan [Name#125, Value#126, ValueSquared#127]","children":[],"metadata":{},"metrics":[{"name":"number of output rows","accumulatorId":55,"metricType":"sum"}]},"time":1718392658027} -{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":2,"time":1718392658069} -{"Event":"SparkListenerApplicationEnd","Timestamp":1718393600896} diff --git a/core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc.zstd b/core/src/test/resources/spark-events-qualification/cluster_information/eventlog_4nodes_8cores_dynamic_alloc.zstd new file mode 100644 index 0000000000000000000000000000000000000000..494fcb8819a24ac54f4cd899ccbd61b85186dc74 GIT binary patch literal 114796 zcmV)wK$O2IwJ-gISZgOB08DBFTJvItG@xjRj2;3CY*xY)QUdl{v%A!<1hd^TqyMU- 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Some(ExistingClusterInfo(vendor = PlatformNames.DEFAULT, coresPerExecutor = 8, - numExecsPerNode = 1, numWorkerNodes = 2, executorHeapMemory = 0L, - driverHost = Some("10.10.10.100"))), + numExecsPerNode = 1, numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, "N/A", "N/A", "N/A", driverHost = Some("10.10.10.100"))), "eventlog_3nodes_12cores_multiple_executors" -> // 3 nodes, each with 2 executors having 12 cores. Some(ExistingClusterInfo(vendor = PlatformNames.DEFAULT, coresPerExecutor = 12, - numExecsPerNode = 2, numWorkerNodes = 3, executorHeapMemory = 0L, - driverHost = Some("10.59.184.210"))), - // TODO: Currently we do not handle dynamic allocation while calculating number of nodes. For - // calculating nodes, we look at unique active hosts at the end of application. In this test - // case, the application used all 4 nodes initially (8 executors total), and then 7 executors were - // removed. In the end, only 1 executor was active on 1 node. This test case should be updated - // once we handle dynamic allocation. - "eventlog_4nodes_8cores_dynamic_alloc" -> // 4 nodes, each with 2 executor having 8 cores, with dynamic allocation. - Some(ExistingClusterInfo(vendor = PlatformNames.DEFAULT, coresPerExecutor = 8, - numExecsPerNode = 2, numWorkerNodes = 1, executorHeapMemory = 0L, - driverHost = Some("test-cpu-cluster-m"))), + numExecsPerNode = -1, numExecutors = 4, numWorkerNodes = 3, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, "N/A", "N/A", "N/A", driverHost = Some("10.59.184.210"))), + "eventlog_4nodes_8cores_dynamic_alloc.zstd" -> // using dynamic allocation, total of 5 nodes, each with max 7 + // executor running having 4 cores. At the end it had 1 active executor. + Some(ExistingClusterInfo(vendor = PlatformNames.DEFAULT, coresPerExecutor = 4, + numExecsPerNode = -1, numExecutors = 7, numWorkerNodes = 5, executorHeapMemory = 20480L, + dynamicAllocationEnabled = true, dynamicAllocationMaxExecutors = "2147483647", + dynamicAllocationMinExecutors = "0", dynamicAllocationInitialExecutors = "2", + driverHost = Some("10.10.6.9"))), "eventlog_3nodes_12cores_variable_cores" -> // 3 nodes with varying cores: 8, 12, and 8, each with 1 executor. Some(ExistingClusterInfo(vendor = PlatformNames.DEFAULT, coresPerExecutor = 12, - numExecsPerNode = 1, numWorkerNodes = 3, executorHeapMemory = 0L, - driverHost = Some("10.10.10.100"))), + numExecsPerNode = 1, numExecutors = 3, numWorkerNodes = 3, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, "N/A", "N/A", "N/A", driverHost = Some("10.10.10.100"))), "eventlog_3nodes_12cores_exec_removed" -> // 2 nodes, each with 1 executor having 12 cores, 1 executor removed. Some(ExistingClusterInfo(vendor = PlatformNames.DEFAULT, coresPerExecutor = 12, - numExecsPerNode = 1, numWorkerNodes = 2, executorHeapMemory = 0L, - driverHost = Some("10.10.10.100"))), + numExecsPerNode = 1, numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, "N/A", "N/A", "N/A", driverHost = Some("10.10.10.100"))), "eventlog_driver_only" -> None // Event log with driver only ) // scalastyle:on line.size.limit @@ -1632,8 +1630,11 @@ class QualificationSuite extends BaseTestSuite { ExistingClusterInfo(vendor = PlatformNames.DATABRICKS_AWS, coresPerExecutor = 8, numExecsPerNode = 1, + numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, + "N/A", "N/A", "N/A", driverNodeType = Some("m6gd.2xlarge"), workerNodeType = Some("m6gd.2xlarge"), driverHost = Some("10.10.10.100"), @@ -1643,8 +1644,11 @@ class QualificationSuite extends BaseTestSuite { ExistingClusterInfo(vendor = PlatformNames.DATABRICKS_AZURE, coresPerExecutor = 8, numExecsPerNode = 1, + numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, + "N/A", "N/A", "N/A", driverNodeType = Some("Standard_E8ds_v4"), workerNodeType = Some("Standard_E8ds_v4"), driverHost = Some("10.10.10.100"), @@ -1654,23 +1658,32 @@ class QualificationSuite extends BaseTestSuite { ExistingClusterInfo(vendor = PlatformNames.DATAPROC, coresPerExecutor = 8, numExecsPerNode = 1, + numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, + "N/A", "N/A", "N/A", driverHost = Some("dataproc-test-m.c.internal")), PlatformNames.EMR -> ExistingClusterInfo(vendor = PlatformNames.EMR, coresPerExecutor = 8, numExecsPerNode = 1, + numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, + "N/A", "N/A", "N/A", driverHost = Some("10.10.10.100"), clusterId = Some("j-123AB678XY321")), PlatformNames.ONPREM -> ExistingClusterInfo(vendor = PlatformNames.ONPREM, coresPerExecutor = 8, numExecsPerNode = 1, + numExecutors = 2, numWorkerNodes = 2, executorHeapMemory = 0L, + dynamicAllocationEnabled = false, + "N/A", "N/A", "N/A", driverHost = Some("10.10.10.100")) ) diff --git a/user_tools/tests/spark_rapids_tools_e2e/features/installation_checks.feature b/user_tools/tests/spark_rapids_tools_e2e/features/installation_checks.feature index 3edce082f..a05191d46 100644 --- a/user_tools/tests/spark_rapids_tools_e2e/features/installation_checks.feature +++ b/user_tools/tests/spark_rapids_tools_e2e/features/installation_checks.feature @@ -28,10 +28,10 @@ Feature: Tool Installation Checks Examples: | platform | cli | expected_stdout | - | dataproc | gcloud | 2 x n1-standard-16 (4 T4 each) | + | dataproc | gcloud | 3 x n1-standard-16 (4 T4 each) | | emr | aws | 10 x g5.xlarge | | databricks-aws | aws | 10 x g5.xlarge | - | databricks-azure | az | 2 x Standard_NC64as_T4_v3 | + | databricks-azure | az | 3 x Standard_NC64as_T4_v3 | @test_id_IC_0002 Scenario: Environment has missing java From b778edbfc22259d94f8e51464f6a3cc3a903adec Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Mon, 21 Oct 2024 08:57:59 -0500 Subject: [PATCH 17/24] Use StorageLib to download dependencies (#1383) * Use StorageLib to download dependencies Signed-off-by: Ahmed Hussein Fixes #1364, Contributes to #1359 - Use the CspPath and CspFs to manage dependencies - This allows more flexibility in specifying custom dependencies including local disk storage. - Remove Pricing catalog from python package * use requests library as a default download utility Signed-off-by: Ahmed Hussein * address code reviews Signed-off-by: Ahmed Hussein --------- Signed-off-by: Ahmed Hussein --- user_tools/pyproject.toml | 12 +- .../spark_rapids_pytools/rapids/rapids_job.py | 2 +- .../rapids/rapids_tool.py | 73 ++-- .../resources/databricks_aws-configs.json | 98 +++-- .../resources/databricks_azure-configs.json | 62 +-- .../resources/dataproc-configs.json | 68 ++-- .../resources/dataproc_gke-configs.json | 68 ++-- .../resources/dev/prepackage_mgr.py | 37 +- .../resources/emr-configs.json | 98 +++-- .../resources/onprem-configs.json | 55 ++- user_tools/src/spark_rapids_tools/enums.py | 36 +- .../spark_rapids_tools/storagelib/cspfs.py | 22 +- .../spark_rapids_tools/storagelib/csppath.py | 54 ++- .../storagelib/tools/fs_utils.py | 186 +++++++++ .../tools/qualx/qualx_main.py | 2 +- .../src/spark_rapids_tools/utils/net_utils.py | 366 ++++++++++++++++++ 16 files changed, 930 insertions(+), 309 deletions(-) create mode 100644 user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py create mode 100644 user_tools/src/spark_rapids_tools/utils/net_utils.py diff --git a/user_tools/pyproject.toml b/user_tools/pyproject.toml index 39d46ebfe..8decf7a82 100644 --- a/user_tools/pyproject.toml +++ b/user_tools/pyproject.toml @@ -24,7 +24,7 @@ dependencies = [ "numpy<=1.24.4", "chevron==0.14.0", "fastprogress==1.0.3", - "fastcore==1.5.29", + "fastcore==1.7.10", "fire>=0.5.0", "pandas==1.4.3", "pyYAML>=6.0", @@ -37,8 +37,8 @@ dependencies = [ "urllib3==1.26.19", "beautifulsoup4==4.11.2", "pygments==2.15.0", - # used to apply validator on objects and models - "pydantic==2.1.1", + # used to apply validator on objects and models. "2.9.2" contains from_json method. + "pydantic==2.9.2", # used to help pylint understand pydantic "pylint-pydantic==0.3.0", # used for common API to access remote filesystems like local/s3/gcs/hdfs @@ -76,7 +76,11 @@ version = {attr = "spark_rapids_pytools.__version__"} repository = "https://github.com/NVIDIA/spark-rapids-tools/tree/main" [project.optional-dependencies] test = [ - "tox", 'pytest', 'cli_test_helpers', 'behave' + "tox", 'pytest', 'cli_test_helpers', 'behave', + # use flak-8 plugin for pydantic + 'flake8-pydantic', + # use pylint specific version + 'pylint==3.2.7', ] qualx = [ "holoviews", diff --git a/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py b/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py index f095daa3f..a77a94b00 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py +++ b/user_tools/src/spark_rapids_pytools/rapids/rapids_job.py @@ -166,7 +166,7 @@ def _get_hadoop_classpath(self) -> Optional[str]: conf_dir_path = LocalPath(conf_dir) if conf_dir_path.is_dir() and conf_dir_path.exists(): # return the first valid directory found without the URI prefix - return conf_dir_path.no_prefix + return conf_dir_path.no_scheme except Exception as e: # pylint: disable=broad-except self.logger.debug( 'Could not build hadoop classpath from %s. Reason: %s', dir_key, e) diff --git a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py index e2d4c226e..55b91d81c 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py +++ b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py @@ -20,7 +20,6 @@ import os import re import sys -import tarfile import time from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass, field @@ -34,13 +33,16 @@ from spark_rapids_pytools.cloud_api.sp_types import get_platform, \ ClusterBase, DeployMode, NodeHWInfo from spark_rapids_pytools.common.prop_manager import YAMLPropertiesContainer, AbstractPropertiesContainer -from spark_rapids_pytools.common.sys_storage import FSUtil, FileVerifier +from spark_rapids_pytools.common.sys_storage import FSUtil from spark_rapids_pytools.common.utilities import ToolLogging, Utils, ToolsSpinner from spark_rapids_pytools.rapids.rapids_job import RapidsJobPropContainer from spark_rapids_pytools.rapids.tool_ctxt import ToolContext from spark_rapids_tools import CspEnv -from spark_rapids_tools.storagelib import LocalPath +from spark_rapids_tools.enums import HashAlgorithm +from spark_rapids_tools.storagelib import LocalPath, CspFs +from spark_rapids_tools.storagelib.tools.fs_utils import untar_file, FileHashAlgorithm from spark_rapids_tools.utils import Utilities +from spark_rapids_tools.utils.net_utils import DownloadTask @dataclass @@ -139,7 +141,7 @@ def _process_output_args(self): self.output_folder = Utils.get_rapids_tools_env('OUTPUT_DIRECTORY', os.getcwd()) try: output_folder_path = LocalPath(self.output_folder) - self.output_folder = output_folder_path.no_prefix + self.output_folder = output_folder_path.no_scheme except Exception as ex: # pylint: disable=broad-except self.logger.error('Failed in processing output arguments. Output_folder must be a local directory') raise ex @@ -411,6 +413,7 @@ class RapidsJarTool(RapidsTool): """ def _process_jar_arg(self): + # TODO: use the StorageLib to download the jar file jar_path = '' tools_jar_url = self.wrapper_options.get('toolsJar') try: @@ -533,43 +536,40 @@ def cache_single_dependency(dep: dict) -> str: """ Downloads the specified URL and saves it to disk """ - start_time = time.monotonic() self.logger.info('Checking dependency %s', dep['name']) dest_folder = self.ctxt.get_cache_folder() - resource_file_name = FSUtil.get_resource_name(dep['uri']) - resource_file = FSUtil.build_path(dest_folder, resource_file_name) - file_check_dict = {'size': dep['size']} - signature_file = FileVerifier.get_signature_file(dep['uri'], dest_folder) - if signature_file is not None: - file_check_dict['signatureFile'] = signature_file - algorithm = FileVerifier.get_integrity_algorithm(dep) - if algorithm is not None: - file_check_dict['hashlib'] = { - 'algorithm': algorithm, - 'hash': dep[algorithm] - } - is_created = FSUtil.cache_from_url(dep['uri'], resource_file, file_checks=file_check_dict) - if is_created: - self.logger.info('The dependency %s has been downloaded into %s', dep['uri'], - resource_file) - # check if we need to decompress files + verify_opts = {} + dep_verification = dep.get('verification') + if dep_verification is not None: + if 'size' in dep_verification: + verify_opts['size'] = dep_verification['size'] + hash_lib_alg = dep_verification.get('hashLib') + if hash_lib_alg: + verify_opts['file_hash'] = FileHashAlgorithm(HashAlgorithm(hash_lib_alg['type']), + hash_lib_alg['value']) + download_task = DownloadTask(src_url=dep['uri'], # pylint: disable=no-value-for-parameter) + dest_folder=dest_folder, + verification=verify_opts) + download_result = download_task.run_task() + self.logger.info('Completed downloading of dependency [%s] => %s', + dep['name'], + f'{download_result.pretty_print()}') + if not download_result.success: + msg = f'Failed to download dependency {dep["name"]}, reason: {download_result.download_error}' + raise RuntimeError(f'Could not download all dependencies. Aborting Executions.\n\t{msg}') + destination_path = self.ctxt.get_local_work_dir() + destination_cspath = LocalPath(destination_path) if dep['type'] == 'archive': - destination_path = self.ctxt.get_local_work_dir() - with tarfile.open(resource_file, mode='r:*') as tar: - tar.extractall(destination_path) - tar.close() - dep_item = FSUtil.remove_ext(resource_file_name) - if dep.get('relativePath') is not None: - dep_item = FSUtil.build_path(dep_item, dep.get('relativePath')) - dep_item = FSUtil.build_path(destination_path, dep_item) + uncompressed_cspath = untar_file(download_result.resource, destination_cspath) + dep_item = uncompressed_cspath.no_scheme + relative_path = dep.get('relativePath') + if relative_path is not None: + dep_item = f'{dep_item}/{relative_path}' else: # copy the jar into dependency folder - dep_item = self.ctxt.platform.storage.download_resource(resource_file, - self.ctxt.get_local_work_dir()) - end_time = time.monotonic() - self.logger.info('Completed downloading of dependency [%s] => %s seconds', - dep['name'], - f'{(end_time-start_time):,.3f}') + CspFs.copy_resources(download_result.resource, destination_cspath) + final_dep_csp = destination_cspath.create_sub_path(download_result.resource.base_name()) + dep_item = final_dep_csp.no_scheme return dep_item def cache_all_dependencies(dep_arr: List[dict]): @@ -593,7 +593,6 @@ def cache_all_dependencies(dep_arr: List[dict]): raise ex return results - # TODO: Verify the downloaded file by checking their MD5 deploy_mode = DeployMode.tostring(self.ctxt.get_deploy_mode()) depend_arr = self.get_rapids_tools_dependencies(deploy_mode, self.ctxt.platform.configs) if depend_arr: diff --git a/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json b/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json index 20afcaa93..7d64dfb44 100644 --- a/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json @@ -7,52 +7,78 @@ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" + }, + "size": 400395283 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319", - "size": 400395283 + "relativePath": "jars/*" }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", - "type": "jar", - "md5": "59907e790ce713441955015d79f670bc", - "sha1": "a65839fbf1869f81a1632e09f415e586922e4f80", - "size": 962685 + "verification": { + "hashLib": { + "type": "sha1", + "value": "a65839fbf1869f81a1632e09f415e586922e4f80" + }, + "size": 962685 + }, + "type": "jar" }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", - "type": "jar", - "md5": "8a22f2d30b7e8eee9ea44f04fb13b35a", - "sha1": "02deec3a0ad83d13d032b1812421b23d7a961eea", - "size": 280645251 + "verification": { + "hashLib": { + "type": "sha1", + "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" + }, + "size": 280645251 + }, + "type": "jar" } ], "333": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" + }, + "size": 299426263 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9", - "size": 299426263 + "relativePath": "jars/*" }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", - "type": "jar", - "md5": "59907e790ce713441955015d79f670bc", - "sha1": "a65839fbf1869f81a1632e09f415e586922e4f80", - "size": 962685 + "verification": { + "hashLib": { + "type": "sha1", + "value": "a65839fbf1869f81a1632e09f415e586922e4f80" + }, + "size": 962685 + }, + "type": "jar" }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", - "type": "jar", - "md5": "8a22f2d30b7e8eee9ea44f04fb13b35a", - "sha1": "02deec3a0ad83d13d032b1812421b23d7a961eea", - "size": 280645251 + "verification": { + "hashLib": { + "type": "sha1", + "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" + }, + "size": 280645251 + }, + "type": "jar" } ] } @@ -205,34 +231,6 @@ } } }, - "pricing": { - "catalog": { - "onlineResources": [ - { - "resourceKey": "databricks-aws-catalog", - "onlineURL": "https://www.databricks.com/en-website-assets/data/pricing/AWS.json", - "//localFile": "the name of the file after downloading", - "localFile": "databricks-aws-catalog.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "databricks-aws-catalog.json" - } - }, - { - "resourceKey": "ec2-catalog", - "onlineURL": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonEC2/current/us-west-2/index.json", - "//localFile": "the name of the file after downloading", - "localFile": "aws_ec2_catalog_ec2_us-west-2.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "aws_ec2_catalog_ec2_us-west-2.json" - } - } - ] - } - }, "gpuConfigs": { "user-tools": { "supportedGpuInstances": { diff --git a/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json b/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json index 07040a0be..0e41fb1ae 100644 --- a/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json @@ -7,36 +7,54 @@ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" + }, + "size": 400395283 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319", - "size": 400395283 + "relativePath": "jars/*" }, { "name": "Hadoop Azure", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-azure/3.3.4/hadoop-azure-3.3.4.jar", - "type": "jar", - "md5": "1ec4cbd59548412010fe1515070eef73", - "sha1": "a23f621bca9b2100554150f6b0b521f94b8b419e", - "size": 574116 + "verification": { + "hashLib": { + "type": "sha1", + "value": "a23f621bca9b2100554150f6b0b521f94b8b419e" + }, + "size": 574116 + }, + "type": "jar" } ], "333": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" + }, + "size": 299426263 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9", - "size": 299426263 + "relativePath": "jars/*" }, { "name": "Hadoop Azure", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-azure/3.3.4/hadoop-azure-3.3.4.jar", - "type": "jar", - "md5": "1ec4cbd59548412010fe1515070eef73", - "sha1": "a23f621bca9b2100554150f6b0b521f94b8b419e", - "size": 574116 + "verification": { + "hashLib": { + "type": "sha1", + "value": "a23f621bca9b2100554150f6b0b521f94b8b419e" + }, + "size": 574116 + }, + "type": "jar" } ] } @@ -154,22 +172,6 @@ } } }, - "pricing": { - "catalog": { - "onlineResources": [ - { - "resourceKey": "premium-databricks-azure-catalog", - "onlineURL": "https://azure.microsoft.com/en-us/pricing/details/databricks/", - "//localFile": "the name of the local file", - "localFile": "premium-databricks-azure-catalog.json", - "backupArchive": { - "//description": "We use this archive as a backup. It is stored in the resources", - "archiveName": "premium-databricks-azure-catalog.json" - } - } - ] - } - }, "gpuConfigs": { "user-tools": { "supportedGpuInstances": { diff --git a/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json b/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json index d25daa2db..1f99e7bac 100644 --- a/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json @@ -7,36 +7,54 @@ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" + }, + "size": 400395283 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319", - "size": 400395283 + "relativePath": "jars/*" }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.19/gcs-connector-hadoop3-2.2.19-shaded.jar", - "type": "jar", - "md5": "2ee6ad7215304cf5da8e731afb36ad72", - "sha1": "3bea6d5e62663a2a5c03d8ca44dff4921aeb3170", - "size": 39359477 + "verification": { + "hashLib": { + "type": "sha1", + "value": "3bea6d5e62663a2a5c03d8ca44dff4921aeb3170" + }, + "size": 39359477 + }, + "type": "jar" } ], "333": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" + }, + "size": 299426263 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9", - "size": 299426263 + "relativePath": "jars/*" }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.17/gcs-connector-hadoop3-2.2.17-shaded.jar", - "type": "jar", - "md5": "41aea3add826dfbf3384a2c638148709", - "sha1": "06438f562692ff8fae5e8555eba2b9f95cb74f66", - "size": 38413466 + "verification": { + "hashLib": { + "type": "sha1", + "value": "06438f562692ff8fae5e8555eba2b9f95cb74f66" + }, + "size": 38413466 + }, + "type": "jar" } ] } @@ -141,28 +159,6 @@ } } }, - "pricing": { - "catalog": { - "onlineResources": [ - { - "resourceKey": "gcloud-catalog", - "onlineURL": "https://cloudpricingcalculator.appspot.com/static/data/pricelist.json", - "//localFile": "the name of the file after downloading", - "localFile": "gcloud-catalog.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "gcloud-catalog.tgz" - } - } - ], - "components": { - "ssd": { - "unitSizeFactor": 0.513698630136986 - } - } - } - }, "gpuConfigs": { "user-tools": { "gpuPerMachine": { diff --git a/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json b/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json index dec1d05db..861b127d7 100644 --- a/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json @@ -7,36 +7,54 @@ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" + }, + "size": 400395283 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319", - "size": 400395283 + "relativePath": "jars/*" }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.19/gcs-connector-hadoop3-2.2.19-shaded.jar", - "type": "jar", - "md5": "2ee6ad7215304cf5da8e731afb36ad72", - "sha1": "3bea6d5e62663a2a5c03d8ca44dff4921aeb3170", - "size": 39359477 + "verification": { + "hashLib": { + "type": "sha1", + "value": "3bea6d5e62663a2a5c03d8ca44dff4921aeb3170" + }, + "size": 39359477 + }, + "type": "jar" } ], "333": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" + }, + "size": 299426263 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9", - "size": 299426263 + "relativePath": "jars/*" }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.17/gcs-connector-hadoop3-2.2.17-shaded.jar", - "type": "jar", - "md5": "41aea3add826dfbf3384a2c638148709", - "sha1": "06438f562692ff8fae5e8555eba2b9f95cb74f66", - "size": 38413466 + "verification": { + "hashLib": { + "type": "sha1", + "value": "06438f562692ff8fae5e8555eba2b9f95cb74f66" + }, + "size": 38413466 + }, + "type": "jar" } ] } @@ -136,28 +154,6 @@ } } }, - "pricing": { - "catalog": { - "onlineResources": [ - { - "resourceKey": "gcloud-catalog", - "onlineURL": "https://cloudpricingcalculator.appspot.com/static/data/pricelist.json", - "//localFile": "the name of the file after downloading", - "localFile": "gcloud-catalog.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "gcloud-catalog.tgz" - } - } - ], - "components": { - "ssd": { - "unitSizeFactor": 0.513698630136986 - } - } - } - }, "gpuConfigs": { "user-tools": { "gpuPerMachine": { diff --git a/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py b/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py index 4c9971689..1920c134b 100644 --- a/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py +++ b/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py @@ -20,7 +20,6 @@ import os import shutil import tarfile -from concurrent.futures import ThreadPoolExecutor from typing import Optional import fire @@ -30,6 +29,7 @@ from spark_rapids_pytools.rapids.rapids_tool import RapidsTool from spark_rapids_tools import CspEnv from spark_rapids_tools.utils import Utilities +from spark_rapids_tools.utils.net_utils import DownloadManager, DownloadTask # Defines the constants and static configurations prepackage_conf = { @@ -122,30 +122,23 @@ def _fetch_resources(self) -> dict: if uri: resource_uris[uri] = {'name': name, 'pbar_enabled': False} resource_uris[uri + '.asc'] = {'name': name + '.asc', 'pbar_enabled': False} - - # Add pricing files as resources - if platform_conf.get_value_silent('pricing'): - for pricing_entry in platform_conf.get_value('pricing', 'catalog', 'onlineResources'): - uri = pricing_entry.get('onlineURL') - name = pricing_entry.get('localFile') - if uri and name: - resource_uris[uri] = {'name': name, 'pbar_enabled': False} - return resource_uris def _download_resources(self, resource_uris: dict): - resource_uris_list = list(resource_uris.items()) - - def download_task(resource_uri, resource_info): - resource_name = resource_info['name'] - pbar_enabled = resource_info['pbar_enabled'] - resource_file_path = FSUtil.build_full_path(self.dest_dir, resource_name) - - print(f'Downloading {resource_name}') - FSUtil.fast_download_url(resource_uri, resource_file_path, pbar_enabled=pbar_enabled) - - with ThreadPoolExecutor() as executor: - executor.map(lambda x: download_task(x[0], x[1]), resource_uris_list) + download_tasks = [] + for res_uri, res_info in resource_uris.items(): + resource_name = res_info.get('name') + print(f'Creating download task: {resource_name}') + # All the downloadTasks enforces download + download_tasks.append(DownloadTask(src_url=res_uri, # pylint: disable=no-value-for-parameter) + dest_folder=self.dest_dir, + configs={'forceDownload': True})) + # Begin downloading the resources + download_results = DownloadManager(download_tasks, max_workers=12).submit() + print('----Download summary---') + for res in download_results: + print(res.pretty_print()) + print('-----------------------') def _compress_resources(self) -> Optional[str]: if not self.archive_enabled: diff --git a/user_tools/src/spark_rapids_pytools/resources/emr-configs.json b/user_tools/src/spark_rapids_pytools/resources/emr-configs.json index e273c224c..5b6a34bd2 100644 --- a/user_tools/src/spark_rapids_pytools/resources/emr-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/emr-configs.json @@ -7,52 +7,78 @@ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" + }, + "size": 400395283 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319", - "size": 400395283 + "relativePath": "jars/*" }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", - "type": "jar", - "md5": "59907e790ce713441955015d79f670bc", - "sha1": "a65839fbf1869f81a1632e09f415e586922e4f80", - "size": 962685 + "verification": { + "hashLib": { + "type": "sha1", + "value": "a65839fbf1869f81a1632e09f415e586922e4f80" + }, + "size": 962685 + }, + "type": "jar" }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", - "type": "jar", - "md5": "8a22f2d30b7e8eee9ea44f04fb13b35a", - "sha1": "02deec3a0ad83d13d032b1812421b23d7a961eea", - "size": 280645251 + "verification": { + "hashLib": { + "type": "sha1", + "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" + }, + "size": 280645251 + }, + "type": "jar" } ], "333": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" + }, + "size": 299426263 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9", - "size": 299426263 + "relativePath": "jars/*" }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", - "type": "jar", - "md5": "59907e790ce713441955015d79f670bc", - "sha1": "a65839fbf1869f81a1632e09f415e586922e4f80", - "size": 962685 + "verification": { + "hashLib": { + "type": "sha1", + "value": "a65839fbf1869f81a1632e09f415e586922e4f80" + }, + "size": 962685 + }, + "type": "jar" }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", - "type": "jar", - "md5": "8a22f2d30b7e8eee9ea44f04fb13b35a", - "sha1": "02deec3a0ad83d13d032b1812421b23d7a961eea", - "size": 280645251 + "verification": { + "hashLib": { + "type": "sha1", + "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" + }, + "size": 280645251 + }, + "type": "jar" } ] } @@ -170,34 +196,6 @@ } } }, - "pricing": { - "catalog": { - "onlineResources": [ - { - "resourceKey": "emr-catalog", - "onlineURL": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/ElasticMapReduce/current/us-west-2/index.json", - "//localFile": "the name of the file after downloading", - "localFile": "aws_ec2_catalog_emr_us-west-2.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "aws_ec2_catalog_emr_us-west-2.json" - } - }, - { - "resourceKey": "ec2-catalog", - "onlineURL": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonEC2/current/us-west-2/index.json", - "//localFile": "the name of the file after downloading", - "localFile": "aws_ec2_catalog_ec2_us-west-2.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "aws_ec2_catalog_ec2_us-west-2.json" - } - } - ] - } - }, "gpuConfigs": { "user-tools": { "supportedGpuInstances": { diff --git a/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json b/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json index 35eecae50..f7e9af218 100644 --- a/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json @@ -7,57 +7,50 @@ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" + }, + "size": 400395283 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319", - "size": 400395283 + "relativePath": "jars/*" } ], "342": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.4.2/spark-3.4.2-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "c9470a557c96fe899dd1c9ea8d0dda3310eaf0155b2bb972f70a6d97fee8cdaf838b425c30df3d5856b2c31fc2be933537c111db72d0427eabb76c6abd92c1f1" + }, + "size": 388664780 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "c9470a557c96fe899dd1c9ea8d0dda3310eaf0155b2bb972f70a6d97fee8cdaf838b425c30df3d5856b2c31fc2be933537c111db72d0427eabb76c6abd92c1f1", - "size": 388664780 + "relativePath": "jars/*" } ], "333": [ { "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", + "verification": { + "hashLib": { + "type": "sha512", + "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" + }, + "size": 299426263 + }, "type": "archive", - "relativePath": "jars/*", - "sha512": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9", - "size": 299426263 + "relativePath": "jars/*" } ] } } }, - "csp_pricing": { - "catalog": { - "onlineResources": [ - { - "resourceKey": "gcloud-catalog", - "onlineURL": "https://cloudpricingcalculator.appspot.com/static/data/pricelist.json", - "//localFile": "the name of the file after downloading", - "localFile": "gcloud-catalog.json", - "backupArchive": { - "//description-1": "In case the file is stuck, we use this archive as a backup.", - "//description-2": "It is stored in the resources", - "archiveName": "gcloud-catalog.tgz" - } - } - ], - "components": { - "ssd": { - "unitSizeFactor": 0.513698630136986 - } - } - } - }, "gpuConfigs": { "dataproc" : { "user-tools": { diff --git a/user_tools/src/spark_rapids_tools/enums.py b/user_tools/src/spark_rapids_tools/enums.py index 29bfcb2c9..61dca23a6 100644 --- a/user_tools/src/spark_rapids_tools/enums.py +++ b/user_tools/src/spark_rapids_tools/enums.py @@ -13,7 +13,7 @@ # limitations under the License. """Enumeration types commonly used through the AS python implementations.""" - +import hashlib from enum import Enum, auto from typing import Union, cast, Callable @@ -67,11 +67,45 @@ def pretty_print(cls, value): value = cast(Enum, value) return str(value._value_) # pylint: disable=protected-access +############### +# Utility Enums +############### + + +class HashAlgorithm(EnumeratedType): + """Represents the supported hashing algorithms""" + MD5 = 'md5' + SHA1 = 'sha1' + SHA256 = 'sha256' + SHA512 = 'sha512' + + @classmethod + def get_default(cls): + return cls.SHA256 + + @classmethod + def _missing_(cls, value): + value = value.lower() + for member in cls: + if member.lower() == value: + return member + return None + + def get_hash_func(self) -> Callable: + """Maps the hash function to the appropriate hashing algorithm""" + hash_functions = { + self.MD5: hashlib.md5, + self.SHA1: hashlib.sha1, + self.SHA256: hashlib.sha256, + self.SHA512: hashlib.sha512, + } + return hash_functions[self] ########### # CSP Enums ########### + class CspEnv(EnumeratedType): """Represents the supported types of runtime CSP""" DATABRICKS_AWS = 'databricks_aws' diff --git a/user_tools/src/spark_rapids_tools/storagelib/cspfs.py b/user_tools/src/spark_rapids_tools/storagelib/cspfs.py index 9adb58ea4..6c00bfaf3 100644 --- a/user_tools/src/spark_rapids_tools/storagelib/cspfs.py +++ b/user_tools/src/spark_rapids_tools/storagelib/cspfs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -99,6 +99,20 @@ def __init__(self, *args: Any, **kwargs: Any): def create_as_path(self, entry_path: Union[str, BoundedCspPath]) -> BoundedCspPath: return self._path_meta.path_class(entry_path=entry_path, fs_obj=self) + @classmethod + def copy_file(cls, src: BoundedCspPath, dest: BoundedCspPath): + """ + Copy a single file between FileSystems. This function assumes that + :param src: + :param dest: + :return: + """ + arrow_fs.copy_files(src.no_scheme, dest.no_scheme, + source_filesystem=src.fs_obj.fs, + destination_filesystem=dest.fs_obj.fs, + # 64 MB chunk size + chunk_size=64 * 1024 * 1024) + @classmethod def copy_resources(cls, src: BoundedCspPath, dest: BoundedCspPath): """ @@ -132,6 +146,8 @@ def copy_resources(cls, src: BoundedCspPath, dest: BoundedCspPath): dest.create_dirs() dest = dest.fs_obj.create_as_path(entry_path=dest_path) - arrow_fs.copy_files(src.no_prefix, dest.no_prefix, + arrow_fs.copy_files(src.no_scheme, dest.no_scheme, source_filesystem=src.fs_obj.fs, - destination_filesystem=dest.fs_obj.fs) + destination_filesystem=dest.fs_obj.fs, + # 64 MB chunk size + chunk_size=64 * 1024 * 1024) diff --git a/user_tools/src/spark_rapids_tools/storagelib/csppath.py b/user_tools/src/spark_rapids_tools/storagelib/csppath.py index 50d1b8c2d..59ec0aed6 100644 --- a/user_tools/src/spark_rapids_tools/storagelib/csppath.py +++ b/user_tools/src/spark_rapids_tools/storagelib/csppath.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -14,7 +14,7 @@ """ Abstract representation of a file path that can access local/URI values. -Similar to cloudpathlib project, this implementation uses dict registry to +Like to cloudpathlib project, this implementation uses dict registry to register an implementation. However, the path representation is built on top of pyArrow FS API. As a result, there is no need to write a full storage client to access remote files. This comes with a tradeoff in providing limited set of file @@ -279,11 +279,15 @@ def is_protocol_prefix(cls, value: str) -> bool: return value.lower().startswith(cls.protocol_prefix.lower()) @cached_property - def no_prefix(self) -> str: + def no_scheme(self) -> str: + """ + Get the path without the scheme. i.e., file:///path/to/file returns /path/to/file + :return: the full url without scheme part. + """ return self._fpath[len(self.protocol_prefix):] def _pull_file_info(self) -> FileInfo: - return self.fs_obj.get_file_info(self.no_prefix) + return self.fs_obj.get_file_info(self.no_scheme) @cached_property def file_info(self) -> FileInfo: @@ -308,16 +312,52 @@ def create_dirs(self, exist_ok: bool = True): # check that the file does not exist if self.exists(): raise CspFileExistsError(f'Path already Exists: {self}') - self.fs_obj.create_dir(self.no_prefix) + self.fs_obj.create_dir(self.no_scheme) # force the file information object to be retrieved again by invalidating the cached property if 'file_info' in self.__dict__: del self.__dict__['file_info'] def open_input_stream(self): - return self.fs_obj.open_input_stream(self.no_prefix) + return self.fs_obj.open_input_stream(self.no_scheme) def open_output_stream(self): - return self.fs_obj.open_output_stream(self.no_prefix) + return self.fs_obj.open_output_stream(self.no_scheme) + + def create_sub_path(self, relative: str) -> 'CspPath': + """ + Given a relative path, it will return a new CspPath object with the relative path appended to + the current path. This is just for building a path, and it does not call mkdirs. + For example, + ```py + root_folder = CspPath('gs://bucket-name/folder_00/subfolder_01') + new_path = root_folder.create_sub_path('subfolder_02') + print(new_path) + >> gs://bucket-name/folder_00/subfolder_01/subfolder_02 + ``` + :param relative: A relative path to append to the current path. + :return: A new path without creating the directory/file. + """ + postfix = '/' + sub_path = relative + if relative.startswith('/'): + sub_path = relative[1:] + if self._fpath.endswith('/'): + postfix = '' + new_path = f'{self._fpath}{postfix}{sub_path}' + return CspPath(new_path) + + @property + def size(self) -> int: + return self.file_info.size + + @property + def extension(self) -> str: + # this is used for existing files + return self.file_info.extension + + def extension_from_path(self) -> str: + # if file does not exist then get extension cannot use pull_info + return self.no_scheme.split('.')[-1] @classmethod def download_files(cls, src_url: str, dest_url: str): diff --git a/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py b/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py new file mode 100644 index 000000000..58dc56ebc --- /dev/null +++ b/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py @@ -0,0 +1,186 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for file system operations""" + +import dataclasses +import tarfile +from functools import cached_property +from typing import Optional, Union, List + +from pydantic import model_validator, FilePath, AnyHttpUrl, StringConstraints, ValidationError +from pydantic.dataclasses import dataclass +from pydantic_core import PydanticCustomError +from typing_extensions import Annotated + +from spark_rapids_tools import CspPath +from spark_rapids_tools.enums import HashAlgorithm +from spark_rapids_tools.storagelib import LocalPath + +CspPathString = Annotated[str, StringConstraints(pattern=r'^\w+://.*')] +""" +A type alias for a string that represents a path. The path must start with a protocol scheme. +""" + + +def strip_extension(file_name: str, count: int = 1) -> str: + """ + Utility method to strip the extension from a file name. By default, it only removes the last extension. + The caller can override the count of extensions. For examples: + :param file_name: The file name. + :param count: The number of extensions to remove. i.e., this for files with multi-extensions. + :return: The file name without the extension. + + Examples: + ```py + strip_extension('foo.tgz') # returns 'foo' + strip_extension('foo.tar.gz', count = 2) # returns 'foo' + strip_extension('foo.tar.gz') # returns 'foo.tar' + ``` + """ + return file_name.rsplit('.', count)[0] + + +def raise_invalid_file(file_path: CspPath, msg: str, error_type: str = 'invalid_file') -> None: + """ + Utility method to raise a custom pydantic error for invalid files. See the custom pydantic + errors for more details: https://docs.pydantic.dev/latest/errors/errors/#custom-errors + :param file_path: The object instantiated with the file path. + :param msg: + :param error_type: the error type top be displayed in the error message. + :return: + """ + raise PydanticCustomError(error_type, f'File {str(file_path)} {msg}') + + +@dataclass +class FileHashAlgorithm: + """ + Represents a file hash algorithm and its value. Used for verification against an + existing file. + ```py + try: + file_algo = FileHashAlgorithm(algorithm=HashAlgorithm.SHA256, value='...') + file_algo.verify_file(CspPath('file://path/to/file')) + except ValidationError as e: + print(e) + ``` + """ + algorithm: HashAlgorithm + value: str + + def verify_file(self, file_path: CspPath) -> bool: + cb = self.algorithm.get_hash_func() + with file_path.open_input_stream() as stream: + res = cb(stream.readall()) + if res.hexdigest() != self.value: + raise_invalid_file(file_path, f'incorrect file hash [{HashAlgorithm.tostring(self.algorithm)}]') + return True + + +@dataclasses.dataclass +class FileVerificationResult: + """ + A class that represents the result of a file verification. + :param res_path: The path to the resource subject of verification. + :param opts: Checking options to be passed to the FileChecker + :param raise_on_error: Flag to raise an exception if the file is invalid. + """ + res_path: CspPath + opts: dict + raise_on_error: bool = False + validation_error: Optional[ValidationError] = dataclasses.field(default=None, init=False) + + def __post_init__(self): + try: + CspFileChecker(**{'file_path': str(self.res_path), **self.opts}) + except ValidationError as err: + self.validation_error = err + if self.raise_on_error: + raise err + + @cached_property + def successful(self) -> bool: + return self.validation_error is None + + +@dataclass +class CspFileChecker: + """ + A class that represents a file checker. It is used as a pydantic model to validate the file. + :param file_path: The path to the file. It accepts any valid CspPath or AnyHttpUrl in case we want to + verify an url path extension even before we download it. + :param must_exist: When True, the file must exist. + :param is_file: When True, the file must be a file. Otherwise, it is a directory. + :param size: The expected size of the file. When 0, it is not checked. + :param extensions: A list of expected extensions. When None, it is not checked. + :param file_hash: A hash algorithm and its value to verify the file. + ```py + try: + TypeAdapter(CspFileChecker).validate_python({ + 'file_path': 'file:///var/tmp/spark_cache_folder_test/rapids-4-spark-tools_2.12-24.08.2.jar', + 'must_exist': True, + 'size': ....., + 'extensions': ['jar']}) + except ValidationError as e: + print(e) + ``` + """ + file_path: Union[CspPathString, FilePath, AnyHttpUrl] + must_exist: Optional[bool] = False + is_file: Optional[bool] = True + size: Optional[int] = 0 + extensions: Optional[List[str]] = None + file_hash: Optional[FileHashAlgorithm] = None + # TODO add verification for the modified time. + + @cached_property + def csp_path(self) -> CspPath: + return CspPath(str(self.file_path)) + + @model_validator(mode='after') + def verify_file(self) -> 'CspFileChecker': + if self.must_exist and not self.csp_path.exists(): + raise_invalid_file(self.file_path, 'does not exist') + if self.is_file and not self.csp_path.is_file(): + raise_invalid_file(self.file_path, 'expected a file but got a directory') + if self.size and self.size > 0: + if self.csp_path.size != self.size: + raise_invalid_file(self.file_path, f'size {self.csp_path.size} does not have the expected size') + if self.extensions: + file_ext = self.csp_path.extension_from_path() + if not any(file_ext == ext for ext in self.extensions): + raise_invalid_file(self.file_path, + f'[{file_ext}] does not match the expected extensions') + if self.file_hash: + self.file_hash.verify_file(self.csp_path) + return self + + +def untar_file(file_path: LocalPath, dest_folder: LocalPath) -> CspPath: + """ + Utility method to decompress a tgz file. + :param file_path: The path to the tar file. + :param dest_folder: The destination folder to untar the file. + """ + dest_folder.create_dirs() + # the compressed file must be local and must exist + FileVerificationResult(res_path=file_path, opts={'must_exist': True, 'is_file': True}, raise_on_error=True) + with tarfile.open(file_path.no_scheme, mode='r:*') as tar: + tar.extractall(dest_folder.no_scheme) + tar.close() + extracted_name = strip_extension(file_path.base_name()) + result = dest_folder.create_sub_path(extracted_name) + # we do not need to verify that it is a folder because the result might be a file. + return result diff --git a/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py b/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py index d14849cfc..bde6da008 100644 --- a/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py +++ b/user_tools/src/spark_rapids_tools/tools/qualx/qualx_main.py @@ -75,7 +75,7 @@ def _get_model_path(platform: str, model: Optional[str]) -> Path: f'Custom model file [{model}] is invalid. Please specify a valid JSON file.') # TODO: If the path is remote, we need to copy it locally in order to successfully # load it with xgboost. - model_path = Path(CspPath(model).no_prefix) + model_path = Path(CspPath(model).no_scheme) if not model_path.exists(): raise FileNotFoundError(f'Model JSON file not found: {model_path}') else: diff --git a/user_tools/src/spark_rapids_tools/utils/net_utils.py b/user_tools/src/spark_rapids_tools/utils/net_utils.py new file mode 100644 index 000000000..85d8158a2 --- /dev/null +++ b/user_tools/src/spark_rapids_tools/utils/net_utils.py @@ -0,0 +1,366 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for network functions""" + +import concurrent +import dataclasses +import datetime +import json +import logging +import os +import shutil +import ssl +import time +import urllib +from concurrent.futures import Future, ThreadPoolExecutor +from functools import cached_property +from logging import Logger +from typing import Optional, List + +import certifi +import requests +from fastcore.all import urlsave +from fastprogress.fastprogress import progress_bar +from pydantic import Field, AnyUrl +from pydantic.dataclasses import dataclass +from typing_extensions import Annotated + +from spark_rapids_pytools.common.utilities import ToolLogging +from spark_rapids_tools import CspPath +from spark_rapids_tools.storagelib import CspFs +from spark_rapids_tools.storagelib.tools.fs_utils import FileVerificationResult + + +def download_url_request(url: str, fpath: str, timeout: float = None, + chunk_size: int = 32 * 1024 * 1024) -> str: + """ + Downloads a file from url source using the requests library. + This implementation is more suitable for large files as the chunk size is set to 32 MB. + For smaller file sizes, it might represent an overhead on memory consumption. + :param url: The source of the file to download. + :param fpath: The file path where the resource is saved. + :param timeout: Time in seconds before the requests times out. + :param chunk_size: Default buffer size to download the file. + :return: Local path where the file is downloaded. + """ + # disable the urllib3 debug messages + logging.getLogger('urllib3').setLevel(logging.WARNING) + with requests.get(url, stream=True, timeout=timeout) as r: + r.raise_for_status() + with open(fpath, 'wb') as f: + # set chunk size to 16 MB to lower the count of iterations. + for chunk in r.iter_content(chunk_size=chunk_size): + f.write(chunk) + return fpath + + +def download_url_urllib(url: str, fpath: str) -> str: + """ + Download the given url to the file path. This function is a simple wrapper around the urllib.request.urlopen + :param url: URL to download. + :param fpath: the destination of the saved file. + :return: Local path to the saved file. + """ + # disable the urllib3 debug messages + logging.getLogger('urllib3').setLevel(logging.WARNING) + # We create a context here to fix and issue with urlib requests issue. + context = ssl.create_default_context(cafile=certifi.where()) + with urllib.request.urlopen(url, context=context) as resp: + with open(fpath, 'wb') as f: + shutil.copyfileobj(resp, f, 64 * 1024 * 1024) + return fpath + + +def download_url_fastcore(url: str, fpath: str, timeout=None, pbar_enabled=False) -> str: + """ + Download the given url and display a progress bar. This implementation uses the fastcore library. + We used this by default because it is faster than the download_from_url function. + """ + pbar = progress_bar([]) + + def progress_bar_cb(count=1, bsize=1, total_size=None): + pbar.total = total_size + pbar.update(count * bsize) + + return urlsave(url, fpath, reporthook=progress_bar_cb if pbar_enabled else None, timeout=timeout) + + +def default_download_options(custom_opts: dict = None) -> dict: + """ + Utility function to create the default options for the download. + :param custom_opts: allows user to override or extend the download options. + :return: A dictionary with the default options + custom options. + """ + custom_opts = custom_opts if custom_opts else {} + default_opts = { + # force the download even teh file exists + 'forceDownload': False, + # 3600 is 1 hour + 'timeOut': 3600, + # number of retries + 'retryCount': 3 + } + default_opts.update(custom_opts) + return default_opts + + +def default_verification_options(custom_opts: dict = None) -> dict: + """ + Utility function to create the default options for the verification. + :param custom_opts: Allows user to override and to extend the default verification options. + :return: A dictionary with the default options + custom options. + """ + custom_opts = custom_opts if custom_opts else {} + default_opts = { + # the file must exist + 'must_exist': True, + # it has to be a file + 'is_file': True, + # size of the file. 0 to ignore + 'size': 0, + # list of extensions to check + 'extensions': [] + } + default_opts.update(custom_opts) + return default_opts + + +def download_exception_handler(future: Future) -> None: + # Handle any exceptions raised by the task + exception = future.exception() + if exception: + print('Error while downloading dependency: %s', exception) + + +@dataclasses.dataclass +class DownloadResult: + """ + A class that represents the result of a download task. It contains the following information: + :param resource: The path where the downloaded resource is located. Notice that we use a CspPath + instead of a string or FilePath to represent different schemes and CspStorages. + :param origin_url: The original URL of the resource. + :param success: Whether the download is successful. + :param downloaded: Whether the resource is downloaded or loaded from an existing folder. + i.e., if the file already exists, and the download is not enforced, then the downloaded + value should be false. + :param download_time: The elapsed download time in seconds. + :param verified: Whether the resource is verified. + :param download_error: The error that occurred during the download if any. + """ + resource: CspPath + origin_url: str + success: bool + downloaded: bool + download_time: float = 0 + verified: bool = True + download_error: Optional[Exception] = None + + def pretty_print(self) -> str: + json_str = { + 'resource': str(self.resource), + 'origin_url': self.origin_url, + 'success': self.success, + 'downloaded': self.downloaded, + 'download_time(seconds)': f'{self.download_time:,.3f}', + } + if self.download_error: + json_str['download_error'] = str(self.download_error) + + return json.dumps(json_str, indent=4) + + +@dataclass +class DownloadTask: + """ + A class that represents a download task. It contains the following information: + :param src_url: The URL of the resource to download. + :param dest_folder: The destination folder where the resource is downloaded. + :param configs: A dictionary of download options. See default_download_options() for more + information about acceptable options. + :param verification: A dictionary of verification options. See default_verification_options() + for more information about acceptable options. + """ + src_url: AnyUrl + dest_folder: str + configs: Annotated[Optional[dict], Field(default_factory=lambda: default_download_options())] # pylint: disable=unnecessary-lambda + verification: Annotated[Optional[dict], Field(default_factory=lambda: default_verification_options())] # pylint: disable=unnecessary-lambda + + def __post_init__(self): + # Add the defaults when the caller does not set default values. + self.configs = default_download_options(self.configs) + self.verification = default_verification_options(self.verification) + + @cached_property + def resource_base_name(self) -> str: + return CspPath(self.src_url.path).base_name() + + @cached_property + def dest_dir(self) -> CspPath: + dest_root = CspPath(self.dest_folder) + dest_root.create_dirs(exist_ok=True) + return dest_root + + @cached_property + def dest_res(self) -> CspPath: + return self.dest_dir.create_sub_path(self.resource_base_name) + + @cached_property + def force_download(self) -> bool: + return self.configs.get('forceDownload', False) + + def _download_resource(self, opts: dict) -> DownloadResult: + """ + Downloads a single Url path to a local file system. + :param opts: Options passed to the internal download call. + :return: A DownloadResult object. + """ + def download_from_weburl() -> None: + # by default use the requests library to download the file as it performs better for + # larger files. + download_url_request(opts['srcUrl'], opts['destPath'], timeout=opts['timeOut']) + + def download_from_cs() -> None: + # download the file from the Cloud storage including local file system. + csp_src = CspPath(opts['srcUrl']) + CspFs.copy_file(csp_src, self.dest_res) + + start_time = time.monotonic() + curr_time_stamp = datetime.datetime.now().timestamp() + download_exception = None + downloaded = False + success = False + try: + if self.src_url.scheme == 'https': + download_from_weburl() + else: + download_from_cs() + FileVerificationResult(res_path=self.dest_res, opts=self.verification, raise_on_error=True) + # update modified time and access time + os.utime(opts['destPath'], times=(curr_time_stamp, curr_time_stamp)) + success = True + downloaded = True + except Exception as e: # pylint: disable=broad-except + download_exception = e + # we need to create a new CsPath in order to refresh the fileInfo cached with the instance. + return DownloadResult(resource=CspPath(self.dest_res), + origin_url=opts['srcUrl'], + success=success, + downloaded=downloaded, + download_time=time.monotonic() - start_time, + download_error=download_exception) + + def run_task(self) -> DownloadResult: + local_res = self.dest_res + if local_res.exists() and not self.force_download: + # verify that the file is correct using the verification options + if FileVerificationResult(res_path=self.dest_res, + opts=self.verification, raise_on_error=False).successful: + # the file already exists. Skip downloading it. + return DownloadResult(resource=local_res, origin_url=str(self.src_url), + success=True, downloaded=False) + # the file needs to be redownloaded. For example, it might be a corrupted attempt. + download_opts = { + 'srcUrl': str(self.src_url), + 'destPath': local_res.no_scheme, + # set default timeout oif a single task to 1 hour + 'timeOut': self.configs.get('timeOut', 3600), + } + download_res = self._download_resource(download_opts) + return download_res + + def async_submit(self, thread_executor: ThreadPoolExecutor, task_list: List[Future]) -> None: + futures = thread_executor.submit(self.run_task) + futures.add_done_callback(download_exception_handler) + task_list.append(futures) + + +@dataclass +class DownloadManager: + """ + A class that downloads a list of resources in parallel. It creates a threadPool and run a + DownloadTask for each. + https://stackoverflow.com/questions/6509261/how-to-use-concurrent-futures-with-timeouts + :param download_tasks: A list of DownloadTask objects. + :param max_workers: The maximum workers threads to run in parallel. + To disable parallelism, set the value to 1. + :param time_out: The maximum time to wait for the download to complete. Default is 30 minutes. + Note that there is a bug in the python module that ignores the timeout. The timeout exception + is not triggered for some reason. Nevertheless, we set the timeout hopefully this bug gets fixed. + The current behavior is to throws an exception after all the future tasks get completed. + + example usage: + ```py + DownloadManager( + [DownloadTask(src_url='https://urlpath/rapids-4-spark-tools_2.12-24.08.2.jar', + dest_folder='file:///var/tmp/spark_cache_folder_test/async', + configs={'forceDownload': True}, + verification={'size': ....}), + DownloadTask(src_url='https://urlpath/rapids-4-spark-tools_2.12-24.08.1.jar', + dest_folder='file:///var/tmp/spark_cache_folder_test/async', + configs={'forceDownload': True}, + verification={'file_hash': FileHashAlgorithm(HashAlgorithm('md5'), '.....')}), + # the following is file-to-file copy. + DownloadTask(src_url='file:///path/to/file.ext', + dest_folder='file:///var/tmp/spark_cache_folder_test/async'), + DownloadTask(src_url='https://urlpath/spark-3.5.0-bin-hadoop3.tgz', + dest_folder='file:///var/tmp/spark_cache_folder_test/async', + configs={'forceDownload': True}, + verification={'file_hash': FileHashAlgorithm(HashAlgorithm('sha512'), '....')}) + ]).submit() + ``` + """ + download_tasks: List[DownloadTask] + # set it to 1 to avoid parallelism + max_workers: Optional[int] = 4 + # set the timeout to 60 minutes. + time_out: Optional[int] = 3600 + raise_on_error: Optional[bool] = True + enable_logging: Optional[bool] = False + + @cached_property + def get_logger(self) -> Logger: + return ToolLogging.get_and_setup_logger('rapids.tools.download_manager', debug_mode=True) + + def loginfo(self, msg: str) -> None: + if self.enable_logging: + self.get_logger.info(msg) + + def submit(self) -> List[DownloadResult]: + futures_list = [] + results = [] + final_results = [] + failed_downloads = [] + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + try: + for task in self.download_tasks: + self.loginfo(f'Submitting download task: {task.src_url}') + task.async_submit(executor, futures_list) + # set the timeout to 30 minutes. + for future in concurrent.futures.as_completed(futures_list, timeout=self.time_out): + results.append(future.result()) + for res in results: + if res is not None: + final_results.append(res) + self.loginfo(f'download result: {res.pretty_print()}') + if not res.success: + failed_downloads.append(res) + if self.raise_on_error: + if failed_downloads: + raise ValueError(f'Failed to download the following resources: {failed_downloads}') + if len(final_results) != len(self.download_tasks): + raise ValueError('Not all tasks are completed') + except concurrent.futures.TimeoutError as e: + raise ValueError('Timed out while downloading all tasks') from e + return final_results From 81328c1b1185eaa5de1f402f71cdbb537c497482 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Mon, 21 Oct 2024 09:31:01 -0700 Subject: [PATCH 18/24] Add support for processing Photon event logs in Scala (#1338) * Add support to process photon event logs Signed-off-by: Partho Sarthi * Add `isPhoton` col in output csv and update tests Signed-off-by: Partho Sarthi * Add unit test for photon event log Signed-off-by: Partho Sarthi * Update E2E tests for Photon Support Signed-off-by: Partho Sarthi * Refactor photon parsing Signed-off-by: Partho Sarthi * Add PhotonPlanParser unit test Signed-off-by: Partho Sarthi * Address comments Signed-off-by: Partho Sarthi * Add databricks runtime info in mapping file Signed-off-by: Partho Sarthi * Address review comments Signed-off-by: Partho Sarthi * Revert "Add `isPhoton` col in output csv and update tests" This reverts commit 316c3424fd8e234dd1ad1c85f785096e2b3f78fa. * Update unit test Signed-off-by: Partho Sarthi * Update Photon parsing Signed-off-by: Partho Sarthi * Remove unused `PhotonEventLogException` Signed-off-by: Partho Sarthi * Fix merge conflict Signed-off-by: Partho Sarthi * Address review feedback Signed-off-by: Partho Sarthi * Address review feedback Signed-off-by: Partho Sarthi --------- Signed-off-by: Partho Sarthi --- .pre-commit-config.yaml | 4 +- .../databricks-13_3.json | 106 ++++++++ .../BroadcastNestedLoopJoinExecParser.scala | 24 +- .../planparser/DatabricksParseHelper.scala | 41 ++- .../tool/planparser/SQLPlanParser.scala | 257 ++++++++++-------- .../planparser/WholeStageExecParser.scala | 10 +- ...tonBroadcastNestedLoopJoinExecParser.scala | 39 +++ .../planparser/photon/PhotonPlanParser.scala | 48 ++++ .../photon/PhotonStageExecParser.scala | 39 +++ .../spark/sql/rapids/tool/ToolUtils.scala | 4 - .../qualification/QualificationAppInfo.scala | 5 +- .../sql/rapids/tool/util/ToolsPlanGraph.scala | 13 +- .../tool/util/plangraph/PhotonPlan.scala | 72 +++++ .../util/plangraph/PlanGraphTransformer.scala | 55 ++++ .../nds_q88_photon_db_13_3.zstd | Bin 0 -> 3473101 bytes .../tool/planparser/BasePlanParserSuite.scala | 83 ++++++ .../planparser/PhotonPlanParserSuite.scala | 47 ++++ .../tool/planparser/SqlPlanParserSuite.scala | 64 +---- .../qualification/QualificationSuite.scala | 7 + .../features/event_log_processing.feature | 14 +- 20 files changed, 729 insertions(+), 203 deletions(-) create mode 100644 core/src/main/resources/photonOperatorMappings/databricks-13_3.json create mode 100644 core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonBroadcastNestedLoopJoinExecParser.scala create mode 100644 core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonPlanParser.scala create mode 100644 core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonStageExecParser.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PhotonPlan.scala create mode 100644 core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PlanGraphTransformer.scala create mode 100644 core/src/test/resources/spark-events-qualification/nds_q88_photon_db_13_3.zstd create mode 100644 core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/BasePlanParserSuite.scala create mode 100644 core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/PhotonPlanParserSuite.scala diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 19ab1a9d3..4204d7503 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -31,8 +31,8 @@ repos: rev: v4.0.1 hooks: - id: check-added-large-files - name: Check for file over 2.0MiB - args: ['--maxkb=2000', '--enforce-all'] + name: Check for file over 4.0MiB + args: ['--maxkb=4000', '--enforce-all'] - id: trailing-whitespace name: trim trailing white spaces preserving md files args: ['--markdown-linebreak-ext=md'] diff --git a/core/src/main/resources/photonOperatorMappings/databricks-13_3.json b/core/src/main/resources/photonOperatorMappings/databricks-13_3.json new file mode 100644 index 000000000..35ffaab3f --- /dev/null +++ b/core/src/main/resources/photonOperatorMappings/databricks-13_3.json @@ -0,0 +1,106 @@ +{ + "//comments": [ + "This file contains the mapping between Photon operators and Spark operators generated from Databricks Runtime 13.3", + "Some entries have one-to-many mappings. For example, 'PhotonAgg' can map to either 'HashAggregate', 'SortAggregate', or 'ObjectHashAggregate'.", + "Currently, only the first mapping in the list is used.", + "This limitation exists because we cannot differentiate between these operators in the SparkPlan.", + "TODO: Create separate mapping file for different Photon/Databricks versions" + ], + "PhotonAdapter": [ + "Scan" + ], + "PhotonScan": [ + "Scan" + ], + "PhotonSubqueryBroadcast": [ + "SubqueryBroadcast" + ], + "PhotonResultStage": [ + "WholeStageCodegen" + ], + "PhotonUnionShuffleExchangeSink": [ + "Union" + ], + "PhotonUnionShuffleMapStage": [ + "WholeStageCodegen" + ], + "PhotonTopK": [ + "TakeOrderedAndProject" + ], + "PhotonAgg": [ + "HashAggregate", + "SortAggregate", + "ObjectHashAggregate" + ], + "PhotonBroadcastExchange": [ + "BroadcastExchange" + ], + "PhotonBroadcastHashJoin": [ + "BroadcastHashJoin" + ], + "PhotonBroadcastNestedLoopJoin": [ + "BroadcastNestedLoopJoin" + ], + "PhotonExpand": [ + "Expand" + ], + "PhotonFilter": [ + "Filter" + ], + "PhotonGenerate": [ + "Generate" + ], + "PhotonGlobalLimit": [ + "GlobalLimit" + ], + "PhotonGroupingAgg": [ + "HashAggregate", + "SortAggregate", + "ObjectHashAggregate" + ], + "PhotonGroupingAggWithRollup": [ + "HashAggregate" + ], + "PhotonHashJoin": [ + "BroadcastHashJoin" + ], + "PhotonLocalLimit": [ + "LocalLimit" + ], + "PhotonProject": [ + "Project" + ], + "PhotonRowToColumnar": [ + "RowToColumnar" + ], + "PhotonShuffledHashJoin": [ + "SortMergeJoin" + ], + "PhotonShuffleExchangeSink": [ + "Exchange", + "StageBoundary", + "BroadcastExchange" + ], + "PhotonShuffleExchangeSource": [ + "Exchange", + "StageBoundary", + "AQEShuffleRead", + "ShuffleQueryStage" + ], + "PhotonShuffleHashJoin": [ + "ShuffledHashJoin" + ], + "PhotonShuffleMapStage": [ + "WholeStageCodegen" + ], + "PhotonSort": [ + "Sort" + ], + "PhotonUnion": [ + "Union" + ], + "PhotonWindow": [ + "Window", + "RunningWindowFunction" + ] +} diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/BroadcastNestedLoopJoinExecParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/BroadcastNestedLoopJoinExecParser.scala index cbd7bdd4c..49f242efc 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/BroadcastNestedLoopJoinExecParser.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/BroadcastNestedLoopJoinExecParser.scala @@ -20,17 +20,29 @@ import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker import org.apache.spark.sql.execution.ui.SparkPlanGraphNode -case class BroadcastNestedLoopJoinExecParser( +abstract class BroadcastNestedLoopJoinExecParserBase( node: SparkPlanGraphNode, checker: PluginTypeChecker, sqlID: Long) extends ExecParser { - val fullExecName = node.name + "Exec" + val fullExecName: String = node.name + "Exec" + + protected def extractBuildAndJoinTypes(exprStr: String): (String, String) = { + // BuildRight, LeftOuter, ((CEIL(cast(id1#1490 as double)) <= cast(id2#1496 as bigint)) + // AND (cast(id1#1490 as bigint) < CEIL(cast(id2#1496 as double)))) + // Get joinType and buildSide by splitting the input string. + val nestedLoopParameters = exprStr.split(",", 3) + val buildSide = nestedLoopParameters(0).trim + val joinType = nestedLoopParameters(1).trim + (buildSide, joinType) + } override def parse: ExecInfo = { // BroadcastNestedLoopJoin doesn't have duration val exprString = node.desc.replaceFirst("BroadcastNestedLoopJoin ", "") - val (expressions, supportedJoinType) = SQLPlanParser.parseNestedLoopJoinExpressions(exprString) + val (buildSide, joinType) = extractBuildAndJoinTypes(exprString) + val (expressions, supportedJoinType) = + SQLPlanParser.parseNestedLoopJoinExpressions(exprString, buildSide, joinType) val notSupportedExprs = expressions.filterNot(expr => checker.isExprSupported(expr)) val duration = None val (speedupFactor, isSupported) = if (checker.isExecSupported(fullExecName) && @@ -43,3 +55,9 @@ case class BroadcastNestedLoopJoinExecParser( ExecInfo(node, sqlID, node.name, "", speedupFactor, duration, node.id, isSupported, None) } } + +case class BroadcastNestedLoopJoinExecParser( + node: SparkPlanGraphNode, + checker: PluginTypeChecker, + sqlID: Long) + extends BroadcastNestedLoopJoinExecParserBase(node, checker, sqlID) diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala index 53f5e075c..4916d3529 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala @@ -16,12 +16,17 @@ package com.nvidia.spark.rapids.tool.planparser +import java.nio.file.Paths + import scala.util.control.NonFatal +import scala.util.matching.Regex -import org.json4s.DefaultFormats +import org.json4s.{DefaultFormats, Formats} +import org.json4s.jackson.JsonMethods import org.json4s.jackson.JsonMethods.parse import org.apache.spark.internal.Logging +import org.apache.spark.sql.rapids.tool.util.UTF8Source // Utilities used to handle Databricks and Photon Ops object DatabricksParseHelper extends Logging { @@ -42,6 +47,11 @@ object DatabricksParseHelper extends Logging { val SUB_PROP_CLUSTER_ID = "ClusterId" val SUB_PROP_JOB_ID = "JobId" val SUB_PROP_RUN_NAME = "RunName" + + private val PHOTON_PATTERN: Regex = "Photon[a-zA-Z]*".r + private val PHOTON_OPS_MAPPING_DIR = "photonOperatorMappings" + // TODO: Create separate mapping file for different Photon/Databricks versions + private val DEFAULT_PHOTON_OPS_MAPPING_FILE = "databricks-13_3.json" /** * Checks if the properties indicate that the application is a Photon app. * This ca be checked by looking for keywords in one of the keys defined in PHOTON_SPARK_PROPS @@ -108,4 +118,33 @@ object DatabricksParseHelper extends Logging { Map.empty } } + + /** + * Maps the Photon operator names to Spark operator names using a mapping JSON file. + */ + private lazy val photonToSparkMapping: Map[String, String] = { + val mappingFile = Paths.get(PHOTON_OPS_MAPPING_DIR, DEFAULT_PHOTON_OPS_MAPPING_FILE).toString + val jsonString = UTF8Source.fromResource(mappingFile).mkString + val json = JsonMethods.parse(jsonString) + // Implicitly define JSON formats for deserialization using DefaultFormats + implicit val formats: Formats = DefaultFormats + // Extract and deserialize the JValue object into a Map[String, String] + // TODO: Currently, only the first mapping in the list is used. + // This limitation exists because we cannot differentiate between + // these operators in the SparkPlan. + json.extract[Map[String, List[String]]].mapValues(_.head) + } + + def isPhotonNode(nodeName: String): Boolean = PHOTON_PATTERN.findFirstIn(nodeName).isDefined + + /** + * Replaces all occurrences in the input string that match the PHOTON_PATTERN + * with corresponding values from the photonToSparkMapping map. + * + * @param inputStr the node name, potentially containing a Photon identifier + * @return an `Option[String]` with the Spark node name, or `None` if no match is found + */ + def mapPhotonToSpark(inputStr: String): String = { + PHOTON_PATTERN.replaceAllIn(inputStr, m => photonToSparkMapping.getOrElse(m.matched, m.matched)) + } } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/SQLPlanParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/SQLPlanParser.scala index 2150b0401..47d42d102 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/SQLPlanParser.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/SQLPlanParser.scala @@ -21,6 +21,7 @@ import scala.collection.mutable.{ArrayBuffer, WeakHashMap} import scala.util.control.NonFatal import scala.util.matching.Regex +import com.nvidia.spark.rapids.tool.planparser.photon.{PhotonPlanParser, PhotonStageExecParser} import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker import org.apache.spark.internal.Logging @@ -28,6 +29,7 @@ import org.apache.spark.sql.execution.SparkPlanInfo import org.apache.spark.sql.execution.ui.{SparkPlanGraph, SparkPlanGraphCluster, SparkPlanGraphNode} import org.apache.spark.sql.rapids.tool.{AppBase, BuildSide, ExecHelper, JoinType, RDDCheckHelper, ToolUtils, UnsupportedExpr} import org.apache.spark.sql.rapids.tool.util.ToolsPlanGraph +import org.apache.spark.sql.rapids.tool.util.plangraph.{PhotonSparkPlanGraphCluster, PhotonSparkPlanGraphNode} object OpActions extends Enumeration { type OpAction = Value @@ -451,6 +453,103 @@ object SQLPlanParser extends Logging { // Note that Spark graph may create duplicate nodes when any of the following execs exists. private val reuseExecs = Set("ReusedExchange") + /** + * Parse the SparkPlanGraphNode and return ExecInfo. + */ + def parseSparkNode( + node: SparkPlanGraphNode, + sqlID: Long, + checker: PluginTypeChecker, + app: AppBase): ExecInfo = { + val normalizedNodeName = node.name.stripSuffix("$") + normalizedNodeName match { + case "AggregateInPandas" => + AggregateInPandasExecParser(node, checker, sqlID).parse + case "ArrowEvalPython" => + ArrowEvalPythonExecParser(node, checker, sqlID).parse + case "BatchScan" => + BatchScanExecParser(node, checker, sqlID, app).parse + case "BroadcastExchange" => + BroadcastExchangeExecParser(node, checker, sqlID, app).parse + case "BroadcastHashJoin" => + BroadcastHashJoinExecParser(node, checker, sqlID).parse + case "BroadcastNestedLoopJoin" => + BroadcastNestedLoopJoinExecParser(node, checker, sqlID).parse + case "CartesianProduct" => + CartesianProductExecParser(node, checker, sqlID).parse + case "Coalesce" => + CoalesceExecParser(node, checker, sqlID).parse + case "CollectLimit" => + CollectLimitExecParser(node, checker, sqlID).parse + case "CustomShuffleReader" | "AQEShuffleRead" => + CustomShuffleReaderExecParser(node, checker, sqlID).parse + case "Exchange" => + ShuffleExchangeExecParser(node, checker, sqlID, app).parse + case "Expand" => + ExpandExecParser(node, checker, sqlID).parse + case "Filter" => + FilterExecParser(node, checker, sqlID).parse + case "FlatMapGroupsInPandas" => + FlatMapGroupsInPandasExecParser(node, checker, sqlID).parse + case "Generate" => + GenerateExecParser(node, checker, sqlID).parse + case "GlobalLimit" => + GlobalLimitExecParser(node, checker, sqlID).parse + case "HashAggregate" => + HashAggregateExecParser(node, checker, sqlID, app).parse + case "LocalLimit" => + LocalLimitExecParser(node, checker, sqlID).parse + case "InMemoryTableScan" => + InMemoryTableScanExecParser(node, checker, sqlID).parse + case i if DataWritingCommandExecParser.isWritingCmdExec(i) => + DataWritingCommandExecParser.parseNode(node, checker, sqlID) + case "MapInPandas" => + MapInPandasExecParser(node, checker, sqlID).parse + case "ObjectHashAggregate" => + ObjectHashAggregateExecParser(node, checker, sqlID, app).parse + case "Project" => + ProjectExecParser(node, checker, sqlID).parse + case "PythonMapInArrow" | "MapInArrow" => + PythonMapInArrowExecParser(node, checker, sqlID).parse + case "Range" => + RangeExecParser(node, checker, sqlID).parse + case "Sample" => + SampleExecParser(node, checker, sqlID).parse + case "ShuffledHashJoin" => + ShuffledHashJoinExecParser(node, checker, sqlID, app).parse + case "Sort" => + SortExecParser(node, checker, sqlID).parse + case s if ReadParser.isScanNode(s) => + FileSourceScanExecParser(node, checker, sqlID, app).parse + case "SortAggregate" => + SortAggregateExecParser(node, checker, sqlID).parse + case smj if SortMergeJoinExecParser.accepts(smj) => + SortMergeJoinExecParser(node, checker, sqlID).parse + case "SubqueryBroadcast" => + SubqueryBroadcastExecParser(node, checker, sqlID, app).parse + case sqe if SubqueryExecParser.accepts(sqe) => + SubqueryExecParser.parseNode(node, checker, sqlID, app) + case "TakeOrderedAndProject" => + TakeOrderedAndProjectExecParser(node, checker, sqlID).parse + case "Union" => + UnionExecParser(node, checker, sqlID).parse + case "Window" => + WindowExecParser(node, checker, sqlID).parse + case "WindowInPandas" => + WindowInPandasExecParser(node, checker, sqlID).parse + case "WindowGroupLimit" => + WindowGroupLimitParser(node, checker, sqlID).parse + case wfe if WriteFilesExecParser.accepts(wfe) => + WriteFilesExecParser(node, checker, sqlID).parse + case _ => + // Execs that are members of reuseExecs (i.e., ReusedExchange) should be marked as + // supported but with shouldRemove flag set to True. + // Setting the "shouldRemove" is handled at the end of the function. + ExecInfo(node, sqlID, normalizedNodeName, expr = "", 1, duration = None, node.id, + isSupported = reuseExecs.contains(normalizedNodeName), None) + } + } + def parsePlanNode( node: SparkPlanGraphNode, sqlID: Long, @@ -470,121 +569,47 @@ object SQLPlanParser extends Logging { logDebug(s"Marking [sqlID = ${sqlID}, node = ${normalizedNodeName}] as shouldRemove. " + s"Reason: duplicate - ancestor of ReusedExchange") } - if (normalizedNodeName.contains("WholeStageCodegen")) { - // this is special because it is a SparkPlanGraphCluster vs SparkPlanGraphNode - WholeStageExecParser(node.asInstanceOf[SparkPlanGraphCluster], checker, sqlID, app, - reusedNodeIds).parse - } else { - val execInfos = try { - normalizedNodeName match { - case "AggregateInPandas" => - AggregateInPandasExecParser(node, checker, sqlID).parse - case "ArrowEvalPython" => - ArrowEvalPythonExecParser(node, checker, sqlID).parse - case "BatchScan" => - BatchScanExecParser(node, checker, sqlID, app).parse - case "BroadcastExchange" => - BroadcastExchangeExecParser(node, checker, sqlID, app).parse - case "BroadcastHashJoin" => - BroadcastHashJoinExecParser(node, checker, sqlID).parse - case "BroadcastNestedLoopJoin" => - BroadcastNestedLoopJoinExecParser(node, checker, sqlID).parse - case "CartesianProduct" => - CartesianProductExecParser(node, checker, sqlID).parse - case "Coalesce" => - CoalesceExecParser(node, checker, sqlID).parse - case "CollectLimit" => - CollectLimitExecParser(node, checker, sqlID).parse - case "CustomShuffleReader" | "AQEShuffleRead" => - CustomShuffleReaderExecParser(node, checker, sqlID).parse - case "Exchange" => - ShuffleExchangeExecParser(node, checker, sqlID, app).parse - case "Expand" => - ExpandExecParser(node, checker, sqlID).parse - case "Filter" => - FilterExecParser(node, checker, sqlID).parse - case "FlatMapGroupsInPandas" => - FlatMapGroupsInPandasExecParser(node, checker, sqlID).parse - case "Generate" => - GenerateExecParser(node, checker, sqlID).parse - case "GlobalLimit" => - GlobalLimitExecParser(node, checker, sqlID).parse - case "HashAggregate" => - HashAggregateExecParser(node, checker, sqlID, app).parse - case "LocalLimit" => - LocalLimitExecParser(node, checker, sqlID).parse - case "InMemoryTableScan" => - InMemoryTableScanExecParser(node, checker, sqlID).parse - case i if DataWritingCommandExecParser.isWritingCmdExec(i) => - DataWritingCommandExecParser.parseNode(node, checker, sqlID) - case "MapInPandas" => - MapInPandasExecParser(node, checker, sqlID).parse - case "ObjectHashAggregate" => - ObjectHashAggregateExecParser(node, checker, sqlID, app).parse - case "Project" => - ProjectExecParser(node, checker, sqlID).parse - case "PythonMapInArrow" | "MapInArrow" => - PythonMapInArrowExecParser(node, checker, sqlID).parse - case "Range" => - RangeExecParser(node, checker, sqlID).parse - case "Sample" => - SampleExecParser(node, checker, sqlID).parse - case "ShuffledHashJoin" => - ShuffledHashJoinExecParser(node, checker, sqlID, app).parse - case "Sort" => - SortExecParser(node, checker, sqlID).parse - case s if ReadParser.isScanNode(s) => - FileSourceScanExecParser(node, checker, sqlID, app).parse - case "SortAggregate" => - SortAggregateExecParser(node, checker, sqlID).parse - case smj if SortMergeJoinExecParser.accepts(smj) => - SortMergeJoinExecParser(node, checker, sqlID).parse - case "SubqueryBroadcast" => - SubqueryBroadcastExecParser(node, checker, sqlID, app).parse - case sqe if SubqueryExecParser.accepts(sqe) => - SubqueryExecParser.parseNode(node, checker, sqlID, app) - case "TakeOrderedAndProject" => - TakeOrderedAndProjectExecParser(node, checker, sqlID).parse - case "Union" => - UnionExecParser(node, checker, sqlID).parse - case "Window" => - WindowExecParser(node, checker, sqlID).parse - case "WindowInPandas" => - WindowInPandasExecParser(node, checker, sqlID).parse - case "WindowGroupLimit" => - WindowGroupLimitParser(node, checker, sqlID).parse - case wfe if WriteFilesExecParser.accepts(wfe) => - WriteFilesExecParser(node, checker, sqlID).parse - case _ => - // Execs that are members of reuseExecs (i.e., ReusedExchange) should be marked as - // supported but with shouldRemove flag set to True. - // Setting the "shouldRemove" is handled at the end of the function. + + node match { + // For WholeStageCodegen clusters, use PhotonStageExecParser if the cluster is of Photon type. + // Else, fall back to WholeStageExecParser to parse the cluster. + case photonCluster: PhotonSparkPlanGraphCluster => + PhotonStageExecParser(photonCluster, checker, sqlID, app, reusedNodeIds).parse + case cluster: SparkPlanGraphCluster => + WholeStageExecParser(cluster, checker, sqlID, app, reusedNodeIds).parse + case _ => + // For individual nodes, use PhotonPlanParser if the node is of Photon type. + // Else, fall back to the Spark node parsing logic to parse the node. + val execInfo = try { + node match { + case photonNode: PhotonSparkPlanGraphNode => + PhotonPlanParser.parseNode(photonNode, sqlID, checker, app) + case _ => + parseSparkNode(node, sqlID, checker, app) + } + } catch { + // Error parsing expression could trigger an exception. If the exception is not handled, + // the application will be skipped. We need to suppress exceptions here to avoid + // sacrificing the entire app analysis. + // Note that: + // - The exec will be considered unsupported. + // - No need to add the SQL to the failed SQLs, because this will cause the app to be + // labeled as "Not Applicable" which is not preferred at this point. + case NonFatal(e) => + logWarning(s"Unexpected error parsing plan node ${normalizedNodeName}. " + + s" sqlID = ${sqlID}", e) ExecInfo(node, sqlID, normalizedNodeName, expr = "", 1, duration = None, node.id, - isSupported = reuseExecs.contains(normalizedNodeName), None) + isSupported = false, None) } - } catch { - // Error parsing expression could trigger an exception. If the exception is not handled, - // the application will be skipped. We need to suppress exceptions here to avoid - // sacrificing the entire app analysis. - // Note that: - // - The exec will be considered unsupported. - // - No need to add the SQL to the failed SQLs, because this will cause the app to be - // labeled as "Not Applicable" which is not preferred at this point. - case NonFatal(e) => - logWarning(s"Unexpected error parsing plan node ${normalizedNodeName}. " + - s" sqlID = ${sqlID}", e) - ExecInfo(node, sqlID, normalizedNodeName, expr = "", 1, duration = None, node.id, - isSupported = false, None) - } - val stagesInNode = getStagesInSQLNode(node, app) - execInfos.setStages(stagesInNode) - // shouldRemove is set to true if the exec is a member of "execsToBeRemoved" or if the node - // is a duplicate - execInfos.setShouldRemove(isDupNode) - // Set the custom reasons for unsupported execs - val unsupportedExecsReason = checker.getNotSupportedExecsReason(execInfos.exec) - execInfos.setUnsupportedExecReason(unsupportedExecsReason) - Seq(execInfos) + val stagesInNode = getStagesInSQLNode(node, app) + execInfo.setStages(stagesInNode) + // shouldRemove is set to true if the exec is a member of "execsToBeRemoved" or if the node + // is a duplicate + execInfo.setShouldRemove(isDupNode) + // Set the custom reasons for unsupported execs + val unsupportedExecsReason = checker.getNotSupportedExecsReason(execInfo.exec) + execInfo.setUnsupportedExecReason(unsupportedExecsReason) + Seq(execInfo) } } @@ -932,14 +957,12 @@ object SQLPlanParser extends Logging { } } - def parseNestedLoopJoinExpressions(exprStr: String): (Array[String], Boolean) = { + def parseNestedLoopJoinExpressions(exprStr: String, buildSide: String, + joinType: String): (Array[String], Boolean) = { // BuildRight, LeftOuter, ((CEIL(cast(id1#1490 as double)) <= cast(id2#1496 as bigint)) // AND (cast(id1#1490 as bigint) < CEIL(cast(id2#1496 as double)))) // Get joinType and buildSide by splitting the input string. val nestedLoopParameters = exprStr.split(",", 3) - val buildSide = nestedLoopParameters(0).trim - val joinType = nestedLoopParameters(1).trim - // Check if condition present on join columns else return empty array val parsedExpressions = if (nestedLoopParameters.size > 2) { parseConditionalExpressions(exprStr) diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/WholeStageExecParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/WholeStageExecParser.scala index e6efb848a..19eb46a52 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/WholeStageExecParser.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/WholeStageExecParser.scala @@ -22,7 +22,7 @@ import org.apache.spark.internal.Logging import org.apache.spark.sql.execution.ui.SparkPlanGraphCluster import org.apache.spark.sql.rapids.tool.AppBase -case class WholeStageExecParser( +abstract class WholeStageExecParserBase( node: SparkPlanGraphCluster, checker: PluginTypeChecker, sqlID: Long, @@ -67,3 +67,11 @@ case class WholeStageExecParser( Seq(execInfo) } } + +case class WholeStageExecParser( + node: SparkPlanGraphCluster, + checker: PluginTypeChecker, + sqlID: Long, + app: AppBase, + reusedNodeIds: Set[Long]) + extends WholeStageExecParserBase(node, checker, sqlID, app, reusedNodeIds) diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonBroadcastNestedLoopJoinExecParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonBroadcastNestedLoopJoinExecParser.scala new file mode 100644 index 000000000..e3a23bac3 --- /dev/null +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonBroadcastNestedLoopJoinExecParser.scala @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.nvidia.spark.rapids.tool.planparser.photon + +import com.nvidia.spark.rapids.tool.planparser.BroadcastNestedLoopJoinExecParserBase +import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker + +import org.apache.spark.sql.rapids.tool.util.plangraph.PhotonSparkPlanGraphNode + +case class PhotonBroadcastNestedLoopJoinExecParser( + node: PhotonSparkPlanGraphNode, + checker: PluginTypeChecker, + sqlID: Long) + extends BroadcastNestedLoopJoinExecParserBase(node, checker, sqlID) { + + override def extractBuildAndJoinTypes(exprStr: String): (String, String) = { + // LeftOuter, BuildRight, ((CEIL(cast(id1#1490 as double)) <= cast(id2#1496 as bigint)) + // AND (cast(id1#1490 as bigint) < CEIL(cast(id2#1496 as double)))) + // Get joinType and buildSide by splitting the input string. + val nestedLoopParameters = exprStr.split(",", 3) + val joinType = nestedLoopParameters(0).trim + val buildSide = nestedLoopParameters(1).trim + (buildSide, joinType) + } +} diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonPlanParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonPlanParser.scala new file mode 100644 index 000000000..ad3e6ed18 --- /dev/null +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonPlanParser.scala @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.nvidia.spark.rapids.tool.planparser.photon + +import com.nvidia.spark.rapids.tool.planparser._ +import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker + +import org.apache.spark.sql.rapids.tool.AppBase +import org.apache.spark.sql.rapids.tool.util.plangraph.PhotonSparkPlanGraphNode + +object PhotonPlanParser { + /** + * Parses a Photon node, using a specific Photon parser if available. + * If no Photon specific parser is defined, use Spark CPU equivalent + * ExecParser. + * + * @return Parsed ExecInfo + */ + def parseNode( + node: PhotonSparkPlanGraphNode, + sqlID: Long, + checker: PluginTypeChecker, + app: AppBase): ExecInfo = { + node.photonName match { + case "PhotonBroadcastNestedLoopJoin" => + PhotonBroadcastNestedLoopJoinExecParser(node, checker, sqlID).parse + case _ => + // If no photon specific parser is defined, parse using Spark CPU parser. + // This is allowed because `node.name` and `node.desc` are set to the Spark CPU + // operator's name and description. + SQLPlanParser.parseSparkNode(node, sqlID, checker, app) + } + } +} diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonStageExecParser.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonStageExecParser.scala new file mode 100644 index 000000000..d49192d27 --- /dev/null +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/photon/PhotonStageExecParser.scala @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.nvidia.spark.rapids.tool.planparser.photon + +import com.nvidia.spark.rapids.tool.planparser.WholeStageExecParserBase +import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker + +import org.apache.spark.sql.rapids.tool.AppBase +import org.apache.spark.sql.rapids.tool.util.plangraph.PhotonSparkPlanGraphCluster + +/** + * Parses Photon Stage Exec equivalent to Spark's `WholeStageCodegenExec` + * (e.g. PhotonShuffleMapStage, PhotonUnionShuffleMapStage etc.). + * This can be extended if specific parsing is needed for a particular Stage operator. + * + * @see Resource file in `photonOperatorMappings` for the mapping of Photon operators + * to Spark operators + */ +case class PhotonStageExecParser( + node: PhotonSparkPlanGraphCluster, + checker: PluginTypeChecker, + sqlID: Long, + app: AppBase, + reusedNodeIds: Set[Long]) + extends WholeStageExecParserBase(node, checker, sqlID, app, reusedNodeIds) diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/ToolUtils.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/ToolUtils.scala index 717851f9e..fc276a064 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/ToolUtils.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/ToolUtils.scala @@ -431,10 +431,6 @@ case class GpuEventLogException( message: String = "Cannot parse event logs from GPU run: skipping this file") extends AppEventlogProcessException(message) -case class PhotonEventLogException( - message: String = "Encountered Databricks Photon event log: skipping this file!") - extends AppEventlogProcessException(message) - case class StreamingEventLogException( message: String = "Encountered Spark Structured Streaming Job: skipping this file!") extends AppEventlogProcessException(message) diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala index 2b01f4fef..048b2afca 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/qualification/QualificationAppInfo.scala @@ -27,7 +27,7 @@ import org.apache.hadoop.conf.Configuration import org.apache.spark.internal.Logging import org.apache.spark.scheduler.{SparkListener, SparkListenerEvent} -import org.apache.spark.sql.rapids.tool.{AppBase, AppEventlogProcessException, ClusterSummary, FailureApp, GpuEventLogException, IncorrectAppStatusException, MlOps, MlOpsEventLogType, PhotonEventLogException, SupportedMLFuncsName, ToolUtils} +import org.apache.spark.sql.rapids.tool.{AppBase, AppEventlogProcessException, ClusterSummary, FailureApp, GpuEventLogException, IncorrectAppStatusException, MlOps, MlOpsEventLogType, SupportedMLFuncsName, ToolUtils} import org.apache.spark.sql.rapids.tool.annotation.{Calculated, WallClock} import org.apache.spark.sql.rapids.tool.store.StageModel @@ -112,9 +112,6 @@ class QualificationAppInfo( if (gpuMode) { throw GpuEventLogException() } - if (isPhoton) { - throw PhotonEventLogException() - } } override def guestimateAppEndTimeCB(): () => Option[Long] = { diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala index 1e4243595..6d6187d80 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/ToolsPlanGraph.scala @@ -16,13 +16,14 @@ package org.apache.spark.sql.rapids.tool.util +import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper import java.util.concurrent.atomic.AtomicLong - import scala.collection.mutable import org.apache.spark.sql.execution.SparkPlanInfo import org.apache.spark.sql.execution.ui.{SparkPlanGraph, SparkPlanGraphCluster, SparkPlanGraphEdge, SparkPlanGraphNode, SQLPlanMetric} import org.apache.spark.sql.rapids.tool.store.AccumNameRef +import org.apache.spark.sql.rapids.tool.util.plangraph.PlanGraphTransformer import org.apache.spark.sql.rapids.tool.util.stubs.{GraphReflectionAPI, GraphReflectionAPIHelper} /** @@ -91,6 +92,8 @@ object ToolsPlanGraph { private def processPlanInfo(nodeName: String): String = { if (nodeName.startsWith("Gpu")) { nodeName.replaceFirst("Gpu", "") + } else if (DatabricksParseHelper.isPhotonNode(nodeName)) { + DatabricksParseHelper.mapPhotonToSpark(nodeName) } else { nodeName } @@ -110,12 +113,14 @@ object ToolsPlanGraph { constructSQLPlanMetric(metric.name, metric.accumulatorId, metric.metricType) } - val cluster = api.constructCluster( + val baseCluster = api.constructCluster( nodeIdGenerator.getAndIncrement(), planInfo.nodeName, planInfo.simpleString, mutable.ArrayBuffer[SparkPlanGraphNode](), metrics) + // Transform if it is a specialized type(e.g. Photon) + val cluster = PlanGraphTransformer.transformPlanCluster(baseCluster) nodes += cluster buildSparkPlanGraphNode( @@ -155,8 +160,10 @@ object ToolsPlanGraph { val metrics = planInfo.metrics.map { metric => constructSQLPlanMetric(metric.name, metric.accumulatorId, metric.metricType) } - val node = api.constructNode(nodeIdGenerator.getAndIncrement(), + val baseNode = api.constructNode(nodeIdGenerator.getAndIncrement(), planInfo.nodeName, planInfo.simpleString, metrics) + // Transform if it is a specialized type(e.g. Photon) + val node = PlanGraphTransformer.transformPlanNode(baseNode) if (subgraph == null) { nodes += node } else { diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PhotonPlan.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PhotonPlan.scala new file mode 100644 index 000000000..0b95025c5 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PhotonPlan.scala @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.plangraph + +import scala.collection.mutable + +import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphNode, SQLPlanMetric} + +/** + * Extension of SparkPlanGraphNode to handle Photon nodes. + * Note: + * - photonName and photonDesc are the name and description of the Photon node + * - sparkName and sparkDesc are the name and description of the equivalent Spark node + */ +class PhotonSparkPlanGraphNode( + id: Long, + val photonName: String, + val photonDesc: String, + sparkName: String, + sparkDesc: String, + metrics: collection.Seq[SQLPlanMetric]) + extends SparkPlanGraphNode(id, sparkName, sparkDesc, metrics) + +object PhotonSparkPlanGraphNode { + def from(node: SparkPlanGraphNode): PhotonSparkPlanGraphNode = { + val sparkName = DatabricksParseHelper.mapPhotonToSpark(node.name) + val sparkDesc = DatabricksParseHelper.mapPhotonToSpark(node.desc) + new PhotonSparkPlanGraphNode(node.id, node.name, node.desc, sparkName, sparkDesc, node.metrics) + } +} + +/** + * Extension of SparkPlanGraphCluster to handle Photon nodes that are + * mapped to WholeStageCodegen. + * Note: + * - photonName and photonDesc are the name and description of the Photon node + * - name and desc are the name and description of the equivalent Spark node + */ +class PhotonSparkPlanGraphCluster( + id: Long, + val photonName: String, + val photonDesc: String, + sparkName: String, + sparkDesc: String, + nodes: mutable.ArrayBuffer[SparkPlanGraphNode], + metrics: collection.Seq[SQLPlanMetric]) + extends SparkPlanGraphCluster(id, sparkName, sparkDesc, nodes, metrics) + +object PhotonSparkPlanGraphCluster { + def from(cluster: SparkPlanGraphCluster): PhotonSparkPlanGraphCluster = { + val sparkName = DatabricksParseHelper.mapPhotonToSpark(cluster.name) + val sparkDesc = DatabricksParseHelper.mapPhotonToSpark(cluster.desc) + new PhotonSparkPlanGraphCluster(cluster.id, cluster.name, cluster.desc, sparkName, + sparkDesc, cluster.nodes, cluster.metrics) + } +} diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PlanGraphTransformer.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PlanGraphTransformer.scala new file mode 100644 index 000000000..09df73b35 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/plangraph/PlanGraphTransformer.scala @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.rapids.tool.util.plangraph + +import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper + +import org.apache.spark.sql.execution.ui.{SparkPlanGraphCluster, SparkPlanGraphNode} + + +/** + * Object responsible for transforming instances of SparkPlanGraphNode and SparkPlanGraphCluster + * to specialized instances (e.g. PhotonSparkPlanGraphNode) if they are of a specialized type. + * + * This can be extended to handle other types of nodes or clusters (e.g. GPU nodes or Velox nodes). + */ +object PlanGraphTransformer { + + /** + * Transforms a SparkPlanGraphNode and returns a specialized node if it is of a specialized + * type (e.g. Photon). + */ + def transformPlanNode(node: SparkPlanGraphNode): SparkPlanGraphNode = { + if (DatabricksParseHelper.isPhotonNode(node.name)) { + PhotonSparkPlanGraphNode.from(node) + } else { // More cases can be added here to handle other types of nodes + node + } + } + + /** + * Transforms a SparkPlanGraphCluster and returns a specialized cluster if it is of a specialized + * type (e.g. Photon). + */ + def transformPlanCluster(cluster: SparkPlanGraphCluster): SparkPlanGraphCluster = { + if (DatabricksParseHelper.isPhotonNode(cluster.name)) { + PhotonSparkPlanGraphCluster.from(cluster) + } else { // More cases can be added here to handle other types of clusters + cluster + } + } +} diff --git a/core/src/test/resources/spark-events-qualification/nds_q88_photon_db_13_3.zstd b/core/src/test/resources/spark-events-qualification/nds_q88_photon_db_13_3.zstd new file mode 100644 index 0000000000000000000000000000000000000000..505a36b01db55b3d65882792826e31701c1c3a42 GIT binary patch literal 3473101 zcmV(rK<>XNwJ-gIh@L}y2t;uK8VQLnF|bO6jvl~-m1L46#T-c6hubV@8k+gz$zn(m zJmR0gbGY1F)e&Cs(a(eagT5*Ji39Hf-U8+VHe1)JY)oAOOC%Bq7CQbUL=FMxN?ONU zzhqQupQnO*WLVQ54R)r*|4Ag zYw*$hP;M1dzc)i#;j0oupBzYpLRBgaZZe?|GTJlY-xtDDx7Eei&J|D`BF*ZpG?NtI zz<>c)fbjkd&c*3_fH-UEi=8e2Vp*DqWw~G)E}992iBuvB1qWEe(fsAU|qwq{=8I*LP=PSx@pT=1by8Y+4R=jccMNYpij3G46zOq?WjTWqkg* 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z>i_%@1i=0Ou%hsQ5RkN^r8}1(kE^SbtFo0Fm9!@xCpQ&^yAXvCH?JT+CxwlZyP2Dt zlZ73{7d0nWZaywSUOzz&Ka_U?5N#asy|7tb1?XW(mZws!s%SX}u>9gShA%i6r g17kqV&(*k=yw)b~EnCtcQK#a&J`8BCL}kwZ0ypavK>z>% literal 0 HcmV?d00001 diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/BasePlanParserSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/BasePlanParserSuite.scala new file mode 100644 index 000000000..54cad1b1c --- /dev/null +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/BasePlanParserSuite.scala @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.nvidia.spark.rapids.tool.planparser + +import com.nvidia.spark.rapids.BaseTestSuite +import com.nvidia.spark.rapids.tool.{EventLogPathProcessor, PlatformFactory, ToolTestUtils} +import com.nvidia.spark.rapids.tool.qualification._ + +import org.apache.spark.sql.rapids.tool.qualification.QualificationAppInfo +import org.apache.spark.sql.rapids.tool.util.RapidsToolsConfUtil + +class BasePlanParserSuite extends BaseTestSuite { + + val profileLogDir: String = ToolTestUtils.getTestResourcePath("spark-events-profiling") + val qualLogDir: String = ToolTestUtils.getTestResourcePath("spark-events-qualification") + + def assertSizeAndNotSupported(size: Int, execs: Seq[ExecInfo], + checkDurations: Boolean = true): Unit = { + for (t <- Seq(execs)) { + assert(t.size == size, t) + assert(t.forall(_.speedupFactor == 1), t) + assert(t.forall(_.isSupported == false), t) + assert(t.forall(_.children.isEmpty), t) + if (checkDurations) { + assert(t.forall(_.duration.isEmpty), t) + } + } + } + + def assertSizeAndSupported(size: Int, execs: Seq[ExecInfo], + expectedDur: Seq[Option[Long]] = Seq.empty, extraText: String = "", + checkDurations: Boolean = true): Unit = { + for (t <- Seq(execs)) { + assert(t.size == size, s"$extraText $t") + assert(t.forall(_.isSupported == true), s"$extraText $t") + assert(t.forall(_.children.isEmpty), s"$extraText $t") + if (expectedDur.nonEmpty) { + val durations = t.map(_.duration) + assert(durations.diff(expectedDur).isEmpty, + s"$extraText durations differ expected ${expectedDur.mkString(",")} " + + s"but got ${durations.mkString(",")}") + } else if (checkDurations) { + assert(t.forall(_.duration.isEmpty), s"$extraText $t") + } + } + } + + def createAppFromEventlog(eventLog: String): QualificationAppInfo = { + val hadoopConf = RapidsToolsConfUtil.newHadoopConf() + val (_, allEventLogs) = EventLogPathProcessor.processAllPaths( + None, None, List(eventLog), hadoopConf) + val pluginTypeChecker = new PluginTypeChecker() + assert(allEventLogs.size == 1) + val appResult = QualificationAppInfo.createApp(allEventLogs.head, hadoopConf, + pluginTypeChecker, reportSqlLevel = false, mlOpsEnabled = false, penalizeTransitions = true, + PlatformFactory.createInstance()) + appResult match { + case Right(app) => app + case Left(_) => throw new AssertionError("Cannot create application") + } + } + + def getAllExecsFromPlan(plans: Seq[PlanInfo]): Seq[ExecInfo] = { + val topExecInfo = plans.flatMap(_.execInfo) + topExecInfo.flatMap { e => + e.children.getOrElse(Seq.empty) :+ e + } + } +} diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/PhotonPlanParserSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/PhotonPlanParserSuite.scala new file mode 100644 index 000000000..edf8095bc --- /dev/null +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/PhotonPlanParserSuite.scala @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2024, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.nvidia.spark.rapids.tool.planparser + +import com.nvidia.spark.rapids.tool.qualification.PluginTypeChecker + + +class PhotonPlanParserSuite extends BasePlanParserSuite { + + // scalastyle:off line.size.limit + // Test cases for Photon nodes. We could add more operators here. + val photonOpTestCases: Seq[(String, String)] = Seq( + "PhotonBroadcastNestedLoopJoin" -> "BroadcastNestedLoopJoin", // Case: Photon specific parser + "PhotonProject" -> "Project", // Case: Fallback to Spark CPU parser + "PhotonShuffleMapStage" -> "WholeStageCodegen" // Case: WholeStageCodegen operator + ) + // scalastyle:on line.size.limit + + photonOpTestCases.foreach { case (photonName, sparkName) => + test(s"$photonName is parsed as Spark $sparkName") { + val eventLog = s"$qualLogDir/nds_q88_photon_db_13_3.zstd" + val pluginTypeChecker = new PluginTypeChecker() + val app = createAppFromEventlog(eventLog) + assert(app.sqlPlans.nonEmpty) + val parsedPlans = app.sqlPlans.map { case (sqlID, plan) => + SQLPlanParser.parseSQLPlan(app.appId, plan, sqlID, "", pluginTypeChecker, app) + } + val allExecInfo = getAllExecsFromPlan(parsedPlans.toSeq) + val reader = allExecInfo.filter(_.exec.contains(sparkName)) + assert(reader.nonEmpty, s"Failed to find $sparkName in $allExecInfo") + } + } +} diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala index 8d958f6c3..7c3948f9a 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/planparser/SqlPlanParserSuite.scala @@ -21,8 +21,7 @@ import java.io.{File, PrintWriter} import scala.collection.mutable import scala.util.control.NonFatal -import com.nvidia.spark.rapids.BaseTestSuite -import com.nvidia.spark.rapids.tool.{EventLogPathProcessor, PlatformFactory, ToolTestUtils} +import com.nvidia.spark.rapids.tool.ToolTestUtils import com.nvidia.spark.rapids.tool.qualification._ import org.scalatest.Matchers.{be, contain, convertToAnyShouldWrapper} import org.scalatest.exceptions.TestFailedException @@ -32,66 +31,9 @@ import org.apache.spark.sql.execution.ui.SQLPlanMetric import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ import org.apache.spark.sql.rapids.tool.ToolUtils -import org.apache.spark.sql.rapids.tool.qualification.QualificationAppInfo -import org.apache.spark.sql.rapids.tool.util.{FSUtils, RapidsToolsConfUtil, ToolsPlanGraph, UTF8Source} +import org.apache.spark.sql.rapids.tool.util.{FSUtils, ToolsPlanGraph, UTF8Source} -class SQLPlanParserSuite extends BaseTestSuite { - - private val profileLogDir = ToolTestUtils.getTestResourcePath("spark-events-profiling") - private val qualLogDir = ToolTestUtils.getTestResourcePath("spark-events-qualification") - - private def assertSizeAndNotSupported(size: Int, execs: Seq[ExecInfo], - checkDurations: Boolean = true): Unit = { - for (t <- Seq(execs)) { - assert(t.size == size, t) - assert(t.forall(_.speedupFactor == 1), t) - assert(t.forall(_.isSupported == false), t) - assert(t.forall(_.children.isEmpty), t) - if (checkDurations) { - assert(t.forall(_.duration.isEmpty), t) - } - } - } - - private def assertSizeAndSupported(size: Int, execs: Seq[ExecInfo], - expectedDur: Seq[Option[Long]] = Seq.empty, extraText: String = "", - checkDurations: Boolean = true): Unit = { - for (t <- Seq(execs)) { - assert(t.size == size, s"$extraText $t") - assert(t.forall(_.isSupported == true), s"$extraText $t") - assert(t.forall(_.children.isEmpty), s"$extraText $t") - if (expectedDur.nonEmpty) { - val durations = t.map(_.duration) - assert(durations.diff(expectedDur).isEmpty, - s"$extraText durations differ expected ${expectedDur.mkString(",")} " + - s"but got ${durations.mkString(",")}") - } else if (checkDurations) { - assert(t.forall(_.duration.isEmpty), s"$extraText $t") - } - } - } - - private def createAppFromEventlog(eventLog: String): QualificationAppInfo = { - val hadoopConf = RapidsToolsConfUtil.newHadoopConf() - val (_, allEventLogs) = EventLogPathProcessor.processAllPaths( - None, None, List(eventLog), hadoopConf) - val pluginTypeChecker = new PluginTypeChecker() - assert(allEventLogs.size == 1) - val appResult = QualificationAppInfo.createApp(allEventLogs.head, hadoopConf, - pluginTypeChecker, reportSqlLevel = false, mlOpsEnabled = false, penalizeTransitions = true, - PlatformFactory.createInstance()) - appResult match { - case Right(app) => app - case Left(_) => throw new AssertionError("Cannot create application") - } - } - - private def getAllExecsFromPlan(plans: Seq[PlanInfo]): Seq[ExecInfo] = { - val topExecInfo = plans.flatMap(_.execInfo) - topExecInfo.flatMap { e => - e.children.getOrElse(Seq.empty) :+ e - } - } +class SQLPlanParserSuite extends BasePlanParserSuite { test("Error parser does not cause entire app to fail") { // The purpose of this test is to make sure that the SQLParser won't trigger an exception that diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/qualification/QualificationSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/qualification/QualificationSuite.scala index 8f5f11a3c..0287c72f4 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/qualification/QualificationSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/qualification/QualificationSuite.scala @@ -1768,6 +1768,13 @@ class QualificationSuite extends BaseTestSuite { runQualificationTest(logFiles, expectedStatus = expectedStatus) } + test("test support for photon event log") { + val logFiles = Array(s"$logDir/nds_q88_photon_db_13_3.zstd") // photon event log + // Status counts: 1 SUCCESS, 0 FAILURE, 0 SKIPPED, 0 UNKNOWN + val expectedStatus = Some(StatusReportCounts(1, 0, 0, 0)) + runQualificationTest(logFiles, expectedStatus = expectedStatus) + } + test("process multiple attempts of the same app ID and skip lower attempts") { TrampolineUtil.withTempDir { outPath => val baseArgs = Array("--output-directory", diff --git a/user_tools/tests/spark_rapids_tools_e2e/features/event_log_processing.feature b/user_tools/tests/spark_rapids_tools_e2e/features/event_log_processing.feature index 6a3f97cb0..cd66b0bb6 100644 --- a/user_tools/tests/spark_rapids_tools_e2e/features/event_log_processing.feature +++ b/user_tools/tests/spark_rapids_tools_e2e/features/event_log_processing.feature @@ -21,16 +21,16 @@ Feature: Event Log Processing """ """ - And processed applications is "0" + And processed applications is "" And return code is "0" Examples: - | event_logs | expected_stderr | - | invalid_path_eventlog | process.failure.count = 1;invalid_path_eventlog not found, skipping! | - | gpu_eventlog.zstd | process.skipped.count = 1;GpuEventLogException: Cannot parse event logs from GPU run: skipping this file | - | photon_eventlog.zstd | process.skipped.count = 1;PhotonEventLogException: Encountered Databricks Photon event log: skipping this file! | - | streaming_eventlog.zstd | process.skipped.count = 1;StreamingEventLogException: Encountered Spark Structured Streaming Job: skipping this file! | - | incorrect_app_status_eventlog.zstd | process.NA.count = 1;IncorrectAppStatusException: Application status is incorrect. Missing AppInfo | + | event_logs | expected_stderr | processed_apps_count | + | invalid_path_eventlog | process.failure.count = 1;invalid_path_eventlog not found, skipping! | 0 | + | gpu_eventlog.zstd | process.skipped.count = 1;GpuEventLogException: Cannot parse event logs from GPU run: skipping this file | 0 | + | photon_eventlog.zstd | process.success.count = 1; | 1 | + | streaming_eventlog.zstd | process.skipped.count = 1;StreamingEventLogException: Encountered Spark Structured Streaming Job: skipping this file! | 0 | + | incorrect_app_status_eventlog.zstd | process.NA.count = 1;IncorrectAppStatusException: Application status is incorrect. Missing AppInfo | 0 | @test_id_ELP_0002 Scenario: Qualification tool JAR crashes From 88b37bf1cade5722fdf3c6f3eb4d8adda9378d08 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Mon, 21 Oct 2024 13:02:05 -0500 Subject: [PATCH 19/24] Use environment variables to set thresholds in static yaml configurations (#1389) * Use environment variables to set thresholds in static yaml configurations Signed-off-by: Ahmed Hussein Fixes #1387 This code change aims at parsing yaml configuration file and resolving the environment variables. - `spillThresholdBytes` defined in qualification-conf.yaml can be overridden by an env-var `RAPIDS_USER_TOOLS_SPILL_BYTES_THRESHOLD`. - `totalCoreSecThreshold` defined in qualification-conf.yaml can be overridden by an env-var `RAPIDS_USER_TOOLS_CORE_SECONDS_THRESHOLD`. Usage: ``` export RAPIDS_USER_TOOLS_CORE_SECONDS_THRESHOLD=1024 spark_rapids ``` --------- Signed-off-by: Ahmed Hussein --- user_tools/pyproject.toml | 4 +++- .../spark_rapids_pytools/common/prop_manager.py | 14 +++++++------- .../resources/qualification-conf.yaml | 4 ++-- .../tools/additional_heuristics.py | 3 ++- .../src/spark_rapids_tools/tools/top_candidates.py | 3 ++- .../src/spark_rapids_tools/utils/propmanager.py | 5 +++-- 6 files changed, 19 insertions(+), 14 deletions(-) diff --git a/user_tools/pyproject.toml b/user_tools/pyproject.toml index 8decf7a82..c92bff303 100644 --- a/user_tools/pyproject.toml +++ b/user_tools/pyproject.toml @@ -27,7 +27,9 @@ dependencies = [ "fastcore==1.7.10", "fire>=0.5.0", "pandas==1.4.3", - "pyYAML>=6.0", + "pyYAML>=6.0,<=7.0", + # This is used to resolve env-variable in yaml files. It requires netween 5.0 and 6.0 + "pyaml-env==1.2.1", "tabulate==0.8.10", "importlib-resources==5.10.2", "requests==2.31.0", diff --git a/user_tools/src/spark_rapids_pytools/common/prop_manager.py b/user_tools/src/spark_rapids_pytools/common/prop_manager.py index 637b139b0..2b2f8588d 100644 --- a/user_tools/src/spark_rapids_pytools/common/prop_manager.py +++ b/user_tools/src/spark_rapids_pytools/common/prop_manager.py @@ -21,6 +21,7 @@ from typing import Any, Callable, Union import yaml +from pyaml_env import parse_config from spark_rapids_tools import get_elem_from_dict, get_elem_non_safe @@ -104,14 +105,13 @@ def __open_json_file(self): def __open_yaml_file(self): try: - with open(self.prop_arg, 'r', encoding='utf-8') as yaml_file: - try: - self.props = yaml.safe_load(yaml_file) - except yaml.YAMLError as e: - raise RuntimeError('Incorrect format of Yaml File') from e + # parse_config sets the default encoding to utf-8 + self.props = parse_config(path=self.prop_arg) + except yaml.YAMLError as e: + raise RuntimeError('Incorrect format of Yaml File') from e except OSError as err: - raise RuntimeError('Please ensure the properties file exists ' - 'and you have the required access privileges.') from err + raise RuntimeError('Please ensure the properties file exists and you have the required ' + 'access privileges.') from err def _load_as_yaml(self): if self.file_load: diff --git a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml index 74c138a11..c6edb6431 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -249,7 +249,7 @@ local: columnWidth: 14 totalCoreSecCol: 'Total Core Seconds' # This is total core seconds of an 8-core machine running for 24 hours - totalCoreSecThreshold: 691200 + totalCoreSecThreshold: !ENV ${RAPIDS_USER_TOOLS_CORE_SECONDS_THRESHOLD:691200} speedupCategories: speedupColumnName: 'Estimated GPU Speedup' categoryColumnName: 'Estimated GPU Speedup Category' @@ -293,7 +293,7 @@ local: columns: - 'stageId' - 'SQL Nodes(IDs)' - spillThresholdBytes: 10737418240 + spillThresholdBytes: !ENV ${RAPIDS_USER_TOOLS_SPILL_BYTES_THRESHOLD:10737418240} allowedExecs: - 'Aggregate' - 'Join' diff --git a/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py b/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py index 0396957e2..48f01fac4 100644 --- a/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py +++ b/user_tools/src/spark_rapids_tools/tools/additional_heuristics.py @@ -109,7 +109,8 @@ def heuristics_based_on_spills(self, app_id_path: str) -> (bool, str): 'sqlToStageInfo', 'columns')] # Identify stages with significant spills - spill_threshold_bytes = self.props.get_value('spillBased', 'spillThresholdBytes') + # Convert the string to int because the parse_config method returns a string + spill_threshold_bytes = int(self.props.get_value('spillBased', 'spillThresholdBytes')) spill_condition = stage_agg_metrics['memoryBytesSpilled_sum'] > spill_threshold_bytes stages_with_spills = stage_agg_metrics[spill_condition] diff --git a/user_tools/src/spark_rapids_tools/tools/top_candidates.py b/user_tools/src/spark_rapids_tools/tools/top_candidates.py index 1572aa538..fb207406b 100644 --- a/user_tools/src/spark_rapids_tools/tools/top_candidates.py +++ b/user_tools/src/spark_rapids_tools/tools/top_candidates.py @@ -74,7 +74,8 @@ def _filter_apps(self) -> None: # Filter based on total core seconds threshold total_core_sec_col = self.props.get('totalCoreSecCol') - total_core_sec_threshold = self.props.get('totalCoreSecThreshold') + # Convert the string to int because the parse_config method returns a string + total_core_sec_threshold = int(self.props.get('totalCoreSecThreshold')) total_core_sec_condition = self.tools_processed_apps[total_core_sec_col] > total_core_sec_threshold filter_condition = filter_condition & total_core_sec_condition diff --git a/user_tools/src/spark_rapids_tools/utils/propmanager.py b/user_tools/src/spark_rapids_tools/utils/propmanager.py index c7dd6d822..584bbf7ac 100644 --- a/user_tools/src/spark_rapids_tools/utils/propmanager.py +++ b/user_tools/src/spark_rapids_tools/utils/propmanager.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -21,6 +21,7 @@ from typing import Union, Any, TypeVar, ClassVar, Type, Tuple, Optional import yaml +from pyaml_env import parse_config from pydantic import BaseModel, ConfigDict, model_validator, ValidationError from spark_rapids_tools.exceptions import JsonLoadException, YamlLoadException, InvalidPropertiesSchema @@ -45,7 +46,7 @@ def load_yaml(file_path: Union[str, CspPathT]) -> Any: file_path = CspPath(file_path) with file_path.open_input_stream() as fis: try: - return yaml.safe_load(fis) + return parse_config(data=fis.readall()) except yaml.YAMLError as e: raise YamlLoadException('Incorrect format of Yaml File') from e From 8223ee1a886e4db739cdf7dc37d926fdddecf9a7 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Wed, 23 Oct 2024 16:25:19 -0500 Subject: [PATCH 20/24] user-tools should add xms argument to java cmd (#1391) * user-tools should add xms argument to java cmd Signed-off-by: Ahmed Hussein Fixes #1382 Upon investigation, it was revealed that the min heap size could impact the runtime significantly. This code change aims at setting the xms java argument to 50% of the max heap size. - pass xms to the java cmd - update the runtime report to list jvm info along with jvm arguments related to heap: - `runtime.jvm.*` - `runtime.jvm.arg*` * remove debugging code Signed-off-by: Ahmed Hussein --------- Signed-off-by: Ahmed Hussein --- .../sql/rapids/tool/util/RuntimeUtil.scala | 32 +++++++++++++++++-- .../rapids/rapids_tool.py | 9 ++++-- .../src/spark_rapids_tools/utils/util.py | 5 +++ 3 files changed, 42 insertions(+), 4 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/RuntimeUtil.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/RuntimeUtil.scala index d39323c7a..a5babf35d 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/RuntimeUtil.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/util/RuntimeUtil.scala @@ -17,6 +17,9 @@ package org.apache.spark.sql.rapids.tool.util import java.io.{PrintWriter, StringWriter} +import java.lang.management.ManagementFactory + +import scala.collection.convert.ImplicitConversions.`collection AsScalaIterable` import com.nvidia.spark.rapids.tool.ToolTextFileWriter import org.apache.hadoop.conf.Configuration @@ -49,6 +52,12 @@ object RuntimeUtil extends Logging { // Add the Spark version used in runtime. // Note that it is different from the Spark version used in the build. buildProps.setProperty("runtime.spark.version", ToolUtils.sparkRuntimeVersion) + // Add the JVM and OS information + getJVMOSInfo.foreach { + kv => buildProps.setProperty(s"runtime.${kv._1}", kv._2) + } + // get the JVM memory arguments + getJVMHeapArguments.foreach(kv => buildProps.setProperty(s"runtime.${kv._1}", kv._2)) val reportWriter = new ToolTextFileWriter(outputDir, REPORT_FILE_NAME, REPORT_LABEL, hadoopConf) try { reportWriter.writeProperties(buildProps, REPORT_LABEL) @@ -73,6 +82,25 @@ object RuntimeUtil extends Logging { "os.version" -> System.getProperty("os.version") ) } -} - + def getJVMHeapArguments: Map[String, String] = { + ManagementFactory.getRuntimeMXBean.getInputArguments.filter( + p => p.startsWith("-Xmx") || p.startsWith("-Xms") || p.startsWith("-XX:")).map { + sizeArg => + if (sizeArg.startsWith("-Xmx")) { + ("jvm.arg.heap.max", sizeArg.drop(4)) + } else if (sizeArg.startsWith("-Xms")) { + ("jvm.arg.heap.min", sizeArg.drop(4)) + } else { // this is heap argument + // drop the first "-XX:" + val dropSize = if (sizeArg.startsWith("-XX:+")) 5 else 4 + val parts = sizeArg.drop(dropSize).split("=") + if (parts.length == 2) { + (s"jvm.arg.gc.${parts(0)}", parts(1)) + } else { + (s"jvm.arg.gc.${parts(0)}", "") + } + } + }.toMap + } +} diff --git a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py index 55b91d81c..5f8d5cf1a 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py +++ b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py @@ -878,11 +878,16 @@ def _re_evaluate_platform_args(self, tool_name: str) -> dict: job_args = self.ctxt.get_ctxt('jobArgs') result = copy.deepcopy(job_args) job_resources = self._get_job_submission_resources(tool_name) + jvm_min_heap = job_resources['jvmMinHeapSize'] jvm_max_heap = job_resources['jvmMaxHeapSize'] - jvm_heap_key = f'Xmx{jvm_max_heap}g' + jvm_max_heap_key = f'Xmx{jvm_max_heap}g' + jvm_min_heap_key = f'Xms{jvm_min_heap}g' # At this point, we need to set the heap argument for the JVM. Otherwise, the process uses # its default values. - result['platformArgs']['jvmArgs'].update({jvm_heap_key: ''}) + result['platformArgs']['jvmArgs'].update({ + jvm_min_heap_key: '', + jvm_max_heap_key: '' + }) return result @timeit('Building Job Arguments and Executing Job CMD') # pylint: disable=too-many-function-args diff --git a/user_tools/src/spark_rapids_tools/utils/util.py b/user_tools/src/spark_rapids_tools/utils/util.py index ab90c34ea..cc449a4d0 100644 --- a/user_tools/src/spark_rapids_tools/utils/util.py +++ b/user_tools/src/spark_rapids_tools/utils/util.py @@ -291,13 +291,18 @@ def adjust_tools_resources(cls, else: prof_threads = max(1, jvm_threads - num_threads_unit) if concurrent_mode else jvm_threads + # calculate the min heap size based on the max heap size + min_heap = max(1, heap_unit // 2) + return { 'qualification': { 'jvmMaxHeapSize': heap_unit, + 'jvmMinHeapSize': min_heap, 'rapidsThreads': num_threads_unit }, 'profiling': { 'jvmMaxHeapSize': prof_heap, + 'jvmMinHeapSize': min_heap, 'rapidsThreads': prof_threads } } From 1504968fa2bc48d4cbd74559b9cd9864d86c0040 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Fri, 25 Oct 2024 10:17:04 -0700 Subject: [PATCH 21/24] Add support for Photon-specific SQL Metrics (#1390) * Add missing metrics for Photon event logs Signed-off-by: Partho Sarthi * Add unit tests for metrics Signed-off-by: Partho Sarthi * Improve docs and minor refactor Signed-off-by: Partho Sarthi * Refactor method definition for `applyToAccumInfoMap` Signed-off-by: Partho Sarthi * Address comment and add test for scan metric Signed-off-by: Partho Sarthi --------- Signed-off-by: Partho Sarthi --- .../analysis/AppSparkMetricsAnalyzer.scala | 59 ++++++++++++++++++- .../planparser/DatabricksParseHelper.scala | 28 +++++++++ .../rapids/tool/views/DataSourceView.scala | 19 ++++-- .../sql/rapids/tool/store/AccumManager.scala | 7 +++ ...on_db_13_3_job_metrics_agg_expectation.csv | 58 ++++++++++++++++++ ...on_db_13_3_sql_metrics_agg_expectation.csv | 2 + ..._db_13_3_stage_metrics_agg_expectation.csv | 58 ++++++++++++++++++ .../rapids/tool/profiling/AnalysisSuite.scala | 19 +++++- 8 files changed, 240 insertions(+), 10 deletions(-) create mode 100644 core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_job_metrics_agg_expectation.csv create mode 100644 core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_sql_metrics_agg_expectation.csv create mode 100644 core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_stage_metrics_agg_expectation.csv diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSparkMetricsAnalyzer.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSparkMetricsAnalyzer.scala index 9b5422ccd..48723739a 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSparkMetricsAnalyzer.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/analysis/AppSparkMetricsAnalyzer.scala @@ -16,12 +16,15 @@ package com.nvidia.spark.rapids.tool.analysis +import java.util.concurrent.TimeUnit + import scala.collection.mutable +import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper import com.nvidia.spark.rapids.tool.profiling.{IOAnalysisProfileResult, JobAggTaskMetricsProfileResult, ShuffleSkewProfileResult, SQLDurationExecutorTimeProfileResult, SQLMaxTaskInputSizes, SQLTaskAggMetricsProfileResult, StageAggTaskMetricsProfileResult} import org.apache.spark.sql.rapids.tool.{AppBase, ToolUtils} -import org.apache.spark.sql.rapids.tool.store.TaskModel +import org.apache.spark.sql.rapids.tool.store.{AccumInfo, TaskModel} /** * Does analysis on the DataFrames from object of AppBase. @@ -324,12 +327,62 @@ class AppSparkMetricsAnalyzer(app: AppBase) extends AppAnalysisBase(app) { */ private def aggregateSparkMetricsByStageInternal(index: Int): Unit = { // TODO: this has stage attempts. we should handle different attempts + + // For Photon apps, peak memory and shuffle write time need to be calculated from accumulators + // instead of task metrics. + // Approach: + // 1. Collect accumulators for each metric type. + // 2. For each stage, retrieve the relevant accumulators and calculate aggregated values. + // Note: + // - A HashMap could be used instead of separate mutable.ArrayBuffer for each metric type, + // but avoiding it for readability. + val photonPeakMemoryAccumInfos = mutable.ArrayBuffer[AccumInfo]() + val photonShuffleWriteTimeAccumInfos = mutable.ArrayBuffer[AccumInfo]() + + if (app.isPhoton) { + app.accumManager.applyToAccumInfoMap { accumInfo => + accumInfo.infoRef.name.value match { + case name if name.contains( + DatabricksParseHelper.PHOTON_METRIC_PEAK_MEMORY_LABEL) => + // Collect accumulators for peak memory + photonPeakMemoryAccumInfos += accumInfo + case name if name.contains( + DatabricksParseHelper.PHOTON_METRIC_SHUFFLE_WRITE_TIME_LABEL) => + // Collect accumulators for shuffle write time + photonShuffleWriteTimeAccumInfos += accumInfo + case _ => // Ignore other accumulators + } + } + } + app.stageManager.getAllStages.foreach { sm => // TODO: Should we only consider successful tasks? val tasksInStage = app.taskManager.getTasks(sm.stageInfo.stageId, sm.stageInfo.attemptNumber()) // count duplicate task attempts val numAttempts = tasksInStage.size + + val (peakMemoryMax, shuffleWriteTimeSum) = if (app.isPhoton) { + // For max peak memory, we need to look at the accumulators at the task level. + val peakMemoryValues = tasksInStage.flatMap { taskModel => + photonPeakMemoryAccumInfos.flatMap { accumInfo => + accumInfo.taskUpdatesMap.get(taskModel.taskId) + } + } + // For sum of shuffle write time, we need to look at the accumulators at the stage level. + val shuffleWriteValues = photonShuffleWriteTimeAccumInfos.flatMap { accumInfo => + accumInfo.stageValuesMap.get(sm.stageInfo.stageId) + } + (AppSparkMetricsAnalyzer.maxWithEmptyHandling(peakMemoryValues), + TimeUnit.NANOSECONDS.toMillis(shuffleWriteValues.sum)) + } else { + // For non-Photon apps, use the task metrics directly. + val peakMemoryValues = tasksInStage.map(_.peakExecutionMemory) + val shuffleWriteTime = tasksInStage.map(_.sw_writeTime) + (AppSparkMetricsAnalyzer.maxWithEmptyHandling(peakMemoryValues), + shuffleWriteTime.sum) + } + val (durSum, durMax, durMin, durAvg) = AppSparkMetricsAnalyzer.getDurations(tasksInStage) val stageRow = StageAggTaskMetricsProfileResult(index, sm.stageInfo.stageId, @@ -350,7 +403,7 @@ class AppSparkMetricsAnalyzer(app: AppBase) extends AppAnalysisBase(app) { tasksInStage.map(_.memoryBytesSpilled).sum, tasksInStage.map(_.output_bytesWritten).sum, tasksInStage.map(_.output_recordsWritten).sum, - AppSparkMetricsAnalyzer.maxWithEmptyHandling(tasksInStage.map(_.peakExecutionMemory)), + peakMemoryMax, tasksInStage.map(_.resultSerializationTime).sum, AppSparkMetricsAnalyzer.maxWithEmptyHandling(tasksInStage.map(_.resultSize)), tasksInStage.map(_.sr_fetchWaitTime).sum, @@ -362,7 +415,7 @@ class AppSparkMetricsAnalyzer(app: AppBase) extends AppAnalysisBase(app) { tasksInStage.map(_.sr_totalBytesRead).sum, tasksInStage.map(_.sw_bytesWritten).sum, tasksInStage.map(_.sw_recordsWritten).sum, - tasksInStage.map(_.sw_writeTime).sum + shuffleWriteTimeSum ) stageLevelSparkMetrics(index).put(sm.stageInfo.stageId, stageRow) } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala index 4916d3529..886c9986b 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/planparser/DatabricksParseHelper.scala @@ -17,15 +17,19 @@ package com.nvidia.spark.rapids.tool.planparser import java.nio.file.Paths +import java.util.concurrent.TimeUnit import scala.util.control.NonFatal import scala.util.matching.Regex +import com.nvidia.spark.rapids.tool.profiling.SQLAccumProfileResults +import com.nvidia.spark.rapids.tool.views.IoMetrics import org.json4s.{DefaultFormats, Formats} import org.json4s.jackson.JsonMethods import org.json4s.jackson.JsonMethods.parse import org.apache.spark.internal.Logging +import org.apache.spark.sql.rapids.tool.UnsupportedMetricNameException import org.apache.spark.sql.rapids.tool.util.UTF8Source // Utilities used to handle Databricks and Photon Ops @@ -48,6 +52,13 @@ object DatabricksParseHelper extends Logging { val SUB_PROP_JOB_ID = "JobId" val SUB_PROP_RUN_NAME = "RunName" + // scalastyle:off + // Photon metric labels that are used as alternatives to Spark metric labels + val PHOTON_METRIC_CUMULATIVE_TIME_LABEL = "cumulative time" // Alternative for "scan time" + val PHOTON_METRIC_PEAK_MEMORY_LABEL = "peak memory usage" // Alternative for "peak execution memory" + val PHOTON_METRIC_SHUFFLE_WRITE_TIME_LABEL = "part of shuffle file write" // Alternative for "shuffle write time" + // scalastyle:on + private val PHOTON_PATTERN: Regex = "Photon[a-zA-Z]*".r private val PHOTON_OPS_MAPPING_DIR = "photonOperatorMappings" // TODO: Create separate mapping file for different Photon/Databricks versions @@ -147,4 +158,21 @@ object DatabricksParseHelper extends Logging { def mapPhotonToSpark(inputStr: String): String = { PHOTON_PATTERN.replaceAllIn(inputStr, m => photonToSparkMapping.getOrElse(m.matched, m.matched)) } + + /** + * Checks if 'accum' is a Photon I/O metric. + */ + def isPhotonIoMetric(accum: SQLAccumProfileResults): Boolean = + accum.name == PHOTON_METRIC_CUMULATIVE_TIME_LABEL && accum.nodeName.contains("Scan") + + /** + * Updates the I/O metrics for Photon apps based on the accumulator values. + */ + def updatePhotonIoMetric(accum: SQLAccumProfileResults, ioMetrics: IoMetrics): Unit = { + accum.name match { + case PHOTON_METRIC_CUMULATIVE_TIME_LABEL if accum.nodeName.contains("Scan") => + ioMetrics.scanTime = TimeUnit.NANOSECONDS.toMillis(accum.total) + case _ => throw UnsupportedMetricNameException(accum.name) + } + } } diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala index 157af2d0b..ff9a0699c 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/views/DataSourceView.scala @@ -19,6 +19,7 @@ package com.nvidia.spark.rapids.tool.views import scala.collection.Seq import com.nvidia.spark.rapids.tool.analysis.{ProfAppIndexMapperTrait, QualAppIndexMapperTrait} +import com.nvidia.spark.rapids.tool.planparser.DatabricksParseHelper import com.nvidia.spark.rapids.tool.profiling.{DataSourceProfileResult, SQLAccumProfileResults} import com.nvidia.spark.rapids.tool.qualification.QualSQLPlanAnalyzer @@ -40,6 +41,12 @@ object IoMetrics { val DECODE_TIME_LABEL = "GPU decode time" val EMPTY_IO_METRICS: IoMetrics = IoMetrics(0, 0, 0, 0) + + /** + * Get all labels for IoMetrics + */ + def getAllLabels: Seq[String] = Seq( + BUFFER_TIME_LABEL, SCAN_TIME_LABEL, DATA_SIZE_LABEL, DECODE_TIME_LABEL) } trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { @@ -48,11 +55,13 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { private def getIoMetrics(sqlAccums: Seq[SQLAccumProfileResults]): IoMetrics = { val finalRes = IoMetrics(0, 0, 0, 0) try { - sqlAccums.map(accum => accum.name match { + sqlAccums.foreach(accum => accum.name match { case IoMetrics.BUFFER_TIME_LABEL => finalRes.bufferTime = accum.total case IoMetrics.SCAN_TIME_LABEL => finalRes.scanTime = accum.total case IoMetrics.DATA_SIZE_LABEL => finalRes.dataSize = accum.total case IoMetrics.DECODE_TIME_LABEL => finalRes.decodeTime = accum.total + case _ if DatabricksParseHelper.isPhotonIoMetric(accum) => + DatabricksParseHelper.updatePhotonIoMetric(accum, finalRes) case _ => throw UnsupportedMetricNameException(accum.name) }) } catch { @@ -98,11 +107,9 @@ trait AppDataSourceViewTrait extends ViewableTrait[DataSourceProfileResult] { index: Int, appSqlAccums: Seq[SQLAccumProfileResults]): Seq[DataSourceProfileResult] = { // Filter appSqlAccums to get only required metrics - val dataSourceMetrics = appSqlAccums.filter( - sqlAccum => sqlAccum.name.contains(IoMetrics.BUFFER_TIME_LABEL) - || sqlAccum.name.contains(IoMetrics.SCAN_TIME_LABEL) - || sqlAccum.name.contains(IoMetrics.DECODE_TIME_LABEL) - || sqlAccum.name.equals(IoMetrics.DATA_SIZE_LABEL)) + val dataSourceMetrics = appSqlAccums.filter(sqlAccum => + IoMetrics.getAllLabels.contains(sqlAccum.name) || + app.isPhoton && DatabricksParseHelper.isPhotonIoMetric(sqlAccum)) val dsFromLastPlan = app.dataSourceInfo.map { ds => val sqlIdtoDs = dataSourceMetrics.filter( diff --git a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/AccumManager.scala b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/AccumManager.scala index aeac4eeb3..14f60fe29 100644 --- a/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/AccumManager.scala +++ b/core/src/main/scala/org/apache/spark/sql/rapids/tool/store/AccumManager.scala @@ -66,4 +66,11 @@ class AccumManager { def getMaxStageValue(id: Long): Option[Long] = { accumInfoMap.get(id).map(_.getMaxStageValue.get) } + + /** + * Applies the function `f` to each AccumInfo in the accumInfoMap. + */ + def applyToAccumInfoMap(f: AccumInfo => Unit): Unit = { + accumInfoMap.values.foreach(f) + } } diff --git a/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_job_metrics_agg_expectation.csv b/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_job_metrics_agg_expectation.csv new file mode 100644 index 000000000..97de9ad08 --- /dev/null +++ b/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_job_metrics_agg_expectation.csv @@ -0,0 +1,58 @@ +appIndex,jobId,numTasks,Duration,diskBytesSpilled_sum,duration_sum,duration_max,duration_min,duration_avg,executorCPUTime_sum,executorDeserializeCPUTime_sum,executorDeserializeTime_sum,executorRunTime_sum,input_bytesRead_sum,input_recordsRead_sum,jvmGCTime_sum,memoryBytesSpilled_sum,output_bytesWritten_sum,output_recordsWritten_sum,peakExecutionMemory_max,resultSerializationTime_sum,resultSize_max,sr_fetchWaitTime_sum,sr_localBlocksFetched_sum,sr_localBytesRead_sum,sr_remoteBlocksFetched_sum,sr_remoteBytesRead_sum,sr_remoteBytesReadToDisk_sum,sr_totalBytesRead_sum,sw_bytesWritten_sum,sw_recordsWritten_sum,sw_writeTime_sum +1,48,431,237976,0,371230,1032,333,861.3,343288,1261,1286,367785,190131859,8639936081,160,0,0,0,169667947,21,15767,8,3,15657,3,15657,0,31314,18964,431,16 +1,47,432,214657,0,376777,1133,499,872.2,346230,1272,1310,373271,14007280,8639936081,144,0,0,0,159530057,11,15577,4,3,15657,3,15657,0,31314,19008,432,20 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+1,15,1,64,0,58,58,58,58.0,2,1,1,50,0,0,0,0,0,0,0,0,2306,0,0,0,0,0,0,0,0,0,0 diff --git a/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_sql_metrics_agg_expectation.csv b/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_sql_metrics_agg_expectation.csv new file mode 100644 index 000000000..63aaf7a1b --- /dev/null +++ b/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_sql_metrics_agg_expectation.csv @@ -0,0 +1,2 @@ +appIndex,appID,sqlID,description,numTasks,Duration,executorCPUTime,executorRunTime,executorCPURatio,diskBytesSpilled_sum,duration_sum,duration_max,duration_min,duration_avg,executorCPUTime_sum,executorDeserializeCPUTime_sum,executorDeserializeTime_sum,executorRunTime_sum,input_bytesRead_sum,input_recordsRead_sum,jvmGCTime_sum,memoryBytesSpilled_sum,output_bytesWritten_sum,output_recordsWritten_sum,peakExecutionMemory_max,resultSerializationTime_sum,resultSize_max,sr_fetchWaitTime_sum,sr_localBlocksFetched_sum,sr_localBytesRead_sum,sr_remoteBlocksFetched_sum,sr_remoteBytesRead_sum,sr_remoteBytesReadToDisk_sum,sr_totalBytesRead_sum,sw_bytesWritten_sum,sw_recordsWritten_sum,sw_writeTime_sum +1,"app-20240919162642-0000",26,"query88",3472,250542,2883837,3818106,75.53,0,3858136,6743,54,1111.2,2883837,12349,18186,3818106,52997115316,69120188398,16100,0,0,0,250840493,181,16203,1394,1759,201596,1750,201200,0,402796,218614,19946,154 diff --git a/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_stage_metrics_agg_expectation.csv b/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_stage_metrics_agg_expectation.csv new file mode 100644 index 000000000..ae34c1ac0 --- /dev/null +++ b/core/src/test/resources/ProfilingExpectations/nds_q88_photon_db_13_3_stage_metrics_agg_expectation.csv @@ -0,0 +1,58 @@ +appIndex,stageId,numTasks,Duration,diskBytesSpilled_sum,duration_sum,duration_max,duration_min,duration_avg,executorCPUTime_sum,executorDeserializeCPUTime_sum,executorDeserializeTime_sum,executorRunTime_sum,input_bytesRead_sum,input_recordsRead_sum,jvmGCTime_sum,memoryBytesSpilled_sum,output_bytesWritten_sum,output_recordsWritten_sum,peakExecutionMemory_max,resultSerializationTime_sum,resultSize_max,sr_fetchWaitTime_sum,sr_localBlocksFetched_sum,sr_localBytesRead_sum,sr_remoteBlocksFetched_sum,sr_remoteBytesRead_sum,sr_remoteBytesReadToDisk_sum,sr_totalBytesRead_sum,sw_bytesWritten_sum,sw_recordsWritten_sum,sw_writeTime_sum +1,58,431,237799,0,371230,1032,333,861.3,343288,1261,1286,367785,190131859,8639936081,160,0,0,0,169667947,21,15767,8,3,15657,3,15657,0,31314,18964,431,16 +1,54,432,214633,0,376777,1133,499,872.2,346230,1272,1310,373271,14007280,8639936081,144,0,0,0,159530057,11,15577,4,3,15657,3,15657,0,31314,19008,432,20 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+1,15,1,62,0,58,58,58,58.0,2,1,1,50,0,0,0,0,0,0,0,0,2306,0,0,0,0,0,0,0,0,0,0 diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AnalysisSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AnalysisSuite.scala index 409b6bde3..b247433a4 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AnalysisSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AnalysisSuite.scala @@ -19,7 +19,7 @@ package com.nvidia.spark.rapids.tool.profiling import java.io.File import com.nvidia.spark.rapids.tool.ToolTestUtils -import com.nvidia.spark.rapids.tool.views.RawMetricProfilerView +import com.nvidia.spark.rapids.tool.views.{ProfDataSourceView, RawMetricProfilerView} import org.scalatest.FunSuite import org.apache.spark.sql.{DataFrame, SparkSession} @@ -66,6 +66,15 @@ class AnalysisSuite extends FunSuite { expectFile("sql"), expectFile("job"), expectFile("stage")) } + test("test photon sql metrics aggregation") { + val fileName = "nds_q88_photon_db_13_3" + val expectFile = (metric: String) => { + s"${fileName}_${metric}_metrics_agg_expectation.csv" + } + testSqlMetricsAggregation(Array(s"${qualLogDir}/${fileName}.zstd"), + expectFile("sql"), expectFile("job"), expectFile("stage")) + } + private def testSqlMetricsAggregation(logs: Array[String], expectFileSQL: String, expectFileJob: String, expectFileStage: String): Unit = { val apps = ToolTestUtils.processProfileApps(logs, sparkSession) @@ -158,4 +167,12 @@ class AnalysisSuite extends FunSuite { val metrics = aggResults.sqlDurAggs metrics.foreach(m => assert(m.appDuration.get == 0L)) } + + test("test photon scan metrics") { + val fileName = "nds_q88_photon_db_13_3" + val logs = Array(s"${qualLogDir}/${fileName}.zstd") + val apps = ToolTestUtils.processProfileApps(logs, sparkSession) + val dataSourceResults = ProfDataSourceView.getRawView(apps) + assert(dataSourceResults.exists(_.scan_time > 0)) + } } From 730a05dc7b56750d2805ccb5d3261fe6fa938433 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Mon, 28 Oct 2024 09:35:22 -0700 Subject: [PATCH 22/24] Update error handling in python for parsing cluster information (#1394) * Handle invalid values for num worker nodes and num execs per node Signed-off-by: Partho Sarthi * Fix pylint Signed-off-by: Partho Sarthi --------- Signed-off-by: Partho Sarthi --- .../common/cluster_inference.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/common/cluster_inference.py b/user_tools/src/spark_rapids_pytools/common/cluster_inference.py index 6d8ac3db1..e5f708471 100644 --- a/user_tools/src/spark_rapids_pytools/common/cluster_inference.py +++ b/user_tools/src/spark_rapids_pytools/common/cluster_inference.py @@ -76,6 +76,12 @@ def _get_cluster_template_args(self, cluster_info_df: pd.Series) -> Optional[dic num_driver_nodes = 1 app_id = cluster_info_df.get('App ID') num_worker_nodes = cluster_info_df.get('Num Worker Nodes') + # If number of worker nodes is invalid, log error and return + if pd.isna(num_worker_nodes) or num_worker_nodes <= 0: + self._log_inference_failure(app_id, 'Number of worker nodes cannot be determined. ' + 'See logs for details.') + return None + cores_per_executor = cluster_info_df.get('Cores Per Executor') execs_per_node = cluster_info_df.get('Num Executors Per Node') total_cores_per_node = execs_per_node * cores_per_executor @@ -85,6 +91,11 @@ def _get_cluster_template_args(self, cluster_info_df: pd.Series) -> Optional[dic 'NUM_WORKER_NODES': int(num_worker_nodes), } if self.platform.get_platform_name() == CspEnv.ONPREM: + # For on-prem, if total cores per node is invalid, log error and return + if pd.isna(total_cores_per_node) or total_cores_per_node <= 0: + self._log_inference_failure(app_id, 'Total cores per node cannot be determined. ' + 'See logs for details.') + return None # For on-prem, we need to include number of cores per worker node cluster_prop['NUM_WORKER_CORES'] = int(total_cores_per_node) else: @@ -95,9 +106,10 @@ def _get_cluster_template_args(self, cluster_info_df: pd.Series) -> Optional[dic driver_node_type = self.platform.configs.get_value('clusterInference', 'defaultCpuInstances', 'driver') worker_node_type = cluster_info_df.get('Worker Node Type') if pd.isna(worker_node_type): - # If worker instance is not set, use the default value based on the number of cores - if pd.isna(total_cores_per_node): - self._log_inference_failure(app_id, 'Total cores per node cannot be determined.') + # For CSPs, if worker instance is not set and total cores per node is invalid, log error and return + if pd.isna(total_cores_per_node) or total_cores_per_node <= 0: + self._log_inference_failure(app_id, 'Total cores per node cannot be determined. ' + 'See logs for details.') return None # TODO - need to account for number of GPUs per executor worker_node_type = self.platform.get_matching_worker_node_type(total_cores_per_node) From e1c47420cb48ca372ff4b4d000a372bb42d3ff83 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Thu, 31 Oct 2024 16:03:20 -0500 Subject: [PATCH 23/24] Reduce default memory allocation to the java process (#1407) * Reduce default memory allocation to the java process Signed-off-by: Ahmed Hussein Fixes #1406 * change cap of the xmx to 32 GB Signed-off-by: Ahmed Hussein --------- Signed-off-by: Ahmed Hussein --- .../spark_rapids_tools/cmdli/argprocessor.py | 4 +- .../src/spark_rapids_tools/utils/util.py | 43 ++++++++++++++----- 2 files changed, 34 insertions(+), 13 deletions(-) diff --git a/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py b/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py index e578d23b6..0ab8219e6 100644 --- a/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py +++ b/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py @@ -359,9 +359,9 @@ def process_jvm_args(self) -> None: jvm_heap = self.jvm_heap_size if jvm_heap is None: # set default GC heap size based on the virtual memory of the host. - jvm_heap = Utilities.get_system_memory_in_gb() + jvm_heap = Utilities.calculate_jvm_max_heap_in_gb() # check if both tools are going to run concurrently, then we need to reduce the heap size - # To reduce possibility of OOME, each core-tools thread should be running with at least 6 GB + # To reduce possibility of OOME, each core-tools thread should be running with at least 8 GB # of heap. adjusted_resources = Utilities.adjust_tools_resources(jvm_heap, jvm_processes=2 if self.is_concurrent_submission() else 1, diff --git a/user_tools/src/spark_rapids_tools/utils/util.py b/user_tools/src/spark_rapids_tools/utils/util.py index cc449a4d0..7ceb81ee5 100644 --- a/user_tools/src/spark_rapids_tools/utils/util.py +++ b/user_tools/src/spark_rapids_tools/utils/util.py @@ -175,8 +175,14 @@ def init_environment(short_name: str): class Utilities: """Utility class used to enclose common helpers and utilities.""" + # Assume that the minimum xmx jvm heap allowed to the java cmd is 8 GB. + min_jvm_xmx: ClassVar[int] = 8 + # Assume that the maximum xmx jvm heap allowed to the java cmd is 32 GB. + max_jvm_xmx: ClassVar[int] = 32 # Assume that any tools thread would need at least 8 GB of heap memory. min_jvm_heap_per_thread: ClassVar[int] = 8 + # Assume that maximum allowed number of threads to be passed to the tools java cmd is 8. + max_tools_threads: ClassVar[int] = 8 # Flag used to disable running tools in parallel. This is a temporary hack to reduce possibility # of OOME. Later we can re-enable it. conc_mode_enabled: ClassVar[bool] = False @@ -228,7 +234,7 @@ def reformat_release_version(cls, defined_version: Version) -> str: def get_base_release(cls) -> str: """ For now the tools_jar is always with major.minor.0. - this method makes sure that even if the package version is incremented, we will still + This method makes sure that even if the package version is incremented, we will still get the correct url. :return: a string containing the release number 22.12.0, 23.02.0, amd 23.04.0..etc """ @@ -244,22 +250,37 @@ def get_valid_df_columns(cls, input_cols, input_df: pd.DataFrame) -> list: return [col for col in input_cols if col in input_df.columns] @classmethod - def get_system_memory_in_gb(cls) -> int: + def calculate_jvm_max_heap_in_gb(cls) -> int: """ - Get the total system memory in GB. Ideally we only grab 80% of teh total-memory + Calculates the maximum heap size to pass to the java cmd based on the memory system. + By default, the calculation should not be too aggressive because it would lead to reserving + large memory from the system. In some environments, the OOM killer might kill the tools + process when the OS runs out of resources. + To achieve this, we calculate the heap based on the available memory + (not total memory) capping the value to 32 GB. + :return: The maximum JVM heap size in GB. It is in the range [8-32] GB. """ ps_memory = psutil.virtual_memory() - return int(0.8 * ps_memory.total / (1024 ** 3)) + # get the available memory in the system + available_sys_gb = ps_memory.available / (1024 ** 3) + # set the max heap to 30% of total available memory + heap_based_on_sys = int(0.3 * available_sys_gb) + # enforce the xmx heap argument to be in the range [8, 32] GB + return max(cls.min_jvm_xmx, min(heap_based_on_sys, cls.max_jvm_xmx)) @classmethod - def get_max_jvm_threads(cls) -> int: + def calculate_max_tools_threads(cls) -> int: """ - Get the total cpu_count. + Calculates the maximum number of threads that can be passed to the tools' java cmd based on + the cores of the system. We cap it to 8 threads to reduce teh risk of OOME on the java side. + :return: The maximum thread pool size in the tools' java cmd in the range [1, 8]. """ - # Maximum number of threads that can be used in the tools JVM. - # cpu_count returns the logical number of cores. So, we take a 50% to get better representation - # of physical cores. - return min(3, (psutil.cpu_count() + 1) // 2) + # Get the number of physical cores in the system. The logical cores is usually higher, + # but we are being a little bit conservative here to avoid running high number of threads concurrently. + # Note that on MacOS, the result of both physical/logical count is the same. + physical_cores = psutil.cpu_count(logical=False) + # Enforce a safe range [1, 8] + return max(1, min(cls.max_tools_threads, physical_cores)) @classmethod def adjust_tools_resources(cls, @@ -275,7 +296,7 @@ def adjust_tools_resources(cls, concurrent_mode = cls.conc_mode_enabled and jvm_processes > 1 heap_unit = max(cls.min_jvm_heap_per_thread, jvm_heap // 3 if concurrent_mode else jvm_heap) # calculate the maximum number of threads. - upper_threads = cls.get_max_jvm_threads() // 3 if concurrent_mode else cls.get_max_jvm_threads() + upper_threads = cls.calculate_max_tools_threads() // 3 if concurrent_mode else cls.calculate_max_tools_threads() if jvm_threads is None: # make sure that the qual threads cannot exceed maximum allowed threads num_threads_unit = min(upper_threads, max(1, heap_unit // cls.min_jvm_heap_per_thread)) From ca6cac6c3abd0b1cd73e818ee7b6f73902989ec1 Mon Sep 17 00:00:00 2001 From: Ahmed Hussein <50450311+amahussein@users.noreply.github.com> Date: Mon, 4 Nov 2024 11:34:48 -0600 Subject: [PATCH 24/24] [FEA] Allow users to specify custom Dependency jars (#1395) * Allow users to specify custom Dependency jars Signed-off-by: Ahmed Hussein Fixes #1359 Add a new input argument that takes a path to a yaml file `--tools_config_file` The config file allows the users to define their own binaries that need to be added to the classpath of the tools jar cmd. This change is important because users can use the user-tools wrapper with their custom spark. * fix typo in aws configs Signed-off-by: Ahmed Hussein * fix default dependencyType Signed-off-by: Ahmed Hussein * add unit tests for toolsconfig Signed-off-by: Ahmed Hussein (amahussein) * improve field descriptions Signed-off-by: Ahmed Hussein * address review comments Signed-off-by: Ahmed Hussein --------- Signed-off-by: Ahmed Hussein Signed-off-by: Ahmed Hussein (amahussein) --- .../rapids/rapids_tool.py | 74 ++++--- .../resources/databricks_aws-configs.json | 42 ++-- .../resources/databricks_azure-configs.json | 34 ++-- .../resources/dataproc-configs.json | 34 ++-- .../resources/dataproc_gke-configs.json | 34 ++-- .../resources/dev/prepackage_mgr.py | 38 ++-- .../resources/emr-configs.json | 48 ++--- .../resources/onprem-configs.json | 30 +-- .../spark_rapids_tools/cmdli/argprocessor.py | 29 ++- .../src/spark_rapids_tools/cmdli/tools_cli.py | 14 +- .../configuration/__init__.py | 23 +++ .../configuration/common.py | 74 +++++++ .../configuration/runtime_conf.py | 30 +++ .../configuration/tools_config.py | 55 ++++++ user_tools/src/spark_rapids_tools/enums.py | 14 +- .../storagelib/tools/fs_utils.py | 20 +- .../tests/spark_rapids_tools_ut/conftest.py | 22 ++- .../invalid/tools_config_inv_00.yaml | 11 ++ .../invalid/tools_config_inv_01.yaml | 8 + .../invalid/tools_config_inv_02.yaml | 17 ++ .../sample-config-specification.json | 182 ++++++++++++++++++ .../tools_config/valid/tools_config_00.yaml | 11 ++ .../test_tool_argprocessor.py | 38 +++- 23 files changed, 716 insertions(+), 166 deletions(-) create mode 100644 user_tools/src/spark_rapids_tools/configuration/__init__.py create mode 100644 user_tools/src/spark_rapids_tools/configuration/common.py create mode 100644 user_tools/src/spark_rapids_tools/configuration/runtime_conf.py create mode 100644 user_tools/src/spark_rapids_tools/configuration/tools_config.py create mode 100644 user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_00.yaml create mode 100644 user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_01.yaml create mode 100644 user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_02.yaml create mode 100644 user_tools/tests/spark_rapids_tools_ut/resources/tools_config/sample-config-specification.json create mode 100644 user_tools/tests/spark_rapids_tools_ut/resources/tools_config/valid/tools_config_00.yaml diff --git a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py index 5f8d5cf1a..ee4338ccd 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py +++ b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py @@ -38,9 +38,11 @@ from spark_rapids_pytools.rapids.rapids_job import RapidsJobPropContainer from spark_rapids_pytools.rapids.tool_ctxt import ToolContext from spark_rapids_tools import CspEnv -from spark_rapids_tools.enums import HashAlgorithm +from spark_rapids_tools.configuration.common import RuntimeDependency +from spark_rapids_tools.configuration.tools_config import ToolsConfig +from spark_rapids_tools.enums import DependencyType from spark_rapids_tools.storagelib import LocalPath, CspFs -from spark_rapids_tools.storagelib.tools.fs_utils import untar_file, FileHashAlgorithm +from spark_rapids_tools.storagelib.tools.fs_utils import untar_file from spark_rapids_tools.utils import Utilities from spark_rapids_tools.utils.net_utils import DownloadTask @@ -70,6 +72,13 @@ class RapidsTool(object): logger: Logger = field(default=None, init=False) spinner: ToolsSpinner = field(default=None, init=False) + def get_tools_config_obj(self) -> Optional['ToolsConfig']: + """ + Get the tools configuration object if provided in the CLI arguments. + :return: An object containing all the tools configuration or None if not provided. + """ + return self.wrapper_options.get('toolsConfig') + def pretty_name(self): return self.name.capitalize() @@ -136,7 +145,7 @@ def _check_environment(self) -> None: def _process_output_args(self): self.logger.debug('Processing Output Arguments') - # make sure that output_folder is being absolute + # make sure output_folder is absolute if self.output_folder is None: self.output_folder = Utils.get_rapids_tools_env('OUTPUT_DIRECTORY', os.getcwd()) try: @@ -393,7 +402,8 @@ def _calculate_spark_settings(self, worker_info: NodeHWInfo) -> dict: return res @classmethod - def get_rapids_tools_dependencies(cls, deploy_mode: str, json_props: AbstractPropertiesContainer) -> Optional[list]: + def get_rapids_tools_dependencies(cls, deploy_mode: str, + json_props: AbstractPropertiesContainer) -> Optional[list]: """ Get the tools dependencies from the platform configuration. """ @@ -403,7 +413,9 @@ def get_rapids_tools_dependencies(cls, deploy_mode: str, json_props: AbstractPro depend_arr = json_props.get_value_silent('dependencies', 'deployMode', deploy_mode, active_buildver) if depend_arr is None: raise ValueError(f'Invalid SPARK dependency version [{active_buildver}]') - return depend_arr + # convert the json array to a list of RuntimeDependency objects + runtime_dep_arr = [RuntimeDependency(**dep) for dep in depend_arr] + return runtime_dep_arr @dataclass @@ -532,47 +544,46 @@ def exception_handler(future): if exception: self.logger.error('Error while downloading dependency: %s', exception) - def cache_single_dependency(dep: dict) -> str: + def cache_single_dependency(dep: RuntimeDependency) -> str: """ Downloads the specified URL and saves it to disk """ - self.logger.info('Checking dependency %s', dep['name']) + self.logger.info('Checking dependency %s', dep.name) dest_folder = self.ctxt.get_cache_folder() verify_opts = {} - dep_verification = dep.get('verification') - if dep_verification is not None: - if 'size' in dep_verification: - verify_opts['size'] = dep_verification['size'] - hash_lib_alg = dep_verification.get('hashLib') - if hash_lib_alg: - verify_opts['file_hash'] = FileHashAlgorithm(HashAlgorithm(hash_lib_alg['type']), - hash_lib_alg['value']) - download_task = DownloadTask(src_url=dep['uri'], # pylint: disable=no-value-for-parameter) + if dep.verification is not None: + verify_opts = dict(dep.verification) + download_task = DownloadTask(src_url=dep.uri, # pylint: disable=no-value-for-parameter) dest_folder=dest_folder, verification=verify_opts) download_result = download_task.run_task() self.logger.info('Completed downloading of dependency [%s] => %s', - dep['name'], + dep.name, f'{download_result.pretty_print()}') if not download_result.success: - msg = f'Failed to download dependency {dep["name"]}, reason: {download_result.download_error}' + msg = f'Failed to download dependency {dep.name}, reason: {download_result.download_error}' raise RuntimeError(f'Could not download all dependencies. Aborting Executions.\n\t{msg}') destination_path = self.ctxt.get_local_work_dir() destination_cspath = LocalPath(destination_path) - if dep['type'] == 'archive': + # set the default dependency type to jar + defined_dep_type = DependencyType.get_default() + if dep.dependency_type: + defined_dep_type = dep.dependency_type.dep_type + if defined_dep_type == DependencyType.ARCHIVE: uncompressed_cspath = untar_file(download_result.resource, destination_cspath) dep_item = uncompressed_cspath.no_scheme - relative_path = dep.get('relativePath') - if relative_path is not None: - dep_item = f'{dep_item}/{relative_path}' - else: + if dep.dependency_type.relative_path is not None: + dep_item = f'{dep_item}/{dep.dependency_type.relative_path}' + elif defined_dep_type == DependencyType.JAR: # copy the jar into dependency folder CspFs.copy_resources(download_result.resource, destination_cspath) final_dep_csp = destination_cspath.create_sub_path(download_result.resource.base_name()) dep_item = final_dep_csp.no_scheme + else: + raise ValueError(f'Invalid dependency type [{defined_dep_type}]') return dep_item - def cache_all_dependencies(dep_arr: List[dict]): + def cache_all_dependencies(dep_arr: List[RuntimeDependency]) -> List[str]: """ Create a thread pool and download specified urls """ @@ -593,8 +604,19 @@ def cache_all_dependencies(dep_arr: List[dict]): raise ex return results - deploy_mode = DeployMode.tostring(self.ctxt.get_deploy_mode()) - depend_arr = self.get_rapids_tools_dependencies(deploy_mode, self.ctxt.platform.configs) + def populate_dependency_list() -> List[RuntimeDependency]: + # check if the dependencies is defined in a config file + config_obj = self.get_tools_config_obj() + if config_obj is not None: + if config_obj.runtime.dependencies: + return config_obj.runtime.dependencies + self.logger.info('The ToolsConfig did not specify the dependencies. ' + 'Falling back to the default dependencies.') + # load dependency list from the platform configuration + deploy_mode = DeployMode.tostring(self.ctxt.get_deploy_mode()) + return self.get_rapids_tools_dependencies(deploy_mode, self.ctxt.platform.configs) + + depend_arr = populate_dependency_list() if depend_arr: dep_list = cache_all_dependencies(depend_arr) if any(dep_item is None for dep_item in dep_list): diff --git a/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json b/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json index 7d64dfb44..d0cde1298 100644 --- a/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/databricks_aws-configs.json @@ -8,21 +8,23 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" }, "size": 400395283 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "a65839fbf1869f81a1632e09f415e586922e4f80" }, "size": 962685 @@ -33,8 +35,8 @@ "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" }, "size": 280645251 @@ -47,38 +49,38 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" }, "size": 299426263 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "a65839fbf1869f81a1632e09f415e586922e4f80" }, "size": 962685 - }, - "type": "jar" + } }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" }, "size": 280645251 - }, - "type": "jar" + } } ] } diff --git a/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json b/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json index 0e41fb1ae..4716a60d7 100644 --- a/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/databricks_azure-configs.json @@ -8,26 +8,27 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" }, "size": 400395283 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "Hadoop Azure", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-azure/3.3.4/hadoop-azure-3.3.4.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "a23f621bca9b2100554150f6b0b521f94b8b419e" }, "size": 574116 - }, - "type": "jar" + } } ], "333": [ @@ -35,26 +36,27 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" }, "size": 299426263 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "Hadoop Azure", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-azure/3.3.4/hadoop-azure-3.3.4.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "a23f621bca9b2100554150f6b0b521f94b8b419e" }, "size": 574116 - }, - "type": "jar" + } } ] } diff --git a/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json b/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json index 1f99e7bac..87034191b 100644 --- a/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/dataproc-configs.json @@ -8,26 +8,27 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" }, "size": 400395283 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.19/gcs-connector-hadoop3-2.2.19-shaded.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "3bea6d5e62663a2a5c03d8ca44dff4921aeb3170" }, "size": 39359477 - }, - "type": "jar" + } } ], "333": [ @@ -35,26 +36,27 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" }, "size": 299426263 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.17/gcs-connector-hadoop3-2.2.17-shaded.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "06438f562692ff8fae5e8555eba2b9f95cb74f66" }, "size": 38413466 - }, - "type": "jar" + } } ] } diff --git a/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json b/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json index 861b127d7..2fd4f2344 100644 --- a/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/dataproc_gke-configs.json @@ -8,26 +8,27 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" }, "size": 400395283 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.19/gcs-connector-hadoop3-2.2.19-shaded.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "3bea6d5e62663a2a5c03d8ca44dff4921aeb3170" }, "size": 39359477 - }, - "type": "jar" + } } ], "333": [ @@ -35,26 +36,27 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" }, "size": 299426263 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "GCS Connector Hadoop3", "uri": "https://repo1.maven.org/maven2/com/google/cloud/bigdataoss/gcs-connector/hadoop3-2.2.17/gcs-connector-hadoop3-2.2.17-shaded.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "06438f562692ff8fae5e8555eba2b9f95cb74f66" }, "size": 38413466 - }, - "type": "jar" + } } ] } diff --git a/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py b/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py index 1920c134b..1a196cc93 100644 --- a/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py +++ b/user_tools/src/spark_rapids_pytools/resources/dev/prepackage_mgr.py @@ -18,7 +18,6 @@ """ import os -import shutil import tarfile from typing import Optional @@ -27,7 +26,8 @@ from spark_rapids_pytools.common.prop_manager import JSONPropertiesContainer from spark_rapids_pytools.common.sys_storage import FSUtil from spark_rapids_pytools.rapids.rapids_tool import RapidsTool -from spark_rapids_tools import CspEnv +from spark_rapids_tools import CspEnv, CspPath +from spark_rapids_tools.configuration.common import RuntimeDependency from spark_rapids_tools.utils import Utilities from spark_rapids_tools.utils.net_utils import DownloadManager, DownloadTask @@ -100,16 +100,18 @@ def _fetch_resources(self) -> dict: # Add RAPIDS JAR as dependency if self.tools_jar: - # copy from existing file. replace snapshot - jar_file_name = FSUtil.get_resource_name(self.tools_jar) - FSUtil.make_dirs(self.dest_dir) - dest_file = FSUtil.build_path(self.dest_dir, jar_file_name) - shutil.copy2(self.tools_jar, dest_file) + # copy from existing file. + tools_jar_cspath = CspPath(self.tools_jar) + tools_jar_url = str(tools_jar_cspath) + jar_file_name = tools_jar_cspath.base_name() + print(f'Using the provided tools_jar {tools_jar_url}') else: - # get the latest tools_jar from mvn - rapids_url = self._get_spark_rapids_jar_url() - rapids_name = FSUtil.get_resource_name(rapids_url) - resource_uris[rapids_url] = {'name': rapids_name, 'pbar_enabled': False} + tools_jar_url = self._get_spark_rapids_jar_url() + jar_file_name = FSUtil.get_resource_name(tools_jar_url) + resource_uris[tools_jar_url] = { + 'depItem': RuntimeDependency(name=jar_file_name, uri=tools_jar_url), + 'prettyName': jar_file_name + } for platform in self._supported_platforms: # pylint: disable=no-member config_file = FSUtil.build_full_path(self.resource_dir, @@ -117,17 +119,19 @@ def _fetch_resources(self) -> dict: platform_conf = JSONPropertiesContainer(config_file) dependency_list = RapidsTool.get_rapids_tools_dependencies('LOCAL', platform_conf) for dependency in dependency_list: - uri = dependency.get('uri') - name = FSUtil.get_resource_name(uri) - if uri: - resource_uris[uri] = {'name': name, 'pbar_enabled': False} - resource_uris[uri + '.asc'] = {'name': name + '.asc', 'pbar_enabled': False} + if dependency.uri: + uri_str = str(dependency.uri) + pretty_name = FSUtil.get_resource_name(uri_str) + resource_uris[uri_str] = { + 'depItem': dependency, + 'prettyName': pretty_name + } return resource_uris def _download_resources(self, resource_uris: dict): download_tasks = [] for res_uri, res_info in resource_uris.items(): - resource_name = res_info.get('name') + resource_name = res_info.get('prettyName') print(f'Creating download task: {resource_name}') # All the downloadTasks enforces download download_tasks.append(DownloadTask(src_url=res_uri, # pylint: disable=no-value-for-parameter) diff --git a/user_tools/src/spark_rapids_pytools/resources/emr-configs.json b/user_tools/src/spark_rapids_pytools/resources/emr-configs.json index 5b6a34bd2..52b5097c2 100644 --- a/user_tools/src/spark_rapids_pytools/resources/emr-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/emr-configs.json @@ -8,38 +8,38 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" }, "size": 400395283 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "a65839fbf1869f81a1632e09f415e586922e4f80" }, "size": 962685 - }, - "type": "jar" + } }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" }, "size": 280645251 - }, - "type": "jar" + } } ], "333": [ @@ -47,38 +47,38 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" }, "size": 299426263 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } }, { "name": "Hadoop AWS", "uri": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "a65839fbf1869f81a1632e09f415e586922e4f80" }, "size": 962685 - }, - "type": "jar" + } }, { "name": "AWS Java SDK Bundled", "uri": "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar", "verification": { - "hashLib": { - "type": "sha1", + "fileHash": { + "algorithm": "sha1", "value": "02deec3a0ad83d13d032b1812421b23d7a961eea" }, "size": 280645251 - }, - "type": "jar" + } } ] } diff --git a/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json b/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json index f7e9af218..fe8f07d09 100644 --- a/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json +++ b/user_tools/src/spark_rapids_pytools/resources/onprem-configs.json @@ -8,14 +8,16 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319" }, "size": 400395283 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } } ], "342": [ @@ -23,14 +25,16 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.4.2/spark-3.4.2-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "c9470a557c96fe899dd1c9ea8d0dda3310eaf0155b2bb972f70a6d97fee8cdaf838b425c30df3d5856b2c31fc2be933537c111db72d0427eabb76c6abd92c1f1" }, "size": 388664780 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } } ], "333": [ @@ -38,14 +42,16 @@ "name": "Apache Spark", "uri": "https://archive.apache.org/dist/spark/spark-3.3.3/spark-3.3.3-bin-hadoop3.tgz", "verification": { - "hashLib": { - "type": "sha512", + "fileHash": { + "algorithm": "sha512", "value": "ebf79c7861f3120d5ed9465fdd8d5302a734ff30713a0454b714bbded7ab9f218b3108dc46a5de4cc2102c86e7be53908f84d2c7a19e59bc75880766eeefeef9" }, "size": 299426263 }, - "type": "archive", - "relativePath": "jars/*" + "dependencyType": { + "depType": "archive", + "relativePath": "jars/*" + } } ] } diff --git a/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py b/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py index 0ab8219e6..978f683c1 100644 --- a/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py +++ b/user_tools/src/spark_rapids_tools/cmdli/argprocessor.py @@ -29,6 +29,7 @@ from spark_rapids_pytools.common.utilities import ToolLogging, Utils from spark_rapids_tools.cloud import ClientCluster from spark_rapids_tools.utils import AbstractPropContainer, is_http_file +from ..configuration.tools_config import ToolsConfig from ..enums import QualFilterApp, CspEnv, QualEstimationModel from ..storagelib.csppath import CspPath from ..tools.autotuner import AutoTunerPropMgr @@ -349,6 +350,7 @@ class ToolUserArgModel(AbsToolUserArgModel): eventlogs: Optional[str] = None jvm_heap_size: Optional[int] = None jvm_threads: Optional[int] = None + tools_config_path: Optional[str] = None def is_concurrent_submission(self) -> bool: return False @@ -370,6 +372,26 @@ def process_jvm_args(self) -> None: self.p_args['toolArgs']['jobResources'] = adjusted_resources self.p_args['toolArgs']['log4jPath'] = Utils.resource_path('dev/log4j.properties') + def load_tools_config(self) -> None: + """ + Load the tools config file if it is provided. It creates a ToolsConfig object and sets it + in the toolArgs without processing the actual dependencies. + :return: None + """ + self.p_args['toolArgs']['toolsConfig'] = None + if self.tools_config_path is not None: + # the CLI provides a tools config file + try: + self.p_args['toolArgs']['toolsConfig'] = ToolsConfig.load_from_file(self.tools_config_path) + except ValidationError as ve: + # If required, we can dump the expected specification by appending + # 'ToolsConfig.get_schema()' to the error message + raise PydanticCustomError( + 'invalid_config', + f'Tools config file path {self.tools_config_path} could not be loaded. ' + 'It is expected to be a valid configuration YAML file.' + f'\n Error:{ve}\n') from ve + def init_extra_arg_cases(self) -> list: if self.eventlogs is None: return [ArgValueCase.UNDEFINED] @@ -481,11 +503,14 @@ def build_tools_args(self) -> dict: runtime_platform = self.get_or_set_platform() # process JVM arguments self.process_jvm_args() + # process the tools config file + self.load_tools_config() # finally generate the final values wrapped_args = { 'runtimePlatform': runtime_platform, 'outputFolder': self.output_folder, + 'toolsConfig': self.p_args['toolArgs']['toolsConfig'], 'platformOpts': { 'credentialFile': None, 'deployMode': DeployMode.LOCAL @@ -601,10 +626,12 @@ def build_tools_args(self) -> dict: # process JVM arguments self.process_jvm_args() - + # process the tools config file + self.load_tools_config() # finally generate the final values wrapped_args = { 'runtimePlatform': runtime_platform, + 'toolsConfig': self.p_args['toolArgs']['toolsConfig'], 'outputFolder': self.output_folder, 'platformOpts': { 'credentialFile': None, diff --git a/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py b/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py index 9c6d6c214..0d46e5025 100644 --- a/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py +++ b/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py @@ -45,6 +45,7 @@ def qualification(self, jvm_heap_size: int = None, jvm_threads: int = None, verbose: bool = None, + tools_config_file: str = None, **rapids_options) -> None: """The Qualification cmd provides estimated speedups by migrating Apache Spark applications to GPU accelerated clusters. @@ -79,6 +80,9 @@ def qualification(self, :param jvm_threads: Number of threads to use for parallel processing on the eventlogs batch. Default is calculated as a function of the total number of cores and the heap size on the host. :param verbose: True or False to enable verbosity of the script. + :param tools_config_file: Path to a configuration file that contains the tools' options. + For sample configuration files, please visit + https://github.com/NVIDIA/spark-rapids-tools/tree/main/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/valid :param rapids_options: A list of valid Qualification tool options. Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. @@ -112,7 +116,8 @@ def qualification(self, jvm_heap_size=jvm_heap_size, jvm_threads=jvm_threads, filter_apps=filter_apps, - estimation_model_args=estimation_model_args) + estimation_model_args=estimation_model_args, + tools_config_path=tools_config_file) if qual_args: tool_obj = QualificationAsLocal(platform_type=qual_args['runtimePlatform'], output_folder=qual_args['outputFolder'], @@ -131,6 +136,7 @@ def profiling(self, jvm_heap_size: int = None, jvm_threads: int = None, verbose: bool = None, + tools_config_file: str = None, **rapids_options): """The Profiling cmd provides information which can be used for debugging and profiling Apache Spark applications running on GPU accelerated clusters. @@ -158,6 +164,9 @@ def profiling(self, :param jvm_threads: Number of thread to use for parallel processing on the eventlogs batch. Default is calculated as a function of the total number of cores and the heap size on the host. :param verbose: True or False to enable verbosity of the script. + :param tools_config_file: Path to a configuration file that contains the tools' options. + For sample configuration files, please visit + https://github.com/NVIDIA/spark-rapids-tools/tree/main/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/valid :param rapids_options: A list of valid Profiling tool options. Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. @@ -182,7 +191,8 @@ def profiling(self, jvm_heap_size=jvm_heap_size, jvm_threads=jvm_threads, output_folder=output_folder, - tools_jar=tools_jar) + tools_jar=tools_jar, + tools_config_path=tools_config_file) if prof_args: rapids_options.update(prof_args['rapidOptions']) tool_obj = ProfilingAsLocal(platform_type=prof_args['runtimePlatform'], diff --git a/user_tools/src/spark_rapids_tools/configuration/__init__.py b/user_tools/src/spark_rapids_tools/configuration/__init__.py new file mode 100644 index 000000000..de974a8e6 --- /dev/null +++ b/user_tools/src/spark_rapids_tools/configuration/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""init file of the tools configurations module""" + +from .common import RuntimeDependencyType, DependencyVerification, RuntimeDependency + +__all__ = [ + 'RuntimeDependency', + 'RuntimeDependencyType', + 'DependencyVerification' +] diff --git a/user_tools/src/spark_rapids_tools/configuration/common.py b/user_tools/src/spark_rapids_tools/configuration/common.py new file mode 100644 index 000000000..439904cae --- /dev/null +++ b/user_tools/src/spark_rapids_tools/configuration/common.py @@ -0,0 +1,74 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Common types and definitions used by the configurations. This module is used by other +modules as well.""" + +from typing import Union +from pydantic import BaseModel, Field, AnyUrl, FilePath, AliasChoices + +from spark_rapids_tools.enums import DependencyType +from spark_rapids_tools.storagelib.tools.fs_utils import FileHashAlgorithm + + +class RuntimeDependencyType(BaseModel): + """Defines the type of runtime dependency required by the tools' java cmd.""" + + dep_type: DependencyType = Field( + description='The type of the dependency.', + validation_alias=AliasChoices('dep_type', 'depType')) + relative_path: str = Field( + default=None, + description='Specifies the relative path from within the archive file which will be added to the java cmd. ' + 'Requires field dep_type to be set to (archive).', + validation_alias=AliasChoices('relative_path', 'relativePath'), + examples=['jars/*']) + + +class DependencyVerification(BaseModel): + """The verification information of a runtime dependency required by the tools' java cmd.""" + size: int = Field( + default=0, + description='The size of the dependency file.', + examples=[3265393]) + file_hash: FileHashAlgorithm = Field( + default=None, + description='The hash function to verify the file.', + validation_alias=AliasChoices('file_hash', 'fileHash'), + examples=[ + { + 'algorithm': 'md5', + 'value': 'bc9bf7fedde0e700b974426fbd8d869c' + }]) + + +class RuntimeDependency(BaseModel): + """Holds information about a runtime dependency required by the tools' java cmd.""" + name: str = Field( + description='The name of the dependency.', + examples=['Spark-3.5.0', 'AWS Java SDK']) + uri: Union[AnyUrl, FilePath] = Field( + description='The location of the dependency file. It can be a URL to a remote web/storage or a file path.', + examples=['file:///path/to/file.tgz', + 'https://mvn-url/24.08.1/rapids-4-spark-tools_2.12-24.08.1.jar', + 'gs://bucket-name/path/to/file.jar']) + dependency_type: RuntimeDependencyType = Field( + default_factory=lambda: RuntimeDependencyType(dep_type=DependencyType.get_default()), + description='Specifies the dependency type to determine how the item is processed. ' + 'For example, jar files are appended to the java classpath while archive files ' + 'such as spark are extracted first before adding subdirectory _/jars/* to the classpath.', + validation_alias=AliasChoices('dependency_type', 'dependencyType')) + verification: DependencyVerification = Field( + default=None, + description='Optional specification to verify the dependency file.') diff --git a/user_tools/src/spark_rapids_tools/configuration/runtime_conf.py b/user_tools/src/spark_rapids_tools/configuration/runtime_conf.py new file mode 100644 index 000000000..40ab68cf7 --- /dev/null +++ b/user_tools/src/spark_rapids_tools/configuration/runtime_conf.py @@ -0,0 +1,30 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""The runtime configurations of the tools as defined by the user.""" + +from typing import List + +from pydantic import BaseModel, Field + +from spark_rapids_tools.configuration.common import RuntimeDependency + + +class ToolsRuntimeConfig(BaseModel): + """The runtime configurations of the tools as defined by the user.""" + dependencies: List[RuntimeDependency] = Field( + description='The list of runtime dependencies required by the tools java cmd. ' + 'Set this list to specify Spark binaries along with any other required jar ' + 'files (i.e., hadoop jars, gcp connectors,..etc.). ' + 'When specified, the default predefined dependencies will be ignored.') diff --git a/user_tools/src/spark_rapids_tools/configuration/tools_config.py b/user_tools/src/spark_rapids_tools/configuration/tools_config.py new file mode 100644 index 000000000..b330cfc28 --- /dev/null +++ b/user_tools/src/spark_rapids_tools/configuration/tools_config.py @@ -0,0 +1,55 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Container for the custom tools configurations. This is the parts of the configuration that can +be passed as an input to the CLI""" + +import json +from typing import Union, Optional + +from pydantic import BaseModel, Field, ValidationError + +from spark_rapids_tools import CspPathT +from spark_rapids_tools.configuration.runtime_conf import ToolsRuntimeConfig +from spark_rapids_tools.utils import AbstractPropContainer + + +class ToolsConfig(BaseModel): + """Main container for the user's defined tools configuration""" + api_version: float = Field( + description='The version of the API that the tools are using. ' + 'This is used to test the compatibility of the ' + 'configuration file against the current tools release.', + examples=['1.0'], + le=1.0, # minimum version compatible with the current tools implementation + ge=1.0) + runtime: ToolsRuntimeConfig = Field( + description='Configuration related to the runtime environment of the tools.') + + @classmethod + def load_from_file(cls, file_path: Union[str, CspPathT]) -> Optional['ToolsConfig']: + """Load the tools configuration from a file""" + try: + prop_container = AbstractPropContainer.load_from_file(file_path) + return cls(**prop_container.props) + except ValidationError as e: + # Do nothing. This is kept as a place holder if we want to log the error inside the + # class first + raise e + + @classmethod + def get_schema(cls) -> str: + """Returns a JSON schema of the tools' configuration. This is useful for generating an API + documentation of the model.""" + return json.dumps(cls.model_json_schema(), indent=2) diff --git a/user_tools/src/spark_rapids_tools/enums.py b/user_tools/src/spark_rapids_tools/enums.py index 61dca23a6..46db8aad1 100644 --- a/user_tools/src/spark_rapids_tools/enums.py +++ b/user_tools/src/spark_rapids_tools/enums.py @@ -92,7 +92,7 @@ def _missing_(cls, value): return None def get_hash_func(self) -> Callable: - """Maps the hash function to the appropriate hashing algorithm""" + """Maps the hash function to the appropriate hashing algorithm.""" hash_functions = { self.MD5: hashlib.md5, self.SHA1: hashlib.sha1, @@ -101,6 +101,18 @@ def get_hash_func(self) -> Callable: } return hash_functions[self] + +class DependencyType(EnumeratedType): + """Represents the dependency type for the tools' java cmd.""" + JAR = 'jar' + ARCHIVE = 'archive' + + @classmethod + def get_default(cls) -> 'DependencyType': + """Returns the default dependency type.""" + return cls.JAR + + ########### # CSP Enums ########### diff --git a/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py b/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py index 58dc56ebc..f51bbec8d 100644 --- a/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py +++ b/user_tools/src/spark_rapids_tools/storagelib/tools/fs_utils.py @@ -67,19 +67,21 @@ def raise_invalid_file(file_path: CspPath, msg: str, error_type: str = 'invalid_ @dataclass class FileHashAlgorithm: """ - Represents a file hash algorithm and its value. Used for verification against an - existing file. - ```py - try: - file_algo = FileHashAlgorithm(algorithm=HashAlgorithm.SHA256, value='...') - file_algo.verify_file(CspPath('file://path/to/file')) - except ValidationError as e: - print(e) - ``` + Represents a file hash algorithm and its value. Used for verification against an existing file. """ algorithm: HashAlgorithm value: str + """ + Example usage for the class: + ```py + try: + file_algo = FileHashAlgorithm(algorithm=HashAlgorithm.SHA256, value='...') + file_algo.verify_file(CspPath('file://path/to/file')) + except ValidationError as e: + print(e) + ``` + """ def verify_file(self, file_path: CspPath) -> bool: cb = self.algorithm.get_hash_func() with file_path.open_input_stream() as stream: diff --git a/user_tools/tests/spark_rapids_tools_ut/conftest.py b/user_tools/tests/spark_rapids_tools_ut/conftest.py index 145355f24..de3f2da12 100644 --- a/user_tools/tests/spark_rapids_tools_ut/conftest.py +++ b/user_tools/tests/spark_rapids_tools_ut/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -47,6 +47,26 @@ def gen_cpu_cluster_props(): csps = ['dataproc', 'dataproc_gke', 'emr', 'databricks_aws', 'databricks_azure'] all_csps = csps + ['onprem'] autotuner_prop_path = 'worker_info.yaml' +# valid tools config files +valid_tools_conf_files = ['tools_config_00.yaml'] +# invalid tools config files +invalid_tools_conf_files = [ + # test older API_version + # Error:1 validation error for ToolsConfig + # api_version + # Input should be greater than or equal to 1 [type=greater_than_equal, input_value='0.9', input_type=str] + 'tools_config_inv_00.yaml', + # test empty runtime configs + # Error:1 validation error for ToolsConfig + # runtime.dependencies + # Input should be a valid list [type=list_type, input_value=None, input_type=NoneType] + 'tools_config_inv_01.yaml', + # test local dependency file does not exist + # Error:1 validation error for ToolsConfig + # runtime.dependencies + # Input should be a valid list [type=list_type, input_value=None, input_type=NoneType] + 'tools_config_inv_02.yaml' +] class SparkRapidsToolsUT: # pylint: disable=too-few-public-methods diff --git a/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_00.yaml b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_00.yaml new file mode 100644 index 000000000..aa566a71f --- /dev/null +++ b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_00.yaml @@ -0,0 +1,11 @@ +# This yaml file is a configuration file for a tool that uses Spark 3.5.0 as dependency +# invalid: API version is smaller than accepted API version +api_version: '0.9' +runtime: + dependencies: + - name: my-spark350 + uri: https:///archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz + dependency_type: + dep_type: archive + # for tgz files, it is required to give the subfolder where the jars are located + relative_path: jars/* diff --git a/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_01.yaml b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_01.yaml new file mode 100644 index 000000000..a723f2e66 --- /dev/null +++ b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_01.yaml @@ -0,0 +1,8 @@ +# This yaml file is a configuration file +# invalid: empty file +# Error:1 validation error for ToolsConfig +# runtime.dependencies +# Input should be a valid list [type=list_type, input_value=None, input_type=NoneType] +api_version: '1.0' +runtime: + dependencies: diff --git a/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_02.yaml b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_02.yaml new file mode 100644 index 000000000..210b4419a --- /dev/null +++ b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/invalid/tools_config_inv_02.yaml @@ -0,0 +1,17 @@ +# This yaml file is a configuration file for a tool that uses Spark 3.5.0 as dependency +# invalid: Local file can be verified during the initialization and it should fail because no such +# file exists. +# Error:2 validation errors for ToolsConfig +# runtime.dependencies.0.uri.url +# Input should be a valid URL, relative URL without a base [type=url_parsing, input_value='/home/user/workdir...k-3.5.0-bin-hadoop3.tgz', input_type=str] +# For further information visit https://errors.pydantic.dev/2.9/v/url_parsing +# runtime.dependencies.0.uri.function-after[validate_file(), lax-or-strict[lax=union[json-or-python[json=function-after[path_validator(), str],python=is-instance[Path]],function-after[path_validator(), str]],strict=json-or-python[json=function-after[path_validator(), str],python=is-instance[Path]]]] +# Path does not point to a file [type=path_not_file, input_value='/home/archive.apache.org...k-3.5.0-bin-hadoop3.tgz', input_type=str] +api_version: '1.0' +runtime: + dependencies: + - name: my-spark350 + uri: /home/user/workdir/spark-3.5.0-bin-hadoop3.tgz + dependency_type: + dep_type: archive + relative_path: jars/* diff --git a/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/sample-config-specification.json b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/sample-config-specification.json new file mode 100644 index 000000000..9dbef10ab --- /dev/null +++ b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/sample-config-specification.json @@ -0,0 +1,182 @@ +{ + "$defs": { + "DependencyType": { + "description": "Represents the dependency type for the tools' java cmd.", + "enum": [ + "jar", + "archive" + ], + "title": "DependencyType", + "type": "string" + }, + "DependencyVerification": { + "description": "The verification information of a runtime dependency required by the tools' java cmd.", + "properties": { + "size": { + "default": 0, + "description": "The size of the dependency file.", + "examples": [ + 3265393 + ], + "title": "Size", + "type": "integer" + }, + "file_hash": { + "$ref": "#/$defs/FileHashAlgorithm", + "default": null, + "description": "The hash function to verify the file.", + "examples": [ + { + "algorithm": "md5", + "value": "bc9bf7fedde0e700b974426fbd8d869c" + } + ] + } + }, + "title": "DependencyVerification", + "type": "object" + }, + "FileHashAlgorithm": { + "description": "Represents a file hash algorithm and its value. Used for verification against an existing file.", + "properties": { + "algorithm": { + "$ref": "#/$defs/HashAlgorithm" + }, + "value": { + "title": "Value", + "type": "string" + } + }, + "required": [ + "algorithm", + "value" + ], + "title": "FileHashAlgorithm", + "type": "object" + }, + "HashAlgorithm": { + "description": "Represents the supported hashing algorithms", + "enum": [ + "md5", + "sha1", + "sha256", + "sha512" + ], + "title": "HashAlgorithm", + "type": "string" + }, + "RuntimeDependency": { + "description": "Holds information about a runtime dependency required by the tools' java cmd.", + "properties": { + "name": { + "description": "The name of the dependency.", + "examples": [ + "Spark-3.5.0", + "AWS Java SDK" + ], + "title": "Name", + "type": "string" + }, + "uri": { + "anyOf": [ + { + "format": "uri", + "minLength": 1, + "type": "string" + }, + { + "format": "file-path", + "type": "string" + } + ], + "description": "The location of the dependency file. It can be a URL to a remote web/storage or a file path.", + "examples": [ + "file:///path/to/file.tgz", + "https://mvn-url/24.08.1/rapids-4-spark-tools_2.12-24.08.1.jar", + "gs://bucket-name/path/to/file.jar" + ], + "title": "Uri" + }, + "dependency_type": { + "$ref": "#/$defs/RuntimeDependencyType", + "description": "Specifies the dependency type to determine how the item is processed. For example, jar files are appended to the java classpath while archive files such as spark are extracted first before adding subdirectory _/jars/* to the classpath." + }, + "verification": { + "$ref": "#/$defs/DependencyVerification", + "default": null, + "description": "Optional specification to verify the dependency file." + } + }, + "required": [ + "name", + "uri" + ], + "title": "RuntimeDependency", + "type": "object" + }, + "RuntimeDependencyType": { + "description": "Defines the type of runtime dependency required by the tools' java cmd.", + "properties": { + "dep_type": { + "$ref": "#/$defs/DependencyType", + "description": "The type of the dependency." + }, + "relative_path": { + "default": null, + "description": "Specifies the relative path from within the archive file which will be added to the java cmd. Requires field dep_type to be set to (archive).", + "examples": [ + "jars/*" + ], + "title": "Relative Path", + "type": "string" + } + }, + "required": [ + "dep_type" + ], + "title": "RuntimeDependencyType", + "type": "object" + }, + "ToolsRuntimeConfig": { + "description": "The runtime configurations of the tools as defined by the user.", + "properties": { + "dependencies": { + "description": "The list of runtime dependencies required by the tools java cmd. Set this list to specify Spark binaries along with any other required jar files (i.e., hadoop jars, gcp connectors,..etc.). When specified, the default predefined dependencies will be ignored.", + "items": { + "$ref": "#/$defs/RuntimeDependency" + }, + "title": "Dependencies", + "type": "array" + } + }, + "required": [ + "dependencies" + ], + "title": "ToolsRuntimeConfig", + "type": "object" + } + }, + "description": "Main container for the user's defined tools configuration", + "properties": { + "api_version": { + "description": "The version of the API that the tools are using. This is used to test the compatibility of the configuration file against the current tools release.", + "examples": [ + "1.0" + ], + "maximum": 1.0, + "minimum": 1.0, + "title": "Api Version", + "type": "number" + }, + "runtime": { + "$ref": "#/$defs/ToolsRuntimeConfig", + "description": "Configuration related to the runtime environment of the tools." + } + }, + "required": [ + "api_version", + "runtime" + ], + "title": "ToolsConfig", + "type": "object" +} diff --git a/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/valid/tools_config_00.yaml b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/valid/tools_config_00.yaml new file mode 100644 index 000000000..f7dccf951 --- /dev/null +++ b/user_tools/tests/spark_rapids_tools_ut/resources/tools_config/valid/tools_config_00.yaml @@ -0,0 +1,11 @@ +# This yaml file is a configuration file for a tool that uses Spark 3.5.0 as dependency +# Minimal file content. +api_version: '1.0' +runtime: + dependencies: + - name: my-spark350 + uri: https:///archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz + dependency_type: + dep_type: archive + # for tgz files, it is required to give the subfolder where the jars are located + relative_path: jars/* diff --git a/user_tools/tests/spark_rapids_tools_ut/test_tool_argprocessor.py b/user_tools/tests/spark_rapids_tools_ut/test_tool_argprocessor.py index b899038b7..300751242 100644 --- a/user_tools/tests/spark_rapids_tools_ut/test_tool_argprocessor.py +++ b/user_tools/tests/spark_rapids_tools_ut/test_tool_argprocessor.py @@ -24,7 +24,8 @@ from spark_rapids_tools import CspEnv from spark_rapids_tools.cmdli.argprocessor import AbsToolUserArgModel, ArgValueCase from spark_rapids_tools.enums import QualFilterApp -from .conftest import SparkRapidsToolsUT, autotuner_prop_path, all_cpu_cluster_props, all_csps +from .conftest import SparkRapidsToolsUT, autotuner_prop_path, all_cpu_cluster_props, all_csps, \ + valid_tools_conf_files, invalid_tools_conf_files @dataclasses.dataclass @@ -77,22 +78,24 @@ def validate_args_w_savings_disabled(tool_name: str, t_args: dict): @staticmethod def create_tool_args_should_pass(tool_name: str, platform=None, cluster=None, - eventlogs=None, tools_jar=None): + eventlogs=None, tools_jar=None, tools_config_path=None): return AbsToolUserArgModel.create_tool_args(tool_name, platform=platform, cluster=cluster, eventlogs=eventlogs, - tools_jar=tools_jar) + tools_jar=tools_jar, + tools_config_path=tools_config_path) @staticmethod def create_tool_args_should_fail(tool_name: str, platform=None, cluster=None, - eventlogs=None, tools_jar=None): + eventlogs=None, tools_jar=None, tools_config_path=None): with pytest.raises(SystemExit) as pytest_wrapped_e: AbsToolUserArgModel.create_tool_args(tool_name, platform=platform, cluster=cluster, eventlogs=eventlogs, - tools_jar=tools_jar) + tools_jar=tools_jar, + tools_config_path=tools_config_path) assert pytest_wrapped_e.type == SystemExit @staticmethod @@ -321,6 +324,31 @@ def test_profiler_with_driverlog(self, get_ut_data_dir, prop_path): assert not prof_args['requiresEventlogs'] assert prof_args['rapidOptions']['driverlog'] == f'{get_ut_data_dir}/{prop_path}' + @pytest.mark.parametrize('tool_name', ['profiling', 'qualification']) + @pytest.mark.parametrize('csp', all_csps) + @pytest.mark.parametrize('tools_conf_fname', valid_tools_conf_files) + def test_tools_configs(self, get_ut_data_dir, tool_name, csp, tools_conf_fname): + tools_conf_path = f'{get_ut_data_dir}/tools_config/valid/{tools_conf_fname}' + # should pass: tools config file is provided + tool_args = self.create_tool_args_should_pass(tool_name, + platform=csp, + eventlogs=f'{get_ut_data_dir}/eventlogs', + tools_config_path=tools_conf_path) + assert tool_args['toolsConfig'] is not None + + @pytest.mark.parametrize('tool_name', ['profiling', 'qualification']) + @pytest.mark.parametrize('csp', all_csps) + @pytest.mark.parametrize('tools_conf_fname', invalid_tools_conf_files) + def test_invalid_tools_configs(self, get_ut_data_dir, tool_name, csp, tools_conf_fname): + tools_conf_path = f'{get_ut_data_dir}/tools_config/invalid/{tools_conf_fname}' + # should pass: tools config file is provided + with pytest.raises(SystemExit) as pytest_wrapped_e: + AbsToolUserArgModel.create_tool_args(tool_name, + platform=csp, + eventlogs=f'{get_ut_data_dir}/eventlogs', + tools_config_path=tools_conf_path) + assert pytest_wrapped_e.type == SystemExit + def test_arg_cases_coverage(self): """ This test ensures that above tests have covered all possible states of the `platform`, `cluster`,