From 8ed14f4ce35a88cef2af0a2d033f9605602e6d90 Mon Sep 17 00:00:00 2001 From: spark-rapids automation <70000568+nvauto@users.noreply.github.com> Date: Mon, 30 Oct 2023 16:47:25 +0000 Subject: [PATCH 1/9] Update dev-version by jenkins-spark-rapids-tools-auto-release-47 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 7a9d88451..01249ed6d 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 - 23.08.3-SNAPSHOT + 23.10.1-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 e2c2d6b9b..8672e1571 100644 --- a/user_tools/src/spark_rapids_pytools/__init__.py +++ b/user_tools/src/spark_rapids_pytools/__init__.py @@ -16,5 +16,5 @@ from spark_rapids_pytools.build import get_version -VERSION = '23.08.3' +VERSION = '23.10.1' __version__ = get_version(VERSION) From defd16c43d9b05d812afbd0a2f56329295823ad9 Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Tue, 31 Oct 2023 10:56:26 -0700 Subject: [PATCH 2/9] Add user tools and jar version in logs (#642) Signed-off-by: Partho Sarthi --- user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) 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 de86f63d1..7fbbd9b09 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py +++ b/user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py @@ -26,6 +26,7 @@ from logging import Logger from typing import Any, Callable, Dict, List +import spark_rapids_pytools from spark_rapids_tools import CspEnv from spark_rapids_pytools.cloud_api.sp_types import get_platform, \ ClusterBase, DeployMode, NodeHWInfo @@ -120,6 +121,7 @@ def wrapper(self, *args, **kwargs): def __post_init__(self): # when debug is set to true set it in the environment. self.logger = ToolLogging.get_and_setup_logger(f'rapids.tools.{self.name}') + self.logger.info('Using Spark RAPIDS user tools version %s', spark_rapids_pytools.__version__) def _check_environment(self) -> None: self.ctxt.platform.setup_and_validate_env() @@ -387,8 +389,12 @@ def _process_jar_arg(self): fail_ok=False, create_dir=True) self.logger.info('RAPIDS accelerator jar is downloaded to work_dir %s', jar_path) - # get the jar file name and add it to the tool args + # get the jar file name jar_file_name = FSUtil.get_resource_name(jar_path) + version_match = re.search(r'\d{2}\.\d{2}\.\d+', jar_file_name) + jar_version = version_match.group() if version_match else 'Unknown' + self.logger.info('Using Spark RAPIDS accelerator jar version %s', jar_version) + # add jar file name to the tool args self.ctxt.add_rapids_args('jarFileName', jar_file_name) self.ctxt.add_rapids_args('jarFilePath', jar_path) From ebca53035da1ac790a850a58fe21176c741060eb Mon Sep 17 00:00:00 2001 From: Partho Sarthi Date: Thu, 2 Nov 2023 09:29:29 -0700 Subject: [PATCH 3/9] Support extra arguments in new user tools CLI (#646) * Support extra arguments in new user tools CLI Signed-off-by: Partho Sarthi * Update tests Signed-off-by: Partho Sarthi --------- Signed-off-by: Partho Sarthi --- .../src/spark_rapids_tools/cmdli/tools_cli.py | 22 +++++++++++++++---- .../src/spark_rapids_tools/utils/util.py | 2 +- .../test_tool_argprocessor.py | 4 ++-- 3 files changed, 21 insertions(+), 7 deletions(-) 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 a8ed0bffb..5499bc961 100644 --- a/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py +++ b/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py @@ -48,7 +48,8 @@ def qualification(self, global_discount: int = None, gpu_cluster_recommendation: str = QualGpuClusterReshapeType.tostring( QualGpuClusterReshapeType.get_default()), - verbose: bool = False): + verbose: bool = False, + **rapids_options): """The Qualification cmd provides estimated running costs and speedups by migrating Apache Spark applications to GPU accelerated clusters. @@ -98,6 +99,11 @@ def qualification(self, "CLUSTER": recommend optimal GPU cluster by cost for entire cluster; "JOB": recommend optimal GPU cluster by cost per job :param verbose: True or False to enable verbosity of the script. + :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. + For more details on Qualification tool options, please visit + https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options """ if verbose: ToolLogging.enable_debug_mode() @@ -118,7 +124,8 @@ def qualification(self, if qual_args: tool_obj = QualificationAsLocal(platform_type=qual_args['runtimePlatform'], output_folder=qual_args['outputFolder'], - wrapper_options=qual_args) + wrapper_options=qual_args, + rapids_options=rapids_options) tool_obj.launch() def profiling(self, @@ -126,7 +133,8 @@ def profiling(self, cluster: str = None, platform: str = None, output_folder: str = None, - verbose: bool = False): + verbose: bool = False, + **rapids_options): """The Profiling cmd provides information which can be used for debugging and profiling Apache Spark applications running on accelerated GPU cluster. @@ -145,6 +153,11 @@ def profiling(self, and "databricks-azure". :param output_folder: path to store the output. :param verbose: True or False to enable verbosity of the script. + :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. + For more details on Profiling tool options, please visit + https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options """ if verbose: ToolLogging.enable_debug_mode() @@ -157,7 +170,8 @@ def profiling(self, if prof_args: tool_obj = ProfilingAsLocal(platform_type=prof_args['runtimePlatform'], output_folder=prof_args['outputFolder'], - wrapper_options=prof_args) + wrapper_options=prof_args, + rapids_options=rapids_options) tool_obj.launch() def bootstrap(self, diff --git a/user_tools/src/spark_rapids_tools/utils/util.py b/user_tools/src/spark_rapids_tools/utils/util.py index 71497eddc..e8ed7e05d 100644 --- a/user_tools/src/spark_rapids_tools/utils/util.py +++ b/user_tools/src/spark_rapids_tools/utils/util.py @@ -92,7 +92,7 @@ def dump_tool_usage(tool_name: Optional[str], raise_sys_exit: Optional[bool] = T imported_module = __import__('spark_rapids_tools.cmdli', globals(), locals(), ['ToolsCLI']) wrapper_clzz = getattr(imported_module, 'ToolsCLI') help_name = 'ascli' - usage_cmd = f'{tool_name} --help' + usage_cmd = f'{tool_name} -- --help' try: fire.Fire(wrapper_clzz(), name=help_name, command=usage_cmd) except fire.core.FireExit: 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 f1c719421..49d8f9cec 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 @@ -166,8 +166,8 @@ def test_cluster_props_no_eventlogs_on_prem(self, capsys, tool_name): platform='onprem') assert pytest_wrapped_e.type == SystemExit captured = capsys.readouterr() - # Verify there is no URL in error message - assert 'https://' not in captured.err + # Verify there is no URL in error message except for the one from the documentation + assert 'https://' not in captured.err or 'nvidia.github.io' in captured.err @pytest.mark.skip(reason='Unit tests are not completed yet') def test_arg_cases_coverage(self): From 2f1a9fbf573f4f230b579daee1679f4bdefd787a Mon Sep 17 00:00:00 2001 From: Cindy Jiang <47068112+cindyyuanjiang@users.noreply.github.com> Date: Fri, 3 Nov 2023 10:47:43 -0700 Subject: [PATCH 4/9] added logic to find num_workers for autoscaling clusters (#647) Signed-off-by: cindyyuanjiang --- .../cloud_api/databricks_aws.py | 13 +++++++++++++ .../cloud_api/databricks_azure.py | 13 +++++++++++++ 2 files changed, 26 insertions(+) diff --git a/user_tools/src/spark_rapids_pytools/cloud_api/databricks_aws.py b/user_tools/src/spark_rapids_pytools/cloud_api/databricks_aws.py index 77584aa81..91b439eec 100644 --- a/user_tools/src/spark_rapids_pytools/cloud_api/databricks_aws.py +++ b/user_tools/src/spark_rapids_pytools/cloud_api/databricks_aws.py @@ -205,7 +205,20 @@ def _init_nodes(self): master_nodes_from_conf = self.props.get_value_silent('driver') worker_nodes_from_conf = self.props.get_value_silent('executors') num_workers = self.props.get_value_silent('num_workers') + if num_workers is None and self.props.get_value_silent('autoscale') is not None: + target_workers = self.props.get_value_silent('autoscale', 'target_workers') + # use min_workers since it is usually the same as target_workers + min_workers = self.props.get_value_silent('autoscale', 'min_workers') + if target_workers is not None: + num_workers = target_workers + self.logger.info('Autoscaling cluster, will set number of workers to target_workers = %s', + num_workers) + elif min_workers is not None: + num_workers = min_workers + self.logger.info('Autoscaling cluster, will set number of workers to min_workers = %s', + num_workers) if num_workers is None: + self.logger.info('Unable to find number of workers for cluster, will default to 0') num_workers = 0 # construct master node info when cluster is inactive if master_nodes_from_conf is None: diff --git a/user_tools/src/spark_rapids_pytools/cloud_api/databricks_azure.py b/user_tools/src/spark_rapids_pytools/cloud_api/databricks_azure.py index 9b9d4a490..5cccd133e 100644 --- a/user_tools/src/spark_rapids_pytools/cloud_api/databricks_azure.py +++ b/user_tools/src/spark_rapids_pytools/cloud_api/databricks_azure.py @@ -289,7 +289,20 @@ def _init_nodes(self): driver_nodes_from_conf = self.props.get_value_silent('driver') worker_nodes_from_conf = self.props.get_value_silent('executors') num_workers = self.props.get_value_silent('num_workers') + if num_workers is None and self.props.get_value_silent('autoscale') is not None: + target_workers = self.props.get_value_silent('autoscale', 'target_workers') + # use min_workers since it is usually the same as target_workers + min_workers = self.props.get_value_silent('autoscale', 'min_workers') + if target_workers is not None: + num_workers = target_workers + self.logger.info('Autoscaling cluster, will set number of workers to target_workers = %s', + num_workers) + elif min_workers is not None: + num_workers = min_workers + self.logger.info('Autoscaling cluster, will set number of workers to min_workers = %s', + num_workers) if num_workers is None: + self.logger.info('Unable to find number of workers for cluster, will default to 0') num_workers = 0 # construct driver node info when cluster is inactive if driver_nodes_from_conf is None: From 67200e971e4a2ffc51317ae6232fd1c3a6c63f6f Mon Sep 17 00:00:00 2001 From: Matt Ahrens Date: Fri, 3 Nov 2023 14:39:49 -0500 Subject: [PATCH 5/9] Updating dataproc container cost to be multiplied by number of cores (#648) * Updating dataproc container cost to be multiplied by number of cores Signed-off-by: mattahrens * Simplifying changes for only Dataproc and not Dataproc GKE Signed-off-by: mattahrens * Fixing bug with extraneous dataproc_cost reference Signed-off-by: mattahrens * Fixing pylint with lines too long Signed-off-by: mattahrens * Fixing flake issue with indentation Signed-off-by: mattahrens * Fixing flake issue with indentation Signed-off-by: mattahrens * Fixing flake issue with whitespace Signed-off-by: mattahrens * Fixing Dataproc GKE costs for dataproc container Signed-off-by: mattahrens * Fixing Dataproc GKE costs for dataproc container Signed-off-by: mattahrens --------- Signed-off-by: mattahrens --- user_tools/src/spark_rapids_pytools/cloud_api/dataproc.py | 4 +++- .../src/spark_rapids_pytools/cloud_api/dataproc_gke.py | 5 ++--- user_tools/src/spark_rapids_pytools/cloud_api/onprem.py | 6 +++++- .../spark_rapids_pytools/pricing/dataproc_gke_pricing.py | 3 +-- 4 files changed, 11 insertions(+), 7 deletions(-) diff --git a/user_tools/src/spark_rapids_pytools/cloud_api/dataproc.py b/user_tools/src/spark_rapids_pytools/cloud_api/dataproc.py index d3a24e648..15d466ab9 100644 --- a/user_tools/src/spark_rapids_pytools/cloud_api/dataproc.py +++ b/user_tools/src/spark_rapids_pytools/cloud_api/dataproc.py @@ -545,5 +545,7 @@ def _calculate_group_cost(self, cluster_inst: ClusterGetAccessor, node_type: Spa def _get_cost_per_cluster(self, cluster: ClusterGetAccessor): master_cost = self._calculate_group_cost(cluster, SparkNodeType.MASTER) workers_cost = self._calculate_group_cost(cluster, SparkNodeType.WORKER) - dataproc_cost = self.price_provider.get_container_cost() + master_cores = cluster.get_nodes_cnt(SparkNodeType.MASTER) * cluster.get_node_core_count(SparkNodeType.MASTER) + worker_cores = cluster.get_nodes_cnt(SparkNodeType.WORKER) * cluster.get_node_core_count(SparkNodeType.WORKER) + dataproc_cost = self.price_provider.get_container_cost() * (master_cores + worker_cores) return master_cost + workers_cost + dataproc_cost diff --git a/user_tools/src/spark_rapids_pytools/cloud_api/dataproc_gke.py b/user_tools/src/spark_rapids_pytools/cloud_api/dataproc_gke.py index 06a71d342..6039914bd 100644 --- a/user_tools/src/spark_rapids_pytools/cloud_api/dataproc_gke.py +++ b/user_tools/src/spark_rapids_pytools/cloud_api/dataproc_gke.py @@ -194,7 +194,6 @@ class DataprocGkeSavingsEstimator(DataprocSavingsEstimator): """ def _get_cost_per_cluster(self, cluster: ClusterGetAccessor): - master_cost = self._calculate_group_cost(cluster, SparkNodeType.MASTER) - workers_cost = self._calculate_group_cost(cluster, SparkNodeType.WORKER) + dataproc_cost = super()._get_cost_per_cluster(cluster) dataproc_gke_cost = self.price_provider.get_container_cost() - return master_cost + workers_cost + dataproc_gke_cost + return dataproc_cost + dataproc_gke_cost diff --git a/user_tools/src/spark_rapids_pytools/cloud_api/onprem.py b/user_tools/src/spark_rapids_pytools/cloud_api/onprem.py index 28584617b..c2fc5f19b 100644 --- a/user_tools/src/spark_rapids_pytools/cloud_api/onprem.py +++ b/user_tools/src/spark_rapids_pytools/cloud_api/onprem.py @@ -312,6 +312,10 @@ def _get_cost_per_cluster(self, cluster: ClusterGetAccessor): if self.price_provider.name.casefold() == 'dataproc': master_cost = self.__calculate_dataproc_group_cost(cluster, SparkNodeType.MASTER) workers_cost = self.__calculate_dataproc_group_cost(cluster, SparkNodeType.WORKER) - dataproc_cost = self.price_provider.get_container_cost() + master_cores = (cluster.get_nodes_cnt(SparkNodeType.MASTER) + * cluster.get_node_core_count(SparkNodeType.MASTER)) + worker_cores = (cluster.get_nodes_cnt(SparkNodeType.WORKER) + * cluster.get_node_core_count(SparkNodeType.WORKER)) + dataproc_cost = self.price_provider.get_container_cost() * (master_cores + worker_cores) total_cost = master_cost + workers_cost + dataproc_cost return total_cost diff --git a/user_tools/src/spark_rapids_pytools/pricing/dataproc_gke_pricing.py b/user_tools/src/spark_rapids_pytools/pricing/dataproc_gke_pricing.py index 5c01d3296..3c06e370b 100644 --- a/user_tools/src/spark_rapids_pytools/pricing/dataproc_gke_pricing.py +++ b/user_tools/src/spark_rapids_pytools/pricing/dataproc_gke_pricing.py @@ -27,9 +27,8 @@ class DataprocGkePriceProvider(DataprocPriceProvider): name = 'DataprocGke' def get_container_cost(self) -> float: - dataproc_cost = super().get_container_cost() gke_container_cost = self.__get_gke_container_cost() - return dataproc_cost + gke_container_cost + return gke_container_cost def __get_gke_container_cost(self) -> float: lookup_key = 'CP-GKE-CONTAINER-MANAGMENT-COST' From a5683c3430501f3fab15573bf62550784c434ecb Mon Sep 17 00:00:00 2001 From: Niranjan Artal <50492963+nartal1@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:04:44 -0700 Subject: [PATCH 6/9] Qualification tool: Enhance mapping of Execs to stages (#634) * Qualification tool: Enhance mapping of Execs to stages Signed-off-by: Niranjan Artal * addressed review comments * address review comments and consolidate functions * addressed review comments * optimize checks Signed-off-by: Niranjan Artal * add documentation --------- Signed-off-by: Niranjan Artal --- .../qualification/QualificationAppInfo.scala | 84 ++++++++++++++++--- .../complex_dec_expectation.csv | 2 +- .../jdbc_expectation.csv | 2 +- .../nds_q86_fail_test_expectation.csv | 2 +- .../nds_q86_fail_test_expectation_persql.csv | 2 +- .../nds_q86_test_expectation.csv | 2 +- .../nds_q86_test_expectation_persql.csv | 2 +- .../qual_test_simple_expectation.csv | 8 +- .../qual_test_simple_expectation_persql.csv | 10 +-- .../read_dsv1_expectation.csv | 2 +- .../read_dsv2_expectation.csv | 2 +- .../spark2_expectation.csv | 2 +- .../truncated_1_end_expectation.csv | 2 +- .../write_format_expectation.csv | 2 +- 14 files changed, 91 insertions(+), 33 deletions(-) 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 028c904ed..61377d0b3 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 @@ -198,24 +198,82 @@ class QualificationAppInfo( } } + /** + * Checks the stage ID in the execution information. + * This function determines the associated stages for the given execution information by + * checking the stages in the current execution information, the previous execution information, + * and the next execution information. If there are associated stages, it returns a sequence of + * stage ID and execution information pairs. Otherwise, it returns an optional execution + * information(not associated with any stage). If there is stage ID associated with both the + * previous and the next execution information, then the current execution information is + * associated with the stage ID of the previous execution information. + * @param prev The previous execution information. + * @param execInfo The current execution information. + * @param next The next execution information. + * @return A tuple containing a sequence of stage ID and execution information pairs, + * and an optional execution information. + */ + private def checkStageIdInExec(prev: Option[ExecInfo], + execInfo: ExecInfo, next: Option[ExecInfo]): (Seq[(Int, ExecInfo)], Option[ExecInfo]) = { + val associatedStages = { + if (execInfo.stages.nonEmpty) { + execInfo.stages.toSeq + } else { + if (prev.exists(_.stages.nonEmpty)) { + prev.flatMap(_.stages.headOption).toSeq + } else if (next.exists(_.stages.nonEmpty)) { + next.flatMap(_.stages.headOption).toSeq + } else { + // we don't know what stage its in or its duration + logDebug(s"No stage associated with ${execInfo.exec} " + + s"so speedup factor isn't applied anywhere.") + Seq.empty + } + } + } + if (associatedStages.nonEmpty) { + (associatedStages.map((_, execInfo)), None) + } else { + (Seq.empty, Some(execInfo)) + } + } + private def getStageToExec(execInfos: Seq[ExecInfo]): (Map[Int, Seq[ExecInfo]], Seq[ExecInfo]) = { val execsWithoutStages = new ArrayBuffer[ExecInfo]() - val perStageSum = execInfos.flatMap { execInfo => - if (execInfo.stages.size > 1) { - execInfo.stages.map((_, execInfo)) - } else if (execInfo.stages.size < 1) { - // we don't know what stage its in or its duration - logDebug(s"No stage associated with ${execInfo.exec} " + - s"so speedup factor isn't applied anywhere.") - execsWithoutStages += execInfo - Seq.empty + + // This is to get the mapping between stageId and execs. This is primarily done based on + // accumulatorId. If an Exec has some metrics generated, then an accumulatorId will be + // generated for that Exec. This accumulatorId is used to get the stageId. If an Exec + // doesn't have any metrics, then we will try to get the stageId by looking at the + // neighbor Execs. If either of the neighbor Execs has a stageId, then we will use that + // to assign the same stageId to the current Exec as it's most likely that the current + // Exec is part of the same stage as the neighbor Exec. + val execInfosInOrder = execInfos.reverse + val execsToStageMap = execInfosInOrder.indices.map { + // corner case to handle first element + case 0 => if (execInfosInOrder.size > 1) { + // If there are more than one Execs, then check if the next Exec has a stageId. + checkStageIdInExec(None, execInfosInOrder(0), Some(execInfosInOrder(1))) } else { - Seq((execInfo.stages.head, execInfo)) + checkStageIdInExec(None, execInfosInOrder(0), None) } - }.groupBy(_._1).map { case (stage, execInfos) => - (stage, execInfos.map(_._2)) + // corner case to handle last element + case i if i == execInfosInOrder.size - 1 && execInfosInOrder.size > 1 => + // If there are more than one Execs, then check if the previous Exec has a stageId. + checkStageIdInExec(Some(execInfosInOrder(i - 1)), execInfosInOrder(i), None) + case i => + checkStageIdInExec(Some(execInfosInOrder(i - 1)), + execInfosInOrder(i), Some(execInfosInOrder(i + 1))) } - (perStageSum, execsWithoutStages.toSeq) + val perStageSum = execsToStageMap.map(_._1).toList.flatten + .groupBy(_._1).map { case (stage, execInfo) => + (stage, execInfo.map(_._2)) + } + + // Add all the execs that don't have a stageId to execsWithoutStages. + execsWithoutStages ++= execsToStageMap.map(_._2).toList.flatten + + (perStageSum, execsWithoutStages) } private def flattenedExecs(execs: Seq[ExecInfo]): Seq[ExecInfo] = { diff --git a/core/src/test/resources/QualificationExpectations/complex_dec_expectation.csv b/core/src/test/resources/QualificationExpectations/complex_dec_expectation.csv index 9d29c9106..753bdcacf 100644 --- a/core/src/test/resources/QualificationExpectations/complex_dec_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/complex_dec_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Spark shell","local-1626104300434","Not Recommended",1.01,129484.66,1619.33,2429,1469,131104,2429,88.35,"","","","struct,lastname:string>;struct,previous:struct,city:string>>;array>;map;map>;map>;array>;array","struct,lastname:string>;struct,previous:struct,city:string>>;array>;map>;map>;array>","NESTED COMPLEX TYPE",1260,128847,0,1469,3.0,false,"CollectLimit","",30 +"Spark shell","local-1626104300434","Not Recommended",1.0,129898.52,1205.47,2429,1469,131104,1923,88.35,"","","","struct,lastname:string>;struct,previous:struct,city:string>>;array>;map;map>;map>;array>;array","struct,lastname:string>;struct,previous:struct,city:string>>;array>;map>;map>;array>","NESTED COMPLEX TYPE",1260,128847,306,1163,2.68,false,"CollectLimit","",30 diff --git a/core/src/test/resources/QualificationExpectations/jdbc_expectation.csv b/core/src/test/resources/QualificationExpectations/jdbc_expectation.csv index eb8a72b10..7dbea470a 100644 --- a/core/src/test/resources/QualificationExpectations/jdbc_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/jdbc_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Spark shell","app-20211019113801-0001","Not Recommended",1.0,569385.42,2581.57,3627,19894,571967,3503,28.41,"","JDBC[*]","","","","",1812,544575,677,19217,3.8,false,"Scan JDBCRelation(TBLS) [numPartitions=1];Execute CreateViewCommand;CollectLimit","",30 +"Spark shell","app-20211019113801-0001","Not Recommended",1.0,569439.65,2527.34,3627,19894,571967,3470,28.41,"","JDBC[*]","","","","",1812,544575,859,19035,3.68,false,"Scan JDBCRelation(TBLS) [numPartitions=1];Execute CreateViewCommand;CollectLimit","",30 diff --git a/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation.csv b/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation.csv index c646df568..84fe8ae97 100644 --- a/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"TPC-DS Like Bench q86","app-20210319163812-1778","Not Applicable",1.36,19120.15,7050.84,9569,4320658,26171,9569,0.0,"24","","","","","",9565,3595714,0,4320658,3.8,false,"Execute CreateViewCommand","",30 +"TPC-DS Like Bench q86","app-20210319163812-1778","Not Applicable",1.36,19230.84,6940.15,9569,4320658,26171,9569,0.0,"24","","","","","",9565,3595714,0,4320658,3.64,false,"Execute CreateViewCommand","",30 diff --git a/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation_persql.csv b/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation_persql.csv index 26953acab..d88fcb188 100644 --- a/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation_persql.csv +++ b/core/src/test/resources/QualificationExpectations/nds_q86_fail_test_expectation_persql.csv @@ -23,4 +23,4 @@ App Name,App ID,SQL ID,SQL Description,SQL DF Duration,GPU Opportunity,Estimated "TPC-DS Like Bench q86","app-20210319163812-1778",17,"Register input tables",0,0,0.0,0.0,0.0,"Not Recommended" "TPC-DS Like Bench q86","app-20210319163812-1778",8,"Register input tables",0,0,0.0,0.0,0.0,"Not Recommended" "TPC-DS Like Bench q86","app-20210319163812-1778",23,"Register input tables",0,0,0.0,0.0,0.0,"Not Recommended" -"TPC-DS Like Bench q86","app-20210319163812-1778",24,"Benchmark Run: query=q86; iteration=0",9565,9565,2517.1,3.79,7047.89,"Not Applicable" +"TPC-DS Like Bench q86","app-20210319163812-1778",24,"Benchmark Run: query=q86; iteration=0",9565,9565,2627.74,3.64,6937.25,"Not Applicable" diff --git a/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation.csv b/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation.csv index b58f5b8ef..3cd646abf 100644 --- a/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"TPC-DS Like Bench q86","app-20210319163812-1778","Recommended",1.36,19120.15,7050.84,9569,4320658,26171,9569,35.34,"","","","","","",9565,3595714,0,4320658,3.8,false,"Execute CreateViewCommand","",30 +"TPC-DS Like Bench q86","app-20210319163812-1778","Recommended",1.36,19230.84,6940.15,9569,4320658,26171,9569,35.34,"","","","","","",9565,3595714,0,4320658,3.64,false,"Execute CreateViewCommand","",30 diff --git a/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation_persql.csv b/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation_persql.csv index 8dfe3cf65..383bd4d73 100644 --- a/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation_persql.csv +++ b/core/src/test/resources/QualificationExpectations/nds_q86_test_expectation_persql.csv @@ -1,5 +1,5 @@ App Name,App ID,SQL ID,SQL Description,SQL DF Duration,GPU Opportunity,Estimated GPU Duration,Estimated GPU Speedup,Estimated GPU Time Saved,Recommendation -"TPC-DS Like Bench q86","app-20210319163812-1778",24,"Benchmark Run: query=q86; iteration=0",9565,9565,2517.1,3.79,7047.89,"Strongly Recommended" +"TPC-DS Like Bench q86","app-20210319163812-1778",24,"Benchmark Run: query=q86; iteration=0",9565,9565,2627.74,3.64,6937.25,"Strongly Recommended" "TPC-DS Like Bench q86","app-20210319163812-1778",0,"Register input tables",2,2,2.0,1.0,0.0,"Not Recommended" "TPC-DS Like Bench q86","app-20210319163812-1778",21,"Register input tables",1,1,1.0,1.0,0.0,"Not Recommended" "TPC-DS Like Bench q86","app-20210319163812-1778",5,"Register input tables",1,1,1.0,1.0,0.0,"Not Recommended" diff --git a/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation.csv b/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation.csv index f43f88c8d..e41039a69 100644 --- a/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation.csv @@ -1,5 +1,5 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Rapids Spark Profiling Tool Unit Tests","local-1622043423018","Recommended",1.92,8472.65,7846.34,12434,132257,16319,10589,37.7,"","","JSON","","","",7143,4717,19616,112641,3.86,false,"SerializeFromObject;Execute InsertIntoHadoopFsRelationCommand json;DeserializeToObject;Filter;MapElements;Scan","",1 -"Spark shell","local-1651187225439","Not Recommended",1.0,355483.43,153.56,760,180,355637,350,87.88,"","JSON[string:bigint:int]","","","","",498,343411,97,83,1.78,false,"SerializeFromObject;CollectLimit;DeserializeToObject;Scan json;Filter;MapElements","",1 -"Spark shell","local-1651188809790","Not Recommended",1.0,166199.97,15.02,911,283,166215,45,81.18,"","JSON[string:bigint:int]","","","","UDF",715,133608,269,14,1.5,false,"CollectLimit;Scan json;Project","UDF",1 -"Rapids Spark Profiling Tool Unit Tests","local-1623281204390","Not Recommended",1.0,6240.0,0.0,2032,4666,6240,0,46.27,"","JSON[string:bigint:int]","JSON","","","UDF",1209,5793,4664,2,1.0,false,"Execute InsertIntoHadoopFsRelationCommand json;LocalTableScan;Project;Scan json;Execute CreateViewCommand","UDF",1 +"Rapids Spark Profiling Tool Unit Tests","local-1622043423018","Recommended",1.92,8488.68,7830.31,12434,132257,16319,10577,37.7,"","","JSON","","","",7143,4717,19744,112513,3.85,false,"SerializeFromObject;Execute InsertIntoHadoopFsRelationCommand json;DeserializeToObject;Filter;MapElements;Scan","",1 +"Spark shell","local-1651187225439","Not Recommended",1.0,355550.33,86.66,760,180,355637,253,87.88,"","JSON[string:bigint:int]","","","","",498,343411,120,60,1.52,false,"SerializeFromObject;CollectLimit;DeserializeToObject;Scan json;Filter;MapElements","",1 +"Spark shell","local-1651188809790","Not Recommended",1.0,166205.19,9.8,911,283,166215,38,81.18,"","JSON[string:bigint:int]","","","","UDF",715,133608,271,12,1.34,false,"CollectLimit;Scan json;Project","UDF",1 +"Rapids Spark Profiling Tool Unit Tests","local-1623281204390","Not Recommended",1.0,6240.0,0.0,2032,4666,6240,2,46.27,"","JSON[string:bigint:int]","JSON","","","UDF",1209,5793,4661,5,1.0,false,"Execute InsertIntoHadoopFsRelationCommand json;LocalTableScan;Project;Scan json;Execute CreateViewCommand","UDF",1 diff --git a/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation_persql.csv b/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation_persql.csv index 33a0ceccd..c1f77372d 100644 --- a/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation_persql.csv +++ b/core/src/test/resources/QualificationExpectations/qual_test_simple_expectation_persql.csv @@ -2,12 +2,12 @@ App Name,App ID,SQL ID,SQL Description,SQL DF Duration,GPU Opportunity,Estimated "Rapids Spark Profiling Tool Unit Tests","local-1622043423018",1,"count at QualificationInfoUtils.scala:94",7143,6719,2078.49,3.43,5064.5,"Strongly Recommended" "Rapids Spark Profiling Tool Unit Tests","local-1622043423018",3,"count at QualificationInfoUtils.scala:94",2052,1660,800.56,2.56,1251.43,"Strongly Recommended" "Rapids Spark Profiling Tool Unit Tests","local-1622043423018",2,"count at QualificationInfoUtils.scala:94",1933,1551,763.96,2.53,1169.03,"Strongly Recommended" -"Spark shell","local-1651187225439",0,"show at :26",498,249,373.5,1.33,124.5,"Recommended" -"Spark shell","local-1651188809790",1,"show at :26",196,98,147.0,1.33,49.0,"Recommended" -"Spark shell","local-1651187225439",1,"show at :26",262,60,240.54,1.08,21.45,"Not Recommended" -"Rapids Spark Profiling Tool Unit Tests","local-1622043423018",0,"json at QualificationInfoUtils.scala:76",1306,187,1246.97,1.04,59.02,"Not Recommended" +"Spark shell","local-1651188809790",1,"show at :26",196,75,165.75,1.18,30.24,"Not Recommended" +"Spark shell","local-1651187225439",0,"show at :26",498,168,430.53,1.15,67.46,"Not Recommended" +"Spark shell","local-1651187225439",1,"show at :26",262,80,240.22,1.09,21.77,"Not Recommended" +"Rapids Spark Profiling Tool Unit Tests","local-1622043423018",0,"json at QualificationInfoUtils.scala:76",1306,164,1267.14,1.03,38.85,"Not Recommended" "Rapids Spark Profiling Tool Unit Tests","local-1623281204390",0,"json at QualificationInfoUtils.scala:130",1209,0,1209.0,1.0,0.0,"Not Recommended" -"Spark shell","local-1651188809790",0,"show at :26",715,2,715.0,1.0,0.0,"Not Recommended" +"Spark shell","local-1651188809790",0,"show at :26",715,5,715.0,1.0,0.0,"Not Recommended" "Rapids Spark Profiling Tool Unit Tests","local-1623281204390",2,"json at QualificationInfoUtils.scala:136",321,0,321.0,1.0,0.0,"Not Recommended" "Rapids Spark Profiling Tool Unit Tests","local-1623281204390",5,"json at QualificationInfoUtils.scala:136",129,0,129.0,1.0,0.0,"Not Recommended" "Rapids Spark Profiling Tool Unit Tests","local-1623281204390",8,"json at QualificationInfoUtils.scala:136",127,0,127.0,1.0,0.0,"Not Recommended" diff --git a/core/src/test/resources/QualificationExpectations/read_dsv1_expectation.csv b/core/src/test/resources/QualificationExpectations/read_dsv1_expectation.csv index f624cc260..318ea6b28 100644 --- a/core/src/test/resources/QualificationExpectations/read_dsv1_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/read_dsv1_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Spark shell","local-1624371544219","Not Recommended",1.0,174691.42,601.57,6695,20421,175293,1034,72.15,"","JSON[string:double:date:int:bigint];Text[*]","JSON","","","",1859,175857,17266,3155,2.39,false,"CollectLimit;Scan json;Execute InsertIntoHadoopFsRelationCommand json;Scan text","",30 +"Spark shell","local-1624371544219","Not Recommended",1.0,174006.51,1286.48,6695,20421,175293,2268,72.15,"","JSON[string:double:date:int:bigint];Text[*]","JSON","","","",1859,175953,13469,6952,2.31,false,"CollectLimit;Scan json;Execute InsertIntoHadoopFsRelationCommand json;Scan text","",30 diff --git a/core/src/test/resources/QualificationExpectations/read_dsv2_expectation.csv b/core/src/test/resources/QualificationExpectations/read_dsv2_expectation.csv index 5cb64b660..17c155356 100644 --- a/core/src/test/resources/QualificationExpectations/read_dsv2_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/read_dsv2_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Spark shell","local-1624371906627","Not Recommended",1.0,83172.84,565.15,6760,21802,83738,971,71.3,"","Text[*];json[double]","JSON","","","",1984,82505,18668,3134,2.39,false,"BatchScan json;Execute InsertIntoHadoopFsRelationCommand json;CollectLimit;Scan text","",30 +"Spark shell","local-1624371906627","Not Recommended",1.01,82304.74,1433.25,6760,21802,83738,2388,71.3,"","Text[*];json[double]","JSON","","","",1984,82601,14064,7738,2.5,false,"BatchScan json;Execute InsertIntoHadoopFsRelationCommand json;CollectLimit;Scan text","",30 diff --git a/core/src/test/resources/QualificationExpectations/spark2_expectation.csv b/core/src/test/resources/QualificationExpectations/spark2_expectation.csv index 7510bbb42..f300a45a5 100644 --- a/core/src/test/resources/QualificationExpectations/spark2_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/spark2_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Spark shell","local-1634253215009","Not Recommended",1.01,46352.24,710.75,1520,359,47063,1011,67.64,"","Text[*]","","","","",1068,44935,120,239,3.36,false,"CollectLimit;Scan text","",30 +"Spark shell","local-1634253215009","Not Recommended",1.01,46542.98,520.01,1520,359,47063,817,67.64,"","Text[*]","","","","",1068,44935,166,193,2.75,false,"CollectLimit;Scan text","",30 diff --git a/core/src/test/resources/QualificationExpectations/truncated_1_end_expectation.csv b/core/src/test/resources/QualificationExpectations/truncated_1_end_expectation.csv index 07d08a0c3..22394fe3a 100644 --- a/core/src/test/resources/QualificationExpectations/truncated_1_end_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/truncated_1_end_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Rapids Spark Profiling Tool Unit Tests","local-1622043423018","Not Recommended",1.09,4468.98,403.01,1306,14353,4872,570,62.67,"","","JSON","","","",1306,4477,8086,6267,3.41,true,"SerializeFromObject;Execute InsertIntoHadoopFsRelationCommand json;DeserializeToObject;Filter;MapElements;Scan","",30 +"Rapids Spark Profiling Tool Unit Tests","local-1622043423018","Not Recommended",1.08,4479.65,392.34,1306,14353,4872,558,62.67,"","","JSON","","","",1306,4477,8214,6139,3.36,true,"SerializeFromObject;Execute InsertIntoHadoopFsRelationCommand json;DeserializeToObject;Filter;MapElements;Scan","",30 diff --git a/core/src/test/resources/QualificationExpectations/write_format_expectation.csv b/core/src/test/resources/QualificationExpectations/write_format_expectation.csv index d18cf6f22..2ce60816e 100644 --- a/core/src/test/resources/QualificationExpectations/write_format_expectation.csv +++ b/core/src/test/resources/QualificationExpectations/write_format_expectation.csv @@ -1,2 +1,2 @@ App Name,App ID,Recommendation,Estimated GPU Speedup,Estimated GPU Duration,Estimated GPU Time Saved,SQL DF Duration,SQL Dataframe Task Duration,App Duration,GPU Opportunity,Executor CPU Time Percent,SQL Ids with Failures,Unsupported Read File Formats and Types,Unsupported Write Data Format,Complex Types,Nested Complex Types,Potential Problems,Longest SQL Duration,NONSQL Task Duration Plus Overhead,Unsupported Task Duration,Supported SQL DF Task Duration,Task Speedup Factor,App Duration Estimated,Unsupported Execs,Unsupported Expressions,Estimated Job Frequency (monthly) -"Spark shell","local-1629442299891","Not Recommended",1.05,18558.0,996.0,1992,920,19554,1992,91.72,"","","CSV;JSON","","","",1235,16325,0,920,2.0,false,"Execute InsertIntoHadoopFsRelationCommand json;Execute InsertIntoHadoopFsRelationCommand csv","",30 +"Spark shell","local-1629442299891","Not Recommended",1.03,18871.95,682.04,1992,920,19554,1364,91.72,"","","CSV;JSON","","","",1235,16325,290,630,2.0,false,"Execute InsertIntoHadoopFsRelationCommand json;Execute InsertIntoHadoopFsRelationCommand csv","",30 From 876c407ccb9f22969f20cdd40faf9c59ca28e50f Mon Sep 17 00:00:00 2001 From: Matt Ahrens Date: Tue, 7 Nov 2023 08:41:21 -0600 Subject: [PATCH 7/9] Updating autotuner to generation recommendation always, even without cluster info (#650) Signed-off-by: mattahrens --- .../rapids/tool/profiling/AutoTuner.scala | 32 +++++---- .../tool/profiling/AutoTunerSuite.scala | 72 +++++++++++++++++-- .../spark_rapids_pytools/rapids/profiling.py | 30 ++------ .../resources/profiling-conf.yaml | 1 - .../info_recommendations_disabled.ms | 7 -- 5 files changed, 92 insertions(+), 50 deletions(-) delete mode 100644 user_tools/src/spark_rapids_pytools/resources/templates/info_recommendations_disabled.ms 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 4241ac3c3..7d428a14b 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 @@ -568,7 +568,7 @@ class AutoTuner( } } - def calculateRecommendations(): Unit = { + def calculateClusterLevelRecommendations(): Unit = { recommendExecutorInstances() val numExecutorCores = calcNumExecutorCores val execCoresExpr = () => numExecutorCores @@ -593,6 +593,10 @@ class AutoTuner( appendRecommendation("spark.rapids.sql.multiThreadedRead.numThreads", Math.max(20, numExecutorCores)) + recommendAQEProperties() + } + + def calculateJobLevelRecommendations(): Unit = { val shuffleManagerVersion = appInfoProvider.getSparkVersion.get.filterNot("().".toSet) appendRecommendation("spark.shuffle.manager", "com.nvidia.spark.rapids.spark" + shuffleManagerVersion + ".RapidsShuffleManager") @@ -601,7 +605,7 @@ class AutoTuner( recommendFileCache() recommendMaxPartitionBytes() recommendShufflePartitions() - recommendGeneralProperties() + recommendGCProperty() recommendClassPathEntries() } @@ -631,7 +635,17 @@ class AutoTuner( } } - private def recommendGeneralProperties(): Unit = { + private def recommendGCProperty(): Unit = { + val jvmGCFraction = appInfoProvider.getJvmGCFractions + if (jvmGCFraction.nonEmpty) { // avoid zero division + if ((jvmGCFraction.sum / jvmGCFraction.size) > MAX_JVM_GCTIME_FRACTION) { + appendComment("Average JVM GC time is very high. " + + "Other Garbage Collectors can be used for better performance.") + } + } + } + + private def recommendAQEProperties(): Unit = { val aqeEnabled = getPropertyValue("spark.sql.adaptive.enabled") .getOrElse("false").toLowerCase if (aqeEnabled == "false") { @@ -665,13 +679,6 @@ class AutoTuner( // problematic because this is the compressed shuffle size appendRecommendation("spark.sql.adaptive.advisoryPartitionSizeInBytes", "128m") } - val jvmGCFraction = appInfoProvider.getJvmGCFractions - if (jvmGCFraction.nonEmpty) { // avoid zero division - if ((jvmGCFraction.sum / jvmGCFraction.size) > MAX_JVM_GCTIME_FRACTION) { - appendComment("Average JVM GC time is very high. " + - "Other Garbage Collectors can be used for better performance.") - } - } } /** @@ -905,9 +912,10 @@ class AutoTuner( } skipList.foreach(skipSeq => skipSeq.foreach(_ => skippedRecommendations.add(_))) skippedRecommendations ++= selectedPlatform.recommendationsToExclude + initRecommendations() + calculateJobLevelRecommendations() if (processPropsAndCheck) { - initRecommendations() - calculateRecommendations() + calculateClusterLevelRecommendations() } else { // add all default comments addDefaultComments() diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala index cbcf439d9..0a9172ca8 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala @@ -191,15 +191,31 @@ class AutoTunerSuite extends FunSuite with BeforeAndAfterEach with Logging { val (properties, comments) = autoTuner.getRecommendedProperties() val autoTunerOutput = Profiler.getAutoTunerResultsAsString(properties, comments) val expectedResults = - s"""|Cannot recommend properties. See Comments. + s"""| + |Spark Properties: + |--conf spark.shuffle.manager=com.nvidia.spark.rapids.spark311.RapidsShuffleManager + |--conf spark.sql.files.maxPartitionBytes=512m + |--conf spark.sql.shuffle.partitions=200 | |Comments: |- 'spark.executor.instances' should be set to (gpuCount * numWorkers). |- 'spark.executor.memory' should be set to at least 2GB/core. |- 'spark.rapids.memory.pinnedPool.size' should be set to 2048m. |- 'spark.rapids.sql.concurrentGpuTasks' should be set to Max(4, (gpuMemory / 8G)). + |- 'spark.shuffle.manager' was not set. |- 'spark.sql.adaptive.enabled' should be enabled for better performance. + |- 'spark.sql.files.maxPartitionBytes' was not set. + |- 'spark.sql.shuffle.partitions' was not set. |- 'spark.task.resource.gpu.amount' should be set to Max(1, (numCores / gpuCount)). + |- RAPIDS Accelerator for Apache Spark plugin jar is missing + | from the classpath entries. + | If the Spark RAPIDS jar is being bundled with your + | Spark distribution, this step is not needed. + |- The RAPIDS Shuffle Manager requires spark.driver.extraClassPath + | and spark.executor.extraClassPath settings to include the + | path to the Spark RAPIDS plugin jar. + | If the Spark RAPIDS jar is being bundled with your Spark + | distribution, this step is not needed. |- java.io.FileNotFoundException: File non-existing.yaml does not exist |""".stripMargin assert(expectedResults == autoTunerOutput) @@ -212,16 +228,32 @@ class AutoTunerSuite extends FunSuite with BeforeAndAfterEach with Logging { val autoTunerOutput = Profiler.getAutoTunerResultsAsString(properties, comments) // scalastyle:off line.size.limit val expectedResults = - s"""|Cannot recommend properties. See Comments. + s"""| + |Spark Properties: + |--conf spark.shuffle.manager=com.nvidia.spark.rapids.spark311.RapidsShuffleManager + |--conf spark.sql.files.maxPartitionBytes=512m + |--conf spark.sql.shuffle.partitions=200 | |Comments: |- 'spark.executor.instances' should be set to (gpuCount * numWorkers). |- 'spark.executor.memory' should be set to at least 2GB/core. |- 'spark.rapids.memory.pinnedPool.size' should be set to 2048m. |- 'spark.rapids.sql.concurrentGpuTasks' should be set to Max(4, (gpuMemory / 8G)). + |- 'spark.shuffle.manager' was not set. |- 'spark.sql.adaptive.enabled' should be enabled for better performance. + |- 'spark.sql.files.maxPartitionBytes' was not set. + |- 'spark.sql.shuffle.partitions' was not set. |- 'spark.task.resource.gpu.amount' should be set to Max(1, (numCores / gpuCount)). |- Incorrect values in worker system information: {numCores: 0, memory: 122880MiB, numWorkers: 4}. + |- RAPIDS Accelerator for Apache Spark plugin jar is missing + | from the classpath entries. + | If the Spark RAPIDS jar is being bundled with your + | Spark distribution, this step is not needed. + |- The RAPIDS Shuffle Manager requires spark.driver.extraClassPath + | and spark.executor.extraClassPath settings to include the + | path to the Spark RAPIDS plugin jar. + | If the Spark RAPIDS jar is being bundled with your Spark + | distribution, this step is not needed. |""".stripMargin // scalastyle:on line.size.limit assert(expectedResults == autoTunerOutput) @@ -234,16 +266,32 @@ class AutoTunerSuite extends FunSuite with BeforeAndAfterEach with Logging { val autoTunerOutput = Profiler.getAutoTunerResultsAsString(properties, comments) // scalastyle:off line.size.limit val expectedResults = - s"""|Cannot recommend properties. See Comments. + s"""| + |Spark Properties: + |--conf spark.shuffle.manager=com.nvidia.spark.rapids.spark311.RapidsShuffleManager + |--conf spark.sql.files.maxPartitionBytes=512m + |--conf spark.sql.shuffle.partitions=200 | |Comments: |- 'spark.executor.instances' should be set to (gpuCount * numWorkers). |- 'spark.executor.memory' should be set to at least 2GB/core. |- 'spark.rapids.memory.pinnedPool.size' should be set to 2048m. |- 'spark.rapids.sql.concurrentGpuTasks' should be set to Max(4, (gpuMemory / 8G)). + |- 'spark.shuffle.manager' was not set. |- 'spark.sql.adaptive.enabled' should be enabled for better performance. + |- 'spark.sql.files.maxPartitionBytes' was not set. + |- 'spark.sql.shuffle.partitions' was not set. |- 'spark.task.resource.gpu.amount' should be set to Max(1, (numCores / gpuCount)). |- Incorrect values in worker system information: {numCores: 32, memory: , numWorkers: 4}. + |- RAPIDS Accelerator for Apache Spark plugin jar is missing + | from the classpath entries. + | If the Spark RAPIDS jar is being bundled with your + | Spark distribution, this step is not needed. + |- The RAPIDS Shuffle Manager requires spark.driver.extraClassPath + | and spark.executor.extraClassPath settings to include the + | path to the Spark RAPIDS plugin jar. + | If the Spark RAPIDS jar is being bundled with your Spark + | distribution, this step is not needed. |""".stripMargin // scalastyle:on line.size.limit assert(expectedResults == autoTunerOutput) @@ -256,16 +304,32 @@ class AutoTunerSuite extends FunSuite with BeforeAndAfterEach with Logging { val autoTunerOutput = Profiler.getAutoTunerResultsAsString(properties, comments) // scalastyle:off line.size.limit val expectedResults = - s"""|Cannot recommend properties. See Comments. + s"""| + |Spark Properties: + |--conf spark.shuffle.manager=com.nvidia.spark.rapids.spark311.RapidsShuffleManager + |--conf spark.sql.files.maxPartitionBytes=512m + |--conf spark.sql.shuffle.partitions=200 | |Comments: |- 'spark.executor.instances' should be set to (gpuCount * numWorkers). |- 'spark.executor.memory' should be set to at least 2GB/core. |- 'spark.rapids.memory.pinnedPool.size' should be set to 2048m. |- 'spark.rapids.sql.concurrentGpuTasks' should be set to Max(4, (gpuMemory / 8G)). + |- 'spark.shuffle.manager' was not set. |- 'spark.sql.adaptive.enabled' should be enabled for better performance. + |- 'spark.sql.files.maxPartitionBytes' was not set. + |- 'spark.sql.shuffle.partitions' was not set. |- 'spark.task.resource.gpu.amount' should be set to Max(1, (numCores / gpuCount)). |- Incorrect values in worker system information: {numCores: 32, memory: 0m, numWorkers: 4}. + |- RAPIDS Accelerator for Apache Spark plugin jar is missing + | from the classpath entries. + | If the Spark RAPIDS jar is being bundled with your + | Spark distribution, this step is not needed. + |- The RAPIDS Shuffle Manager requires spark.driver.extraClassPath + | and spark.executor.extraClassPath settings to include the + | path to the Spark RAPIDS plugin jar. + | If the Spark RAPIDS jar is being bundled with your Spark + | distribution, this step is not needed. |""".stripMargin // scalastyle:on line.size.limit assert(expectedResults == autoTunerOutput) diff --git a/user_tools/src/spark_rapids_pytools/rapids/profiling.py b/user_tools/src/spark_rapids_pytools/rapids/profiling.py index 97e75a33c..0c6e1cfcc 100644 --- a/user_tools/src/spark_rapids_pytools/rapids/profiling.py +++ b/user_tools/src/spark_rapids_pytools/rapids/profiling.py @@ -25,7 +25,7 @@ from spark_rapids_pytools.cloud_api.sp_types import ClusterBase from spark_rapids_pytools.common.sys_storage import FSUtil -from spark_rapids_pytools.common.utilities import Utils, TemplateGenerator +from spark_rapids_pytools.common.utilities import Utils from spark_rapids_pytools.rapids.rapids_tool import RapidsJarTool @@ -70,11 +70,6 @@ def _process_offline_cluster_args(self): # only if we succeed to get the GPU cluster, we can generate auto-tuner-input self._generate_autotuner_input() - def __load_disabled_recommendation_report(self) -> str: - template_file_name = self.ctxt.get_value('toolOutput', 'recommendations', 'disabledInfoMsgTemplate') - template_path = Utils.resource_path(f'templates/{template_file_name}') - return TemplateGenerator.render_template_file(template_path, {'CLUSTER_ARG': 'cluster'}) - def _process_gpu_cluster_args(self, offline_cluster_opts: dict = None): gpu_cluster_arg = offline_cluster_opts.get('gpuCluster') if gpu_cluster_arg: @@ -82,10 +77,6 @@ def _process_gpu_cluster_args(self, offline_cluster_opts: dict = None): self.ctxt.set_ctxt('gpuClusterProxy', gpu_cluster_obj) return True # If we are here, we know that the workerInfoPath was not set as well. - # Then we can remind the user that recommendations won't be calculated - disabled_recommendations_msg = self.__load_disabled_recommendation_report() - self.ctxt.set_ctxt('disabledRecommendationsMsg', disabled_recommendations_msg) - self.logger.info(disabled_recommendations_msg) return False def _generate_autotuner_file_for_cluster(self, file_path: str, cluster_ob: ClusterBase): @@ -135,10 +126,10 @@ def _generate_autotuner_input(self): self.ctxt.set_ctxt('autoTunerFilePath', autotuner_input_path) def _create_autotuner_rapids_args(self) -> list: - # Add the autotuner argument if the autotunerPath exists + # Add the autotuner argument, also add worker-info if the autotunerPath exists autotuner_path = self.ctxt.get_ctxt('autoTunerFilePath') if autotuner_path is None: - return [] + return ['--auto-tuner'] return ['--auto-tuner', '--worker-info', autotuner_path] def __read_single_app_output(self, file_path: str) -> (str, List[str], List[str]): @@ -209,14 +200,6 @@ def _write_summary(self): print(Utils.gen_multiline_str(self._report_tool_full_location(), self.ctxt.get_ctxt('wrapperOutputContent'))) - def __generate_report_no_recommendations(self): - prof_app_dirs = FSUtil.get_subdirectories(self.ctxt.get_rapids_output_folder()) - wrapper_content = [Utils.gen_report_sec_header('Recommendations'), - self.ctxt.get_ctxt('disabledRecommendationsMsg'), - Utils.gen_report_sec_header('Profiling status'), - f'Total application profiled: {len(prof_app_dirs)}'] - self.ctxt.set_ctxt('wrapperOutputContent', wrapper_content) - def __generate_report_with_recommendations(self): prof_app_dirs = FSUtil.get_subdirectories(self.ctxt.get_rapids_output_folder()) profiling_log = self.ctxt.get_value('toolOutput', 'recommendations', 'fileName') @@ -261,12 +244,7 @@ def _process_output(self): if not self._evaluate_rapids_jar_tool_output_exist(): return - if self.ctxt.get_ctxt('autoTunerFilePath'): - # if autotuner is enabled, generate full recommendations summary - self.__generate_report_with_recommendations() - else: - # generate a brief summary - self.__generate_report_no_recommendations() + self.__generate_report_with_recommendations() def _init_rapids_arg_list(self) -> List[str]: return self._create_autotuner_rapids_args() 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 443e0dc65..3376b1b60 100644 --- a/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml @@ -2,7 +2,6 @@ toolOutput: subFolder: rapids_4_spark_profile recommendations: fileName: profile.log - disabledInfoMsgTemplate: 'info_recommendations_disabled.ms' headers: section: '### D. Recommended Configuration ###' sparkProperties: 'Spark Properties:' diff --git a/user_tools/src/spark_rapids_pytools/resources/templates/info_recommendations_disabled.ms b/user_tools/src/spark_rapids_pytools/resources/templates/info_recommendations_disabled.ms deleted file mode 100644 index 92ddcbc92..000000000 --- a/user_tools/src/spark_rapids_pytools/resources/templates/info_recommendations_disabled.ms +++ /dev/null @@ -1,7 +0,0 @@ -Recommendations cannot be generated. - Reason: The cluster information was not set. - To enable recommendation, the cluster properties must be defined. - Please make sure to set the arguments properly by either: - 1. Setting <{{ CLUSTER_ARG }}> argument and optional set if - the path is not defined by the cluster properties; or - 2. Setting both <{{ CLUSTER_ARG }}> and From 20be6882738a31cec016d8a172b1453bc2204a10 Mon Sep 17 00:00:00 2001 From: Matt Ahrens Date: Tue, 7 Nov 2023 16:58:30 -0600 Subject: [PATCH 8/9] Updating tools docs to remove dead links and profiling docs to not require cluster/worker info (#651) * Updating profiling docs to not require cluster/worker info Signed-off-by: mattahrens * Updating README with docs.nvidia.com links Signed-off-by: mattahrens * Updating old links to new docs.nvidia.com for user tools docs and autotuner code Signed-off-by: mattahrens * Removing old references to Github pages Signed-off-by: mattahrens --------- Signed-off-by: mattahrens --- core/README.md | 4 +- .../Tools/qualification-tool-app-view-01.png | Bin 298189 -> 0 bytes .../Tools/qualification-tool-app-view-02.png | Bin 218188 -> 0 bytes .../Tools/qualification-tool-app-view-03.png | Bin 244955 -> 0 bytes ...l-recommendation-indexview-with-persql.png | Bin 520234 -> 0 bytes core/docs/spark-profiling-tool.md | 729 ------------ core/docs/spark-qualification-tool.md | 1027 ----------------- .../rapids/tool/profiling/AutoTuner.scala | 3 +- .../tool/profiling/AutoTunerSuite.scala | 4 +- user_tools/docs/index.md | 8 +- user_tools/docs/user-tools-aws-emr.md | 11 +- user_tools/docs/user-tools-databricks-aws.md | 11 +- .../docs/user-tools-databricks-azure.md | 12 +- user_tools/docs/user-tools-dataproc-gke.md | 2 +- user_tools/docs/user-tools-dataproc.md | 11 +- user_tools/docs/user-tools-onprem.md | 9 +- .../resources/profiling-conf.yaml | 2 +- .../resources/qualification-conf.yaml | 2 +- .../wrappers/databricks_aws_wrapper.py | 4 +- .../wrappers/databricks_azure_wrapper.py | 4 +- .../wrappers/dataproc_gke_wrapper.py | 2 +- .../wrappers/dataproc_wrapper.py | 4 +- .../wrappers/emr_wrapper.py | 4 +- .../wrappers/onprem_wrapper.py | 4 +- .../src/spark_rapids_tools/cmdli/tools_cli.py | 4 +- .../test_tool_argprocessor.py | 2 +- 26 files changed, 60 insertions(+), 1803 deletions(-) delete mode 100644 core/docs/img/Tools/qualification-tool-app-view-01.png delete mode 100644 core/docs/img/Tools/qualification-tool-app-view-02.png delete mode 100644 core/docs/img/Tools/qualification-tool-app-view-03.png delete mode 100644 core/docs/img/Tools/qualification-tool-recommendation-indexview-with-persql.png delete mode 100644 core/docs/spark-profiling-tool.md delete mode 100644 core/docs/spark-qualification-tool.md diff --git a/core/README.md b/core/README.md index 8000a5460..a233ab4db 100644 --- a/core/README.md +++ b/core/README.md @@ -7,8 +7,8 @@ The Profiling tool generates information which can be used for debugging and pro Information such as Spark version, executor information, properties and so on. This runs on either CPU or GPU generated event logs. -Please refer to [Qualification tool documentation](docs/spark-qualification-tool.md) -and [Profiling tool documentation](docs/spark-profiling-tool.md) +Please refer to [Qualification tool documentation](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html) +and [Profiling tool documentation](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html) for more details on how to use the tools. ## Build diff --git a/core/docs/img/Tools/qualification-tool-app-view-01.png b/core/docs/img/Tools/qualification-tool-app-view-01.png deleted file mode 100644 index 5a930e637bf16fdba01240b5d409cddb645698c0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 298189 zcmZ^~1y~%-@-K`g5Ht|nA-KDSMS@#GaF^ijkN`_?2n5#W#Vr&NK37NJvOHN{X*Fk&vEPBO#%1VLW-P zDMB=3A|W9w+R4hkQIeIVedF$8ZRco(gv63yX=X;C#KAITZf<5a^plWMqt{ z8F-*;ysNuwAa}$pA>GJmgA{)QRjUW7;B9xa19lAUT^-j@MZjjYzV))2m3uv5s|RAq zgyZNflCHA78(%yEweN5kU5*hOJBC%N$=y#0IdT>G{pT0RROs&~kZWh?e&f)36NCn! zRWQ9AA{**NevkFi<|WEcGk@gw$1mxkN*HSMZi11jKbObF1=lCECM0}jN4`zOR@P)! zmTK?_l(u?n{Xsq|n1Lr*lStw+@!Io640cQmDXEk>?%N006A=!wei3{NKQU}s-|Tlu@|;c9ec6tEBXJ zeP`)zW##N)>*A@fBkA#IPDwj0Jx@I~RWVB!Coc2%E*4f?zD}-xS&$@r#U6`JR-Wdx zzD|zL9%8<)=>OG2?6Lf}nwy^XUtK&MUeW8Ry`hzLakrus;^N`rp_hC{OG_)^{@z+l z^R@iH*&lzsqPO+*bQR;~_VMxI^5N%lakt^-6%`fb=HcV!}l@H>FmMq z&mjL9=e3oGrMsQ0r=5#4?cZ_DEnK`jU(wV574*M8|IE|M*Y3ZPoIU=%tj7g%|83#s z<>KM~zp;7RS^q!S{2#(#;_`>#k|A^!hO=KpB=AFO}ZAg1AN_bAT% zuM#DBCAj~Ox_|eV;Qp)O|Do}J%=xd{M|(;>li>bez9IQcP=DhM5|R{>(ralgU*zA$ z=t0K)bCPXtJ`)46Erek&(R@n6n&46@ZU)gWSTzfMYqv~Rg3*GR`iQpv{BfrE^#D1| z%DDZr9MJYFl!TqX?R2e`VbJ=|Sca&N^>k|0N-}3KfR@Je>({TuRO?SMYJ-gIuhj4h zhU@D$7od)zg-Ra`h%a9AcjbgN*b$#vVAJ+Y$EUg`Ab~Cq3rtWDw18|IqW{?b3>_2g z-$aRoDdNR?U@8q@-wCfb^B^#`t9cOE+owDL{+{Uy2VqGPk1o+(P7mBL9gKZhJsW2< zYKGdt15oa*da!=J5a+)6(E=5hYB2OXf<%E79CmI)3N{oTDQ_e)N6?Ky^uC=v$ftv!U20Td zv7uvL=FHMmREBDu;qQTa;mwWd=J15O^E5kK+mF-dHfs1g-atM-ag2t;BYC@{+l*}RFK5p zO+vDql^&jtuVzUqbr38SQE0t0kOo2(S?Y4Z1!9Z23^5>RcCPvc@c>Q46cp8pcnK_I z?OL_zBdnXFUoCr2a|hB@kO63}XoQF=aHRpvJB>-;jD9k!Cs4CP8W8XMO;Z+I7gX0+ zm-&J4-P>>ONwHpn9MT3D+H{@|#zG2hYlLFd;x_}IfRbZYU9IX*0Wyj5%AgP`L!*#yM#hxEXTi{2qq7ZUSAhan z8KI~k^*C<)l=opjCH(8p-YsI|LSYuQDm-N9Fq-M>I5{Gtc&UF=7jm-HBqSJN_v5)h ze=a!UJOYPOyXi%I zcg>x2=J4kOrsnWr>u!^$P1r|0(B++6C#!TY#wGWoCW}?VVGtgGY!7T)LbeC7ypu@> zKs7gUp2qWK6SHJr-m6;Y%CO{*!-v0U(z2k{tC50pIyv@OeY zBTE?>aqg22-Z;zP8iRzwm=(L)cmU!(nqD}2o8p*q1k7fJ$g>fn!yF-6rCR$_LgXf{ zfstK!V_8M#?TcuWt)I>ILcSrDe+(?;!F(kjawkqt6^C43G_GaDiFo$%+w|vkGi~5q zEU*!l3hP+nyQa^A=1(>rqu;*?w*f5}f$lVSjEDFlgK=jw3F(FQuAw$5nZt<&a?Ihe zWDz_-E}L>X81quY9KksTDIKAl`l@Cr)n(2H5D$RN;g=8rgo;hUm~!QZ=y_+sLE66g z7FT`4OlpD<{D%vfdyhRHyfx#_iQj7$(oImgZKpS(B3+w*DBylYkz{{kkycc14MP7K zZ-MsHe+ZQh`N&{z(V++Zf&C!I0Oz}&L+m7H``CUgUnItz4=K2Yye6|n>5vEUY`qML@;V)u6Os;l{;sKUSW1M5{qyE?>a{SK zOEh{g=5|~MIoPa!Ox~EcU~be))u5DCvD{W?dF$;9eqrwVgRwDH{bpx1nVT~0GNliB zGO0b+(6Qbx<5}K*nF>#zMGtLWMm>vMmz`~!puj?#dx{V2sZm&6?)_vED(XJFEYd~{ z=STA!DkpPrT8$>xf0_m`fCLn8#`tDWxz(P)K0+|hX8A&jNf%jj3DoyJ`oB;CG*({dx>Zzs2HltgWqgHBrPkwcN zY zPel9;v){2gCByEXD#4@IAy*BatR^krwb)Fg`IL3itar&3_mwLPJ^FQYR2EI%yzZ1U z?{_=FVkK)slOflSu1}=L$ZknZW*~B_?-^RaGH|Cw1*iR3{sd5eM{@NCIzI$Zt`b5 ztc*0yd+IfYh7x3{?2o0m?j9JYOHMKGvZs9yVnt$h&m?1Kd1O+mF{UHa?688@v8m*1 zAs3%(95-kWpArBmX{H>@%}$J#JDM;R7J_efwA%jW*QU|L>}+b8lym`KMAes?jtkMP z{3!$B6ssHMNEeX%@vrZ@;eJf)kvnt3AjR+CIFLI;HCx2wnB3}mgd#FR%VQoAqKPo5 zmuQYy3s_xhOEb^qnHBCmT4F%~DsSV@7l19O_?Zg<#xB@(4=nS5bsa~5acb%d;10k& zY{3YhrJSB$`iD#L2S)s!2mu$ls8|dymQY*7xh?JZlQWtLQY}EjIYv2TI3VO|)yGsN z$5O+Lk4i5ysxSP{kcHw320U#eV)w~aFcZ?;r;Ze?7^Q(32P4-+)J z9fx3-H%XwE+gaCm1(XZ0#83z;oA}<8rgKCSadX|iI!o6o(-I|aVxFL=?%*g;z*%_z zD(;9g&B(hyyDk)JN{dYXKr)`gtdhq4oSbwtnUh_+Tq|WjEmP@(n65eTN)Z8ZZ83of zn>qT@O=aLtg_6;Oxcv?fj&tyx&bNm%x;@QbK;^g*gT_yP1xeGZ5bs84m(jg}cT+{q z-qr`Kd;6Qk7+vEhcO=Q&bEp}a5BYof&Vx^jr*{(ct~E94W;=m7&nX0GqXcO)7~WVV zO^}CpOK>N1{+)<|CBOLsP`fhw(IuVIRz=C05>~ES2i8xZ;*OCuAvii0D`dkL1 zDcsy0A86Q1z6I)h!WJkEm9x;qN{?!W!Rld%bDp~2R7?ur+Es> zW8X~z9c>%ze)a=j>%11c+31d1A{qjXde?|Vwr~Rv__=CKGZ~aC!*wEY%`ql> zw8;+W^gNnYTWWIRc3Ns2)Ul#EJ7$?HTZr$Gdp3GQl~G4%dPqK1m?azJNG9|?vf+49 zX(XjQh)1mF^W|=@W;fVdC)FRf7aI+|=9gxxz4qaJL384JitZB{coMTl@?5#@X2Dm_ zP2zVYS>E2NxD`tb;W!B?>^gd+(YIIiMTzS;IF76#siM$e_q^`bLZn) z2x|5@t=7qX5&NO>a%W-FxZO7|hC*EIlfL@h!uWRvL5F#DjEQ6O-{}c;?}zDQs|=ev z8ywbIs|)GiB#Ij+%_d2BYXR)$$E_?P zM=)RY1~@AYy^Z4>3v>i~Tc0N9v4c~sGT4iJt{L8*`iom%$1&DG227>Gt$Au6upT|E z@<+to;0<>>IPU@TgnJz&fXFxOo!Sz5J`fb7y6~k$eDX2}8FFpdnH3D*-iDa=!oh6E z;5-WFyL)3^x%fM}KqI

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output information contains the Spark version, executor details, properties, etc. -Starting with release _22.10_, the Profiling tool optionally provides optimized RAPIDS -configurations based on the worker's information (see [Auto-Tuner support](#auto-tuner-support)). - -* TOC -{:toc} - -## How to use the Profiling tool - -### Prerequisites - -- Java 8 or above, Spark 3.1.1+ jars -- Spark event log(s) from Spark 2.0 or above version. Supports both rolled and compressed event logs - with `.lz4`, `.lzf`, `.snappy` and `.zstd` suffixes as well as - Databricks-specific rolled and compressed(`.gz`) event logs. -- The tool does not support nested directories. - Event log files or event log directories should be at the top level when specifying a directory. - -Note: Spark event logs can be downloaded from Spark UI using a _Download_ button on the right side, -or can be found in the location specified by `spark.eventLog.dir`. See the -[Apache Spark Monitoring](http://spark.apache.org/docs/latest/monitoring.html) documentation for -more information. - -### Step 1a: Download the tools jar - -- Download the latest RAPIDS Accelerator for Apache Spark tools jar from [Maven repository](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/) - -If you want to compile the jar, please refer to the instructions [here](./spark-qualification-tool.md#How-to-compile-the-tools-jar). - -### Step 1b: Download the Apache Spark 3 distribution - -The Profiling tool requires the Spark 3.x jars to be able to run but does not need an Apache Spark run time. -If you do not already have Spark 3.x installed, -you can download the Spark distribution to any machine and include the jars in the classpath. -- [Download Apache Spark 3.x](http://spark.apache.org/downloads.html) - -### Step 2 How to run the Profiling tool - -The profiling tool parses the Spark CPU or GPU event log(s) and creates an output report. -If necessary, extract the Spark distribution into a local directory. To run the tool, please note the following: - -- Either set `SPARK_HOME` to point to that local directory or add it to the -classpath `java -cp toolsJar:pathToSparkJars/*:...` when you run the Profiling tool. -- Acceptable input event log paths are files or directories containing spark events logs -in the local filesystem, HDFS, S3 or mixed. -- If you are processing a lot of event logs, then use combined or compare mode. Both these modes may need you to increase -the java heap size using `-Xmx` option. For instance, to specify 30 GB heap size `java -Xmx30g`. - -There are 3 modes of operation for the Profiling tool: - - 1. Collection Mode: - Collection mode is the default mode when no other options are specified it simply collects information - on each application individually and outputs a file per application - - ```bash - Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* - com.nvidia.spark.rapids.tool.profiling.ProfileMain [options] - - ``` - Note that this is the only mode that supports the _Auto-Tuner_ option described in more details - in the [Auto-Tuner support](#auto-tuner-support) section. - - 2. Combined Mode: - Combined mode is collection mode but then combines all the applications - together and you get one file for all applications. - - ```bash - Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* - com.nvidia.spark.rapids.tool.profiling.ProfileMain --combined - - ``` - 3. Compare Mode: - Compare mode will combine all the applications information in the same tables into a single file - and also adds in tables to compare stages and sql ids across all of those applications. - The Compare mode will use more memory if comparing lots of applications. - - ```bash - Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* - com.nvidia.spark.rapids.tool.profiling.ProfileMain --compare - - ``` - Note that if you are on an HDFS cluster the default filesystem is likely HDFS for both the input and output - so if you want to point to the local filesystem be sure to include `file:` in the path. - - Example running on files in HDFS: (include $HADOOP_CONF_DIR in classpath) - - ```bash - java -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.profiling.ProfileMain /eventlogDir - ``` - -Run `--help` for more information. - -## Understanding Profiling tool detailed output and examples - -The default output location is the current directory. -The output location can be changed using the `--output-directory` option. -The output goes into a sub-directory named `rapids_4_spark_profile/` inside that output location. -- If running in normal collect mode, it processes event logs individually and outputs files for each application under -a directory named `rapids_4_spark_profile/{APPLICATION_ID}`. It creates a summary text file named `profile.log`. -- If running combine mode the output is put under a directory named `rapids_4_spark_profile/combined/` and creates a summary -text file named `rapids_4_spark_tools_combined.log`. -- If running compare mode the output is put under a directory named `rapids_4_spark_profile/compare/` and creates a summary -text file named `rapids_4_spark_tools_compare.log`. -The output will go into your default filesystem and the tool supports local filesystem or HDFS. - -If you are on an HDFS cluster, then the default filesystem is likely HDFS for both the input and output -so if you want to point to the local filesystem be sure to include `file:` in the path. -There are separate files that are generated under the same sub-directory when using the options to generate query -visualizations or printing the SQL plans. -Optionally if the `--csv` option is specified then it creates a csv file for each table for each application in the -corresponding sub-directory. - -Additional notes: - -- There is a 100 characters limit for each output column. If the result of the column exceeds this limit, it is suffixed with -`...` for that column. -- ResourceProfile ids are parsed for the event logs that are from Spark 3.1 or later. A ResourceProfile allows the user -to specify executor and task requirements for an RDD that will get applied during a stage. This allows the user to change -the resource requirements between stages. - -#### A. Collect Information or Compare Information (if more than 1 event logs are as input and option --compare is specified) - -- Application information -- Application log path mapping -- Data Source information -- Executors information -- Job, stage and SQL ID information -- SQL to stage information -- Rapids related parameters -- Spark Properties -- Rapids Accelerator jar -- SQL Plan Metrics -- WholeStageCodeGen to node mappings (only applies to CPU plans) -- IO Metrics -- Compare Mode: Matching SQL IDs Across Applications -- Compare Mode: Matching Stage IDs Across Applications -- Optionally: SQL Plan for each SQL query -- Optionally: Generates DOT graphs for each SQL query -- Optionally: Generates timeline graph for application - -For example, GPU run vs CPU run performance comparison or different runs with different parameters. - -We can input multiple Spark event logs and this tool can compare environments, executors, and Rapids related Spark parameters. - -- Compare the durations/versions/gpuMode on or off: - -- Application information: - - ``` - +--------+-----------+-----------------------+---------+-------------+-------------+--------+-----------+------------+-------------+ - |appIndex|appName |appId |sparkUser|startTime |endTime |duration|durationStr|sparkVersion|pluginEnabled| - +--------+-----------+-----------------------+---------+-------------+-------------+--------+-----------+------------+-------------+ - |1 |Spark shell|app-20210329165943-0103|user1 |1617037182848|1617037490515|307667 |5.1 min |3.1.1 |false | - |2 |Spark shell|app-20210329170243-0018|user1 |1617037362324|1617038578035|1215711 |20 min |3.1.1 |true | - +--------+-----------+-----------------------+---------+-------------+-------------+--------+-----------+------------+-------------+ - ``` - -- Data Source information: - The details of this output differ between using a Spark Data Source V1 and Data Source V2 reader. - The Data Source V2 truncates the schema, so if you see `...`, then - the full schema is not available. - - ``` - +--------+-----+-------+---------------------------------------------------------------------------------------------------------------------------+-----------------+---------------------------------------------------------------------------------------------+ - |appIndex|sqlID|format |location |pushedFilters |schema | - +--------+-----+-------+---------------------------------------------------------------------------------------------------------------------------+-----------------+---------------------------------------------------------------------------------------------+ - |1 |0 |Text |InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/integration_tests/src/test/resources/trucks-comments.csv]|[] |value:string | - |1 |1 |csv |Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/integration_tests/src/test/re... |PushedFilters: []|_c0:string | - |1 |2 |parquet|Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/lotscolumnsout] |PushedFilters: []|loan_id:bigint,monthly_reporting_period:string,servicer:string,interest_rate:double,curren...| - |1 |3 |parquet|Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/lotscolumnsout] |PushedFilters: []|loan_id:bigint,monthly_reporting_period:string,servicer:string,interest_rate:double,curren...| - |1 |4 |orc |Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/logscolumsout.orc] |PushedFilters: []|loan_id:bigint,monthly_reporting_period:string,servicer:string,interest_rate:double,curren...| - |1 |5 |orc |Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/logscolumsout.orc] |PushedFilters: []|loan_id:bigint,monthly_reporting_period:string,servicer:string,interest_rate:double,curren...| - |1 |6 |json |Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/lotsofcolumnsout.json] |PushedFilters: []|adj_remaining_months_to_maturity:double,asset_recovery_costs:double,credit_enhancement_pro...| - |1 |7 |json |Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/lotsofcolumnsout.json] |PushedFilters: []|adj_remaining_months_to_maturity:double,asset_recovery_costs:double,credit_enhancement_pro...| - |1 |8 |json |Location: InMemoryFileIndex[file:/home/user1/workspace/spark-rapids-another/lotsofcolumnsout.json] |PushedFilters: []|adj_remaining_months_to_maturity:double,asset_recovery_costs:double,credit_enhancement_pro...| - |1 |9 |JDBC |unknown |unknown | | - +--------+-----+-------+---------------------------------------------------------------------------------------------------------------------------+-----------------+---------------------------------------------------------------------------------------------+ - ``` - -- Executor information: - - ``` - +--------+-----------------+------------+-------------+-----------+------------+-------------+--------------+------------------+---------------+-------+-------+ - |appIndex|resourceProfileId|numExecutors|executorCores|maxMem |maxOnHeapMem|maxOffHeapMem|executorMemory|numGpusPerExecutor|executorOffHeap|taskCpu|taskGpu| - +--------+-----------------+------------+-------------+-----------+------------+-------------+--------------+------------------+---------------+-------+-------+ - |1 |0 |1 |4 |11264537395|11264537395 |0 |20480 |1 |0 |1 |0.0 | - |1 |1 |2 |2 |3247335014 |3247335014 |0 |6144 |2 |0 |2 |2.0 | - +--------+-----------------+------------+-------------+-----------+------------+-------------+-------------+--------------+------------------+---------------+-------+-------+ - ``` - -- Matching SQL IDs Across Applications: - - ``` - +-----------------------+-----------------------+ - |app-20210329165943-0103|app-20210329170243-0018| - +-----------------------+-----------------------+ - |0 |0 | - |1 |1 | - |2 |2 | - |3 |3 | - |4 |4 | - +-----------------------+-----------------------+ - ``` - - There is one column per application. There is a row per SQL ID. The SQL IDs are matched - primarily on the structure of the SQL query run, and then on the order in which they were - run. Be aware that this is truly the structure of the query. Two queries that do similar - things, but on different data are likely to match as the same. An effort is made to - also match between CPU plans and GPU plans so in most cases the same query run on the - CPU and on the GPU will match. - -- Matching Stage IDs Across Applications: - - ``` - +-----------------------+-----------------------+ - |app-20210329165943-0103|app-20210329170243-0018| - +-----------------------+-----------------------+ - |31 |31 | - |32 |32 | - |33 |33 | - |39 |38 | - |40 |40 | - |41 |41 | - +-----------------------+-----------------------+ - ``` - - There is one column per application. There is a row per stage ID. If a SQL query matches - between applications, see Matching SQL IDs Across Applications, then an attempt is made - to match stages within that application to each other. This has the same issues with - stages when generating a dot graph. This can be especially helpful when trying to compare - large queries and Spark happened to assign the stage IDs slightly differently, or in some - cases there are a different number of stages because of slight differences in the plan. This - is a best effort, and it is not guaranteed to match up all stages in a plan. - -- SQL to Stage Information (sorted by stage duration): - - Note that not all SQL nodes have a mapping to stage id so some nodes might be missing. - - ``` - +--------+-----+-----+-------+--------------+--------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+ - |appIndex|sqlID|jobID|stageId|stageAttemptId|Stage Duration|SQL Nodes(IDs) | - +--------+-----+-----+-------+--------------+--------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+ - |1 |0 |1 |1 |0 |8174 |Exchange(9),WholeStageCodegen (1)(10),Scan(13) | - |1 |0 |1 |2 |0 |8154 |Exchange(16),WholeStageCodegen (3)(17),Scan(20) | - |1 |0 |1 |3 |0 |2148 |Exchange(2),HashAggregate(4),SortMergeJoin(6),WholeStageCodegen (5)(3),Sort(8),WholeStageCodegen (2)(7),Exchange(9),Sort(15),WholeStageCodegen (4)(14),Exchange(16)| - |1 |0 |1 |4 |0 |126 |HashAggregate(1),WholeStageCodegen (6)(0),Exchange(2) | - +--------+-----+-----+-------+--------------+--------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+ - ``` - -- Compare Rapids related Spark properties side-by-side: - - ``` - Compare Rapids Properties which are set explicitly: - +-------------------------------------------+----------+----------+ - |propertyName |appIndex_1|appIndex_2| - +-------------------------------------------+----------+----------+ - |spark.rapids.memory.pinnedPool.size |null |2g | - |spark.rapids.sql.castFloatToDecimal.enabled|null |true | - |spark.rapids.sql.concurrentGpuTasks |null |2 | - |spark.rapids.sql.enabled |false |true | - |spark.rapids.sql.explain |null |NOT_ON_GPU| - |spark.rapids.sql.incompatibleOps.enabled |null |true | - +-------------------------------------------+----------+----------+ - ``` - -- List rapids-4-spark jars based on classpath: - - ``` - Rapids Accelerator jar: - +--------+------------------------------------------------------------+ - |appIndex|Rapids4Spark jars | - +--------+------------------------------------------------------------+ - |1 |spark://10.10.10.10:43445/jars/rapids-4-spark_2.12-0.5.0.jar| - |2 |spark://10.10.10.11:41319/jars/rapids-4-spark_2.12-0.5.0.jar| - +--------+------------------------------------------------------------+ - ``` - -- Job, stage and SQL ID information(not in `compare` mode yet): - - ``` - Job Information: - +--------+-----+---------+-----+-------------+-------------+ - |appIndex|jobID|stageIds |sqlID|startTime |endTime | - +--------+-----+---------+-----+-------------+-------------+ - |1 |0 |[0] |null |1622846402778|1622846410240| - |1 |1 |[1,2,3,4]|0 |1622846431114|1622846441591| - +--------+-----+---------+-----+-------------+-------------+ - ``` - -- SQL Plan Metrics for Application for each SQL plan node in each SQL: - - These are also called accumulables in Spark. - Note that not all SQL nodes have a mapping to stage id. - - ``` - SQL Plan Metrics for Application: - +--------+-----+------+--------------------+-------------+-----------------------+-------------+----------+--------+ - |appIndex|sqlID|nodeID|nodeName |accumulatorId|name |max_value |metricType|stageIds| - +--------+-----+------+--------------------+-------------+-----------------------+-------------+----------+--------+ - |1 |0 |1 |GpuColumnarExchange |111 |output rows |1111111111 |sum |4,3 | - |1 |0 |1 |GpuColumnarExchange |112 |output columnar batches|222222 |sum |4,3 | - |1 |0 |1 |GpuColumnarExchange |113 |data size |333333333333 |size |4,3 | - |1 |0 |1 |GpuColumnarExchange |114 |shuffle bytes written |444444444444 |size |4,3 | - |1 |0 |1 |GpuColumnarExchange |115 |shuffle records written|555555 |sum |4,3 | - |1 |0 |1 |GpuColumnarExchange |116 |shuffle write time |666666666666 |nsTiming |4,3 | - ``` - -- WholeStageCodeGen to Node Mapping (only for CPU logs): - - ``` - WholeStageCodeGen Mapping: - +--------+-----+------+---------------------+-------------------+------------+ - |appIndex|sqlID|nodeID|SQL Node |Child Node |Child NodeID| - +--------+-----+------+---------------------+-------------------+------------+ - |1 |0 |0 |WholeStageCodegen (6)|HashAggregate |1 | - |1 |0 |3 |WholeStageCodegen (5)|HashAggregate |4 | - |1 |0 |3 |WholeStageCodegen (5)|Project |5 | - |1 |0 |3 |WholeStageCodegen (5)|SortMergeJoin |6 | - |1 |0 |7 |WholeStageCodegen (2)|Sort |8 | - ``` - - -#### B. Analysis - -- Job + Stage level aggregated task metrics -- SQL level aggregated task metrics -- SQL duration, application during, if it contains Dataset or RDD operation, potential problems, executor CPU time percent -- Shuffle Skew Check: (When task's Shuffle Read Size > 3 * Avg Stage-level size) - - Below we will aggregate the task level metrics at different levels - to do some analysis such as detecting possible shuffle skew. - -- Job + Stage level aggregated task metrics: - - ``` - +--------+-------+--------+--------+--------------------+------------+------------+------------+------------+-------------------+------------------------------+---------------------------+-------------------+-------------------+---------------------+-------------+----------------------+-----------------------+-------------------------+-----------------------+---------------------------+--------------+--------------------+-------------------------+---------------------+--------------------------+----------------------+----------------------------+---------------------+-------------------+---------------------+----------------+ - |appIndex|ID |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 |job_0 |3333 |222222 |0 |11111111 |111111 |111 |1111.1 |6666666 |55555 |55555 |55555555 |222222222222 |22222222222 |111111 |0 |0 |0 |222222222 |1 |11111 |11111 |99999 |22222222222 |2222221 |222222222222 |0 |222222222222 |222222222222 |5555555 |444444 | - ``` - -- SQL level aggregated task metrics: - - ``` - SQL level aggregated task metrics: - +--------+------------------------------+-----+--------------------+--------+--------+---------------+---------------+----------------+--------------------+------------+------------+------------+------------+-------------------+------------------------------+---------------------------+-------------------+-------------------+---------------------+-------------+----------------------+-----------------------+-------------------------+-----------------------+---------------------------+--------------+--------------------+-------------------------+---------------------+--------------------------+----------------------+----------------------------+---------------------+-------------------+---------------------+----------------+ - |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 |application_1111111111111_0001|0 |show at :11|1111 |222222 |6666666 |55555555 |55.55 |0 |13333333 |111111 |999 |3333.3 |6666666 |55555 |66666 |11111111 |111111111111 |11111111111 |111111 |0 |0 |0 |888888888 |8 |11111 |11111 |99999 |11111111111 |2222222 |222222222222 |0 |222222222222 |444444444444 |5555555 |444444 | - ``` - -- SQL duration, application during, if it contains Dataset or RDD operation, potential problems, executor CPU time percent: - - ``` - SQL Duration and Executor CPU Time Percent - +--------+-------------------+-----+------------+--------------------------+------------+---------------------------+-------------------------+ - |appIndex|App ID |sqlID|SQL Duration|Contains Dataset or RDD Op|App Duration|Potential Problems |Executor CPU Time Percent| - +--------+-------------------+-----+------------+--------------------------+------------+---------------------------+-------------------------+ - |1 |local-1626104300434|0 |1260 |false |131104 |NESTED COMPLEX TYPE |92.65 | - |1 |local-1626104300434|1 |259 |false |131104 |NESTED COMPLEX TYPE |76.79 | - ``` - -- Shuffle Skew Check: - - ``` - Shuffle Skew Check: (When task's Shuffle Read Size > 3 * Avg Stage-level size) - +--------+-------+--------------+------+-------+---------------+--------------+-----------------+----------------+----------------+----------+----------------------------------------------------------------------------------------------------+ - |appIndex|stageId|stageAttemptId|taskId|attempt|taskDurationSec|avgDurationSec|taskShuffleReadMB|avgShuffleReadMB|taskPeakMemoryMB|successful|reason | - +--------+-------+--------------+------+-------+---------------+--------------+-----------------+----------------+----------------+----------+----------------------------------------------------------------------------------------------------+ - |1 |2 |0 |2222 |0 |111.11 |7.7 |2222.22 |111.11 |0.01 |false |ExceptionFailure(ai.rapids.cudf.CudfException,cuDF failure at: /dddd/xxxxxxx/ccccc/bbbbbbbbb/aaaaaaa| - |1 |2 |0 |2224 |1 |222.22 |8.8 |3333.33 |111.11 |0.01 |false |ExceptionFailure(ai.rapids.cudf.CudfException,cuDF failure at: /dddd/xxxxxxx/ccccc/bbbbbbbbb/aaaaaaa| - +--------+-------+--------------+------+-------+---------------+--------------+-----------------+----------------+----------------+----------+----------------------------------------------------------------------------------------------------+ - ``` - -#### C. Health Check - -- List failed tasks, stages and jobs -- Removed BlockManagers and Executors -- SQL Plan HealthCheck - -Below are examples. - -- Print failed tasks: - - ``` - Failed tasks: - +--------+-------+--------------+------+-------+----------------------------------------------------------------------------------------------------+ - |appIndex|stageId|stageAttemptId|taskId|attempt|failureReason | - +--------+-------+--------------+------+-------+----------------------------------------------------------------------------------------------------+ - |3 |4 |0 |2842 |0 |ExceptionFailure(ai.rapids.cudf.CudfException,cuDF failure at: /home/jenkins/agent/workspace/jenkins| - |3 |4 |0 |2858 |0 |TaskKilled(another attempt succeeded,List(AccumulableInfo(453,None,Some(22000),None,false,true,None)| - |3 |4 |0 |2884 |0 |TaskKilled(another attempt succeeded,List(AccumulableInfo(453,None,Some(21148),None,false,true,None)| - |3 |4 |0 |2908 |0 |TaskKilled(another attempt succeeded,List(AccumulableInfo(453,None,Some(20420),None,false,true,None)| - |3 |4 |0 |3410 |1 |ExceptionFailure(ai.rapids.cudf.CudfException,cuDF failure at: /home/jenkins/agent/workspace/jenkins| - |4 |1 |0 |1948 |1 |TaskKilled(another attempt succeeded,List(AccumulableInfo(290,None,Some(1107),None,false,true,None),| - +--------+-------+--------------+------+-------+----------------------------------------------------------------------------------------------------+ - ``` - -- Print failed stages: - - ``` - Failed stages: - +--------+-------+---------+-------------------------------------+--------+---------------------------------------------------+ - |appIndex|stageId|attemptId|name |numTasks|failureReason | - +--------+-------+---------+-------------------------------------+--------+---------------------------------------------------+ - |3 |4 |0 |attachTree at Spark300Shims.scala:624|1000 |Job 0 cancelled as part of cancellation of all jobs| - +--------+-------+---------+-------------------------------------+--------+---------------------------------------------------+ - ``` - -- Print failed jobs: - - ``` - Failed jobs: - +--------+-----+---------+------------------------------------------------------------------------+ - |appIndex|jobID|jobResult|failureReason | - +--------+-----+---------+------------------------------------------------------------------------+ - |3 |0 |JobFailed|java.lang.Exception: Job 0 cancelled as part of cancellation of all j...| - +--------+-----+---------+------------------------------------------------------------------------+ - ``` - -- SQL Plan HealthCheck: - - Prints possibly unsupported query plan nodes such as `$Lambda` key word means dataset API. - - ``` - +--------+-----+------+--------+---------------------------------------------------------------------------------------------------+ - |appIndex|sqlID|nodeID|nodeName|nodeDescription | - +--------+-----+------+--------+---------------------------------------------------------------------------------------------------+ - |3 |1 |8 |Filter |Filter $line21.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$Lambda$4578/0x00000008019f1840@4b63e04c.apply| - +--------+-----+------+--------+---------------------------------------------------------------------------------------------------+ - ``` - -#### D. Recommended Configuration - -The _Auto-Tuner_ output has 2 main sections: - -1. _Spark Properties_: A list of Apache Spark configurations to tune the performance of the app. - The list is the result of `diff` between the existing app configurations and the recommended - ones. Therefore, a recommendation matches the existing app configuration, it will not show up in - the list. -2. _Comments_: A list of messages to highlight properties that were missing in the app - configurations, or the cause of failure to generate the recommendations. - -**Examples** - -- A succesful run with missing _softwareProperties_: - - ``` - Spark Properties: - --conf spark.executor.cores=16 - --conf spark.executor.instances=8 - --conf spark.executor.memory=32768m - --conf spark.executor.memoryOverhead=7372m - --conf spark.rapids.memory.pinnedPool.size=4096m - --conf spark.rapids.sql.concurrentGpuTasks=2 - --conf spark.sql.files.maxPartitionBytes=512m - --conf spark.sql.shuffle.partitions=200 - --conf spark.task.resource.gpu.amount=0.0625 - - Comments: - - 'spark.executor.instances' was not set. - - 'spark.executor.cores' was not set. - - 'spark.task.resource.gpu.amount' was not set. - - 'spark.rapids.sql.concurrentGpuTasks' was not set. - - 'spark.executor.memory' was not set. - - 'spark.rapids.memory.pinnedPool.size' was not set. - - 'spark.executor.memoryOverhead' was not set. - - 'spark.sql.files.maxPartitionBytes' was not set. - - 'spark.sql.shuffle.partitions' was not set. - - 'spark.sql.adaptive.enabled' should be enabled for better performance. - ``` - -- A successful run with defined _softwareProperties_. In this example, only - two recommendations did not match the existing app configurations: - - ``` - Spark Properties: - --conf spark.executor.instances=8 - --conf spark.sql.shuffle.partitions=200 - - Comments: - - 'spark.sql.shuffle.partitions' was not set. - ``` - -- Failing to load the worker info: - - ``` - Cannot recommend properties. See Comments. - - Comments: - - java.io.FileNotFoundException: File worker-info.yaml does not exist - - 'spark.executor.memory' should be set to at least 2GB/core. - - 'spark.executor.instances' should be set to (gpuCount * numWorkers). - - 'spark.task.resource.gpu.amount' should be set to Max(1, (numCores / gpuCount)). - - 'spark.rapids.sql.concurrentGpuTasks' should be set to Max(4, (gpuMemory / 8G)). - - 'spark.rapids.memory.pinnedPool.size' should be set to 2048m. - - 'spark.sql.adaptive.enabled' should be enabled for better performance. - ``` - -### Generating Visualizations - -- Print SQL Plans (`--print-plans` option): - Prints the SQL plan as a text string to a file named `planDescriptions.log`. - -- Generate DOT graph for each SQL (`--generate-dot` option): - - ``` - Generated DOT graphs for app app-20210507103057-0000 to /path/. in 17 second(s) - ``` - - A dot file will be generated for each query in the application. - Once the DOT file is generated, you can install [graphviz](http://www.graphviz.org) to convert the DOT file - as a graph in pdf format using below command: - - ```bash - dot -Tpdf ./app-20210507103057-0000-query-0/0.dot > app-20210507103057-0000.pdf - ``` - - Or to svg using - - ```bash - dot -Tsvg ./app-20210507103057-0000-query-0/0.dot > app-20210507103057-0000.svg - ``` - - The pdf or svg file has the SQL plan graph with metrics. The svg file will act a little - more like the Spark UI and include extra information for nodes when hovering over it with - a mouse. - - As a part of this an effort is made to associate parts of the graph with the Spark stage it is a - part of. This is not 100% accurate. Some parts of the plan like `TakeOrderedAndProject` may - be a part of multiple stages and only one of the stages will be selected. `Exchanges` are purposely - left out of the sections associated with a stage because they cover at least 2 stages and possibly - more. In other cases we may not be able to determine what stage something was a part of. In those - cases we mark it as `UNKNOWN STAGE`. This is because we rely on metrics to link a node to a stage. - If a stage has no metrics, like if the query crashed early, we cannot establish that link. - -- Generate timeline for application (`--generate-timeline` option): - - The output of this is an [svg](https://en.wikipedia.org/wiki/Scalable_Vector_Graphics) file - named `timeline.svg`. Most web browsers can display this file. It is a - timeline view similar to Apache Spark's - [event timeline](https://spark.apache.org/docs/latest/web-ui.html). - - This displays several data sections. - - 1. **Tasks** This shows all tasks in the application divided by executor. Please note that this - tries to pack the tasks in the graph. It does not represent actual scheduling on CPU cores. - The tasks are labeled with the time it took for them to run. There is a breakdown of some metrics - per task in the lower half of the task block with different colors used to designate different - metrics. - 1. Yellow is the deserialization time for the task as reported by Spark. This works for both CPU - and GPU tasks. - 2. White is the read time for a task. This is a combination of the "buffer time" GPU SQL metric - and the shuffle read time as reported by Spark. The shuffle time works for both CPU and GPU - tasks, but "buffer time" only is reported for GPU accelerated file reads. - 3. Red is the semaphore wait time. This is the amount of time a task spent waiting to get access - to the GPU. When processing logs generated by versions of the spark rapids plugin prior to - 23.04 this would only show up on GPU tasks when DEBUG metrics are enabled. For logs generated - with 23.04 and above it is always on. It does not apply to CPU tasks, as they don't go through - the Semaphore. - 4. Green is the "op time" SQL metric along with a few other metrics that also indicate the amount - of time the GPU was being used to process data. This is GPU specific. - 5. Blue is the write time for a task. This is the "write time" SQL metric used when writing out - results as files using GPU acceleration, or it is the shuffle write time as reported by Spark. - The shuffle metrics work for both CPU and GPU tasks, but the "write time" metrics is GPU specific. - 6. Anything else is time that is not accounted for by these metrics. Typically, this is time - spent on the CPU, but could also include semaphore wait time as DEBUG metrics are not on by - default. - 2. **STAGES** This shows the stages times reported by Spark. It starts with when the stage was - scheduled and ends when Spark considered the stage done. - 3. **STAGE RANGES** This shows the time from the start of the first task to the end of the last - task. Often a stage is scheduled, but there are not enough resources in the cluster to run it. - This helps to show. How long it takes for a task to start running after it is scheduled, and in - many cases how long it took to run all the tasks in the stage. This is not always true because - Spark can intermix tasks from different stages. - 4. **JOBS** This shows the time range reported by Spark from when a job was scheduled to when it - completed. - 5. **SQL** This shows the time range reported by Spark from when a SQL statement was scheduled to - when it completed. - -Tasks and stages all are color coordinated to help know what tasks are associated with a given -stage. Jobs and SQL are not color coordinated. - -## Profiling tool options - -``` -Profiling tool for the RAPIDS Accelerator and Apache Spark - -Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* - com.nvidia.spark.rapids.tool.profiling.ProfileMain [options] - - - -a, --auto-tuner Toggle AutoTuner module. - --combined Collect mode but combine all applications into - the same tables. - -c, --compare Compare Applications (Note this may require - more memory if comparing a large number of - applications). Default is false. - --csv Output each table to a CSV file as well - creating the summary text file. - -f, --filter-criteria Filter newest or oldest N eventlogs based on - application start timestamp for processing. - Filesystem based filtering happens before - application based filtering (see start-app-time). - eg: 100-newest-filesystem (for processing newest - 100 event logs). eg: 100-oldest-filesystem (for - processing oldest 100 event logs). - -g, --generate-dot Generate query visualizations in DOT format. - Default is false - --generate-timeline Write an SVG graph out for the full - application timeline. - -m, --match-event-logs Filter event logs whose filenames contain the - input string - -n, --num-output-rows Number of output rows for each Application. - Default is 1000 - --num-threads Number of thread to use for parallel - processing. The default is the number of cores - on host divided by 4. - -o, --output-directory Base output directory. Default is current - directory for the default filesystem. The - final output will go into a subdirectory - called rapids_4_spark_profile. It will - overwrite any existing files with the same - name. - -p, --print-plans Print the SQL plans to a file named - 'planDescriptions.log'. - Default is false. - --platform Cluster platform where Spark GPU workloads were - executed. Options include onprem, dataproc, emr, - databricks. - Default is onprem. - -s, --start-app-time Filter event logs whose application start - occurred within the past specified time - period. Valid time periods are - min(minute),h(hours),d(days),w(weeks),m(months). - If a period is not specified it defaults to - days. - -t, --timeout Maximum time in seconds to wait for the event - logs to be processed. Default is 24 hours - (86400 seconds) and must be greater than 3 - seconds. If it times out, it will report what - it was able to process up until the timeout. - -w, --worker-info File path containing the system information of - a worker node. It is assumed that all workers - are homogenous. It requires the AutoTuner to - be enabled. Default is ./worker_info.yaml - -h, --help Show help message - - trailing arguments: - eventlog (required) Event log filenames(space separated) or directories - containing event logs. eg: s3a:///eventlog1 - /path/to/eventlog2 -``` - -### Auto-Tuner support - -Starting with release _22.10_, the Profiling tool a new _Auto-Tuner_ that aims at optimizing -Apache Spark applications by recommending a set of configurations to tune the performance of -Rapids accelerator. - -Currently, the _Auto-Tuner_ calculates a set of configurations that impact the performance of Apache -Spark apps executing on GPU. Those calculations can leverage cluster information -(e.g. memory, cores, Spark default configurations) as well as information processed in the -application event logs. Note that the tool also will recommend settings for the application assuming -that the job will be able to use all the cluster resources (CPU and GPU) when it is running. - -The values loaded from the app logs have higher precedence than the default configs. -Please refer to [Understanding the Profiling tool output](#d-recommended-configuration) for -more details on the output of the _Auto-Tuner_. - -Note the following _Auto-Tuner_ limitations: - -- It is currently only supported in the _Collection Mode_ (see [the 3 different modes](#step-2-how-to-run-the-profiling-tool)), and -- It is assumed that all the _worker_ nodes on the cluster are homogenous. - -To run the _Auto-Tuner_, enable the `auto-tuner` flag and pass a valid `--worker-info `. -The _Auto-Tuner_ needs to learn the system properties of the _worker_ nodes that run application -code in the cluster. The argument `FILE_PATH` can either be local or remote file (i.e., HDFS). -A template of the worker information is shown below: - - ``` - system: - numCores: 32 - memory: 212992MiB - numWorkers: 5 - gpu: - memory: 15109MiB - count: 4 - name: T4 - softwareProperties: - spark.driver.maxResultSize: 7680m - spark.driver.memory: 15360m - spark.executor.cores: '8' - spark.executor.instances: '2' - spark.executor.memory: 47222m - spark.executorEnv.OPENBLAS_NUM_THREADS: '1' - spark.scheduler.mode: FAIR - spark.sql.cbo.enabled: 'true' - spark.ui.port: '0' - spark.yarn.am.memory: 640m - ``` - - -| Property | Optional | If Missing | -|--------------------|:--------:|------------------------------------------------------------------------------------------------------------------------------| -| system.numCores | No | _Auto-Tuner_ does not calculate recommendations | -| system.memory | No | _Auto-Tuner_ does not calculate any recommendations | -| system.numWorkers | Yes | Default: 1 | -| gpu.name | Yes | Default: T4 (Nvidia Tesla T4) | -| gpu.memory | Yes | Default: 16G | -| softwareProperties | Yes | This section is optional. The _Auto-Tuner_ reads the configs within the logs of the Apache Spark apps with higher precedence | - -## Profiling tool metrics definitions - -All the metrics definitions can be found in the -[executor task metrics doc](https://spark.apache.org/docs/latest/monitoring.html#executor-task-metrics) / -[executor metrics doc](https://spark.apache.org/docs/latest/monitoring.html#executor-metrics) or -the [SPARK webUI doc](https://spark.apache.org/docs/latest/web-ui.html#content). diff --git a/core/docs/spark-qualification-tool.md b/core/docs/spark-qualification-tool.md deleted file mode 100644 index 0f9db8720..000000000 --- a/core/docs/spark-qualification-tool.md +++ /dev/null @@ -1,1027 +0,0 @@ ---- -layout: page -title: Qualification Tool -nav_order: 8 ---- -# Qualification Tool - -The Qualification tool analyzes Spark events generated from CPU based Spark applications to help quantify -the expected acceleration of migrating a Spark application or query to GPU. - -The tool first analyzes the CPU event log and determine which operators are likely to run on the GPU. -The tool then uses estimates from historical queries and benchmarks to estimate a speed-up at an individual operator -level to calculate how much a specific operator would accelerate on GPU for the specific query or application. -It calculates an _"Estimated GPU App Duration"_ by adding up the accelerated operator durations along with durations -that could not run on GPU because they are unsupported operators or not SQL/Dataframe. - -This tool is intended to give the users a starting point and does not guarantee the -queries or applications with the highest _recommendation_ will actually be accelerated the most. Currently, -it reports by looking at the amount of time spent in tasks of SQL Dataframe operations. Note that the qualification -tool estimates assume that the application is run on a dedicated cluster where it can use all of the available -Spark resources. - -The estimations for GPU duration are available for different environments and are based on benchmarks run in the -applicable environments. Here are the cluster information for the ETL benchmarks used for the estimates: - -| Environment | CPU Cluster | GPU Cluster | -|--------------------------|-------------------|--------------------------------| -| On-prem | 8x 128-core | 8x 128-core + 8x A100 40 GB | -| Dataproc (T4) | 4x n1-standard-32 | 4x n1-standard-32 + 8x T4 16GB | -| Dataproc (L4) | 8x n1-standard-16 | 8x g2-standard-16 | -| Dataproc Serverless (L4) | 8x 16 cores | 8x 16 cores + 8x L4 24GB | -| Dataproc GKE (T4) | 8x n1-standard-32 | 8x n1-standard-32 + 8x T4 16GB | -| Dataproc GKE (L4) | 8x n1-standard-32 | 8x n1-standard-32 + 8x L4 24GB | -| EMR (T4) | 8x m5d.8xlarge | 4x g4dn.12xlarge | -| EMR (A10) | 8x m5d.8xlarge | 8x g5.8xlarge | -| Databricks AWS | 8x m6gd.8xlage | 8x g5.8xlarge | -| Databricks Azure | 8x E8ds_v4 | 8x NC8as_T4_v3 | - -Note that all benchmarks were run using the [NDS benchmark](https://github.com/NVIDIA/spark-rapids-benchmarks/tree/dev/nds) at SF3K (3 TB). - -> **Disclaimer!** -> Estimates provided by the Qualification tool are based on the currently supported "_SparkPlan_" or "_Executor Nodes_" -> used in the application. It currently does not handle all the expressions or datatypes used. -> Please refer to "[Understanding Execs report](#execs-report)" section and the -> "[Supported Operators](https://github.com/NVIDIA/spark-rapids/blob/main/docs/supported_ops.md)" guide to check the types and expressions you are using are supported. - -This document covers below topics: - -* TOC -{:toc} - -## How to use the Qualification tool - -The Qualification tool can be run in three different ways. One is to run it as a standalone tool on the -Spark event logs after the application(s) have run, the second is to be integrated into a running Spark -application using explicit API calls, and the third is to install a Spark listener which can output -results on a per SQL query basis. - -In running the qualification tool standalone on Spark event logs, the tool can be run as a user tool command -via a [pip package](https://pypi.org/project/spark-rapids-user-tools/) for CSP environments (Google Dataproc, -AWS EMR, Databricks AWS) or as a java application for other environments. - -## Running the Qualification tool standalone for CSP environments on Spark event logs -### User Tools Prerequisites and Setup for CSP environments - -* [Dataproc](https://github.com/NVIDIA/spark-rapids-tools/blob/main/user_tools/docs/user-tools-dataproc.md) -* [EMR](https://github.com/NVIDIA/spark-rapids-tools/blob/main/user_tools/docs/user-tools-aws-emr.md) -* [Databricks AWS](https://github.com/NVIDIA/spark-rapids-tools/blob/main/user_tools/docs/user-tools-databricks-aws.md) - -### Qualify CPU Workloads for Potential Cost Savings and Acceleration with GPUs - -The qualification tool will run against logs from your CSP environment and then will output the applications -recommended for acceleration along with estimated speed-up and cost saving metrics. - -Usage: `spark_rapids_user_tools qualification --cpu_cluster --eventlogs ` - -The supported CSPs are *dataproc*, *emr*, and *databricks-aws*. The EVENTLOGS-PATH should be the storage location -for your eventlogs. For Dataproc, it should be set to the GCS path. For EMR and Databricks-AWS, it should be set to -the S3 path. THE CLUSTER can be a live cluster or a configuration file representing the cluster instances and size. -More details are in the above documentation links per CSP environment - -Help (to see all options available): `spark_rapids_user_tools qualification --help` - -Example output: -``` -+----+------------+--------------------------------+----------------------+-----------------+-----------------+---------------+-----------------+ -| | App Name | App ID | Recommendation | Estimated GPU | Estimated GPU | App | Estimated GPU | -| | | | | Speedup | Duration(s) | Duration(s) | Savings(%) | -|----+------------+--------------------------------+----------------------+-----------------+-----------------+---------------+-----------------| -| 0 | query24 | application_1664888311321_0011 | Strongly Recommended | 3.49 | 257.18 | 897.68 | 59.70 | -| 1 | query78 | application_1664888311321_0009 | Strongly Recommended | 3.35 | 113.89 | 382.35 | 58.10 | -| 2 | query23 | application_1664888311321_0010 | Strongly Recommended | 3.08 | 325.77 | 1004.28 | 54.37 | -| 3 | query64 | application_1664888311321_0008 | Strongly Recommended | 2.91 | 150.81 | 440.30 | 51.82 | -| 4 | query50 | application_1664888311321_0003 | Recommended | 2.47 | 101.54 | 250.95 | 43.08 | -| 5 | query16 | application_1664888311321_0005 | Recommended | 2.36 | 106.33 | 251.95 | 40.63 | -| 6 | query38 | application_1664888311321_0004 | Recommended | 2.29 | 67.37 | 154.33 | 38.59 | -| 7 | query87 | application_1664888311321_0006 | Recommended | 2.25 | 75.67 | 170.69 | 37.64 | -| 8 | query51 | application_1664888311321_0002 | Recommended | 1.53 | 53.94 | 82.63 | 8.18 | -+----+------------+--------------------------------+----------------------+-----------------+-----------------+---------------+-----------------+ -``` - -## Running the Qualification tool standalone on Spark event logs - -### Prerequisites -- Java 8 or above, Spark 3.1.1+ jars. -- Spark event log(s) from Spark 2.0 or above version. Supports both rolled and compressed event logs - with `.lz4`, `.lzf`, `.snappy` and `.zstd` suffixes as well as Databricks-specific rolled and compressed(.gz) event logs. -- The tool does not support nested directories. - Event log files or event log directories should be at the top level when specifying a directory. - -Note: Spark event logs can be downloaded from Spark UI using a "Download" button on the right side, -or can be found in the location specified by `spark.eventLog.dir`. See the -[Apache Spark Monitoring](http://spark.apache.org/docs/latest/monitoring.html) documentation for -more information. - -### Step 1 Download the tools jar and Apache Spark 3 Distribution - -The Qualification tool require the Spark 3.x jars to be able to run but do not need an Apache Spark run time. -If you do not already have Spark 3.x installed, you can download the Spark distribution to -any machine and include the jars in the classpath. -- Download the latest jar from [Maven repository](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/) -- [Download Apache Spark 3.x](http://spark.apache.org/downloads.html) - Spark 3.1.1 for Apache Hadoop is recommended - -### Step 2 Run the Qualification tool - -1. The Qualification tool reads the log files and process them in-memory. So the heap memory should be increased when - processing large volume of events. It is recommended to pass VM options `-Xmx10g` and adjust according to the - number-of-apps / size-of-logs being processed. - ``` - export QUALIFICATION_HEAP=-Xmx10g - ``` - -2. Event logs stored on a local machine: - - Extract the Spark distribution into a local directory if necessary. - - Either set SPARK_HOME to point to that directory or just put the path inside of the classpath - `java -cp toolsJar:pathToSparkJars/*:...` when you run the Qualification tool. - - This tool parses the Spark CPU event log(s) and creates an output report. Acceptable inputs are either individual or - multiple event logs files or directories containing spark event logs in the local filesystem, HDFS, S3 or mixed. - - ```bash - Usage: java ${QUALIFICATION_HEAP} \ - -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain [options] - - ``` - - ```bash - Sample: java ${QUALIFICATION_HEAP} \ - -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain /usr/logs/app-name1 - ``` - -3. Event logs stored on an on-premises HDFS cluster: - - Example running on files in HDFS: (include `$HADOOP_CONF_DIR` in classpath) - - ```bash - Usage: java ${QUALIFICATION_HEAP} \ - -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain /eventlogDir - ``` - - Note, on an HDFS cluster, the default filesystem is likely HDFS for both the input and output - so if you want to point to the local filesystem be sure to include file: in the path. - -### Qualification tool options - Note: `--help` should be before the trailing event logs. - -```bash -java -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain --help - -RAPIDS Accelerator Qualification tool for Apache Spark - -Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* - com.nvidia.spark.rapids.tool.qualification.QualificationMain [options] - - - --all Apply multiple event log filtering criteria - and process only logs for which all - conditions are satisfied.Example: - --all -> result is - AND AND . - Default is all=true - --any Apply multiple event log filtering criteria - and process only logs for which any condition - is satisfied.Example: - --any -> result is OR - OR - -a, --application-name Filter event logs by application name. The - string specified can be a regular expression, - substring, or exact match. For filtering - based on complement of application name, use - ~APPLICATION_NAME. i.e Select all event logs - except the ones which have application name - as the input string. - -f, --filter-criteria Filter newest or oldest N eventlogs based on - application start timestamp, unique - application name or filesystem timestamp. - Filesystem based filtering happens before any - application based filtering.For application - based filtering, the order in which filters - areapplied is: application-name, - start-app-time, filter-criteria.Application - based filter-criteria are:100-newest (for - processing newest 100 event logs based on - timestamp insidethe eventlog) i.e application - start time) 100-oldest (for processing - oldest 100 event logs based on timestamp - insidethe eventlog) i.e application start - time) 100-newest-per-app-name (select at - most 100 newest log files for each unique - application name) 100-oldest-per-app-name - (select at most 100 oldest log files for each - unique application name)Filesystem based - filter criteria are:100-newest-filesystem - (for processing newest 100 event logs based - on filesystem timestamp). - 100-oldest-filesystem (for processing oldest - 100 event logsbased on filesystem timestamp). - -h, --html-report Default is to generate an HTML report. - --no-html-report Disables generating the HTML report. - -m, --match-event-logs Filter event logs whose filenames contain the - input string. Filesystem based filtering - happens before any application based - filtering. - --max-sql-desc-length Maximum length of the SQL description - string output with the per sql output. - Default is 100. - --ml-functions Report if there are any SparkML or Spark XGBoost - functions in the eventlog. - -n, --num-output-rows Number of output rows in the summary report. - Default is 1000. - --num-threads Number of thread to use for parallel - processing. The default is the number of - cores on host divided by 4. - --order Specify the sort order of the report. desc or - asc, desc is the default. desc (descending) - would report applications most likely to be - accelerated at the top and asc (ascending) - would show the least likely to be accelerated - at the top. - -o, --output-directory Base output directory. Default is current - directory for the default filesystem. The - final output will go into a subdirectory - called rapids_4_spark_qualification_output. - It will overwrite any existing directory with - the same name. - -p, --per-sql Report at the individual SQL query level. - --platform Cluster platform where Spark CPU workloads were - executed. Options include onprem, dataproc-t4, - dataproc-l4, dataproc-serverless-l4, dataproc-gke-t4, - dataproc-gke-l4, emr-t4, emr-a10, databricks-aws, - and databricks-azure. Default is onprem. - -r, --report-read-schema Whether to output the read formats and - datatypes to the CSV file. This can be very - long. Default is false. - --spark-property ... Filter applications based on certain Spark - properties that were set during launch of the - application. It can filter based on key:value - pair or just based on keys. Multiple configs - can be provided where the filtering is done - if any of theconfig is present in the - eventlog. filter on specific configuration: - --spark-property=spark.eventLog.enabled:truefilter - all eventlogs which has config: - --spark-property=spark.driver.portMultiple - configs: - --spark-property=spark.eventLog.enabled:true - --spark-property=spark.driver.port - --speedup-factor-file Custom speedup factor file used to get estimated - GPU speedup that is specific to the user's environment. - If the file is not provided, it defaults to use the - speedup factor files included in the jar. - -s, --start-app-time Filter event logs whose application start - occurred within the past specified time - period. Valid time periods are - min(minute),h(hours),d(days),w(weeks),m(months). - If a period is not specified it defaults to - days. - -t, --timeout Maximum time in seconds to wait for the event - logs to be processed. Default is 24 hours - (86400 seconds) and must be greater than 3 - seconds. If it times out, it will report what - it was able to process up until the timeout. - -u, --user-name Applications which a particular user has - submitted. - --help Show help message - - trailing arguments: - eventlog (required) Event log filenames(space separated) or directories - containing event logs. eg: s3a:///eventlog1 - /path/to/eventlog2 -``` - -Example commands: -- Process the 10 newest logs, and only output the top 3 in the output: - -```bash -java ${QUALIFICATION_HEAP} \ - -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain -f 10-newest -n 3 /eventlogDir -``` - -- Process last 100 days' logs: - -```bash -java ${QUALIFICATION_HEAP} \ - -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain -s 100d /eventlogDir -``` - -- Process only the newest log with the same application name: - -```bash -java ${QUALIFICATION_HEAP} \ - -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain -f 1-newest-per-app-name /eventlogDir -``` - -- Parse ML functions from the eventlog: - -```bash -java ${QUALIFICATION_HEAP} \ - -cp ~/rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ - com.nvidia.spark.rapids.tool.qualification.QualificationMain --ml-functions /eventlogDir -``` - -Note: the “regular expression” used by `-a` option is based on -[java.util.regex.Pattern](https://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html). - -### The Qualification tool output - -After the above command is executed, the summary report goes to STDOUT and by default it outputs -log/CSV files under `./rapids_4_spark_qualification_output/` that contain the processed applications. -The output will go into your default filesystem and it supports both local filesystem and HDFS. -Note that if you are on an HDFS cluster the default filesystem is likely HDFS for both the input and output. -If you want to point to the local filesystem be sure to include `file:` in the path. - -The Qualification tool generates a brief summary on the STDOUT, which also gets saved as a text file. -The detailed report of the processed apps is saved as a set of CSV files that can be used for post-processing. -The CSV reports include the estimated performance if the app is run on the GPU for each of the following: -_app execution_; _stages_; and _execs_. - -Starting with release "_22.06_", the default is to generate the report into two different formats: -text files; and HTML. - -The tree structure of the output directory `${OUTPUT_FOLDER}/rapids_4_spark_qualification_output` is as follows: - -```bash - rapids_4_spark_qualification_output - ├── rapids_4_spark_qualification_output.csv - ├── rapids_4_spark_qualification_output.log - ├── rapids_4_spark_qualification_output_persql.log - ├── rapids_4_spark_qualification_output_persql.csv - ├── rapids_4_spark_qualification_output_execs.csv - ├── rapids_4_spark_qualification_output_stages.csv - ├── rapids_4_spark_qualification_output_mlfunctions.csv - ├── rapids_4_spark_qualification_output_mlfunctions_totalduration.csv - └── ui - ├── assets - │   ├── bootstrap/ - │   ├── datatables/ - │   ├── jquery/ - │   ├── mustache-js/ - │   └── spur/ - ├── css - │   └── rapids-dashboard.css - ├── html - │   ├── application.html - │   ├── index.html - │   ├── raw.html - │   └── sql-recommendation.html - └── js - ├── app-report.js - ├── data-output.js - ├── per-sql-report.js - ├── qual-report.js - ├── raw-report.js - ├── ui-config.js - └── uiutils.js -``` - -For information on the files content and processing the Qualification report and the recommendation, please refer -to [Understanding the Qualification tool output](#understanding-the-qualification-tool-output) and -[Output Formats](#output-formats) sections below. - -## Running using a Spark Listener - -We provide a Spark Listener that can be installed at application start that will produce output -for each SQL queries in the running application and indicate if that query is a good fit to try -with the Rapids Accelerator for Spark. - -### Prerequisites -- Java 8 or above, Spark 3.1.1+ - -### Download the tools jar -- Download the latest jar from [Maven repository](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/) - -### Configuration - -Add the RunningQualificationEventProcess to the spark listeners configuration: -`spark.extraListeners=org.apache.spark.sql.rapids.tool.qualification.RunningQualificationEventProcessor` - -The user should specify the output directory if they want the output to go to separate -files, otherwise it will go to the Spark driver log. If the output directory is specified, it outputs -two different files, one csv and one pretty printed log file. The output directory can be a local directory -or point to a distributed file system or blobstore like S3. - - `spark.rapids.qualification.outputDir` - -By default, this will output results for 10 SQL queries per file and will -keep 100 files. This behavior is because many blob stores don't show files until -they are fully written so you wouldn't be able to see the results for a running -application until it finishes the number of SQL queries per file. This behavior -can be configured with the following configs. - - `spark.rapids.qualification.output.numSQLQueriesPerFile` - default 10 - - `spark.rapids.qualification.output.maxNumFiles` - default 100 - -### Run the Spark application - -Run the application and include the tools jar, `spark.extraListeners` config and optionally the other -configs to control the tools behavior. - -For example: - -```bash -$SPARK_HOME/bin/spark-shell \ ---jars rapids-4-spark-tools_2.12-.jar \ ---conf spark.extraListeners=org.apache.spark.sql.rapids.tool.qualification.RunningQualificationEventProcessor \ ---conf spark.rapids.qualification.outputDir=/tmp/qualPerSqlOutput \ ---conf spark.rapids.qualification.output.numSQLQueriesPerFile=5 \ ---conf spark.rapids.qualification.output.maxNumFiles=10 -``` - -After running some SQL queries you can look in the output directory and see files like: - -``` -rapids_4_spark_qualification_output_persql_0.csv -rapids_4_spark_qualification_output_persql_0.log -rapids_4_spark_qualification_output_persql_1.csv -rapids_4_spark_qualification_output_persql_1.log -rapids_4_spark_qualification_output_persql_2.csv -rapids_4_spark_qualification_output_persql_2.log -``` - -See the [Understanding the Qualification tool output](#understanding-the-qualification-tool-output) -section on the file contents details. - -## Running the Qualification tool inside a running Spark application using the API - -### Prerequisites -- Java 8 or above, Spark 3.1.1+ - -### Download the tools jar -- Download the latest jar from [Maven repository](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/) - -### Modify your application code to call the api's - -Currently only Scala api's are supported. Note this does not support reporting at the per sql level currently. This can be done manually by just wrapping and reporting around those queries instead of the entire application. - -Create the `RunningQualicationApp`: -``` -val qualApp = new com.nvidia.spark.rapids.tool.qualification.RunningQualificationApp() -``` - -Get the event listener from it and install it as a Spark listener: -``` -val listener = qualApp.getEventListener -spark.sparkContext.addSparkListener(listener) -``` - -Run your queries and then get the summary or detailed output to see the results. - -The summary output api: -``` -/** - * Get the summary report for qualification. - * @param delimiter The delimiter separating fields of the summary report. - * @param prettyPrint Whether to including the separate at start and end and - * add spacing so the data rows align with column headings. - * @return String of containing the summary report. - */ -getSummary(delimiter: String = "|", prettyPrint: Boolean = true): String -``` - -The detailed output api: -``` -/** - * Get the detailed report for qualification. - * @param delimiter The delimiter separating fields of the summary report. - * @param prettyPrint Whether to including the separate at start and end and - * add spacing so the data rows align with column headings. - * @return String of containing the detailed report. - */ -getDetailed(delimiter: String = "|", prettyPrint: Boolean = true, reportReadSchema: Boolean = false): String -``` - -Example: -``` -// run your sql queries ... - -// To get the summary output: -val summaryOutput = qualApp.getSummary() - -// To get the detailed output: -val detailedOutput = qualApp.getDetailed() - -// print the output somewhere for user to see -println(summaryOutput) -println(detailedOutput) -``` - -If you need to specify the tools jar as a maven dependency to compile the Spark application: -``` - - com.nvidia - rapids-4-spark-tools_2.12 - ${version} - -``` - -### Run the Spark application -- Run your Spark application and include the tools jar you downloaded with the spark '--jars' options and -view the output wherever you had it printed. - -For example, if running the spark-shell: -``` -$SPARK_HOME/bin/spark-shell --jars rapids-4-spark-tools_2.12-.jar -``` - -## Understanding the Qualification tool output - -For each processed Spark application, the Qualification tool generates two main fields to help quantify the expected -acceleration of migrating a Spark application or query to GPU. - -1. `Estimated GPU Duration`: predicted runtime of the app if it was run on GPU. It is the sum of the accelerated - operator durations and ML functions duration(if applicable) along with durations that could not run on GPU because - they are unsupported operators or not SQL/Dataframe. -2. `Estimated Speed-up`: the estimated speed-up is simply the original CPU duration of the app divided by the - estimated GPU duration. That will estimate how much faster the application would run on GPU. - -The lower the estimated GPU duration, the higher the "_Estimated Speed-up_". -The processed applications or queries are ranked by the "_Estimated Speed-up_". Based on how high the estimated speed-up, -the tool classifies the applications into the following different categories: - -- `Strongly Recommended` -- `Recommended` -- `Not Recommended` -- `Not Applicable`: indicates that the app has job or stage failures. - -As mentioned before, the tool does not guarantee the applications or queries with the highest _recommendation_ will actually be -accelerated the most. Please refer to [Supported Operators](https://github.com/NVIDIA/spark-rapids/blob/main/docs/supported_ops.md) section. - -In addition to the _recommendation_, the Qualification tool reports a set of metrics in tasks of SQL Dataframe operations -within the scope of: "_Entire App_"; "_Stages_"; and "_Execs_". The report is divided into three main levels. The fields -of each level are described in details in the following sections: [Detailed App Report](#detailed-app-report), -[Stages report](#stages-report), and [Execs report](#execs-report). Then we describe the output formats and their file -locations in [Output Formats](#output-formats) section. - -There is an option to print a report at the SQL query level in addition to the application level. - -### Detailed App report - -The report represents the entire app execution, including unsupported operators and non-SQL operations. - -1. _App Name_ -2. _App ID_ -3. _Recommendation_: recommendation based on `Estimated Speed-up Factor`, where - an app can be "_Strongly Recommended_", "_Recommended_", "_Not Recommended_", - or "_Not Applicable_". The latter indicates that the app has job or stage failures. -4. _App Duration_: wall-Clock time measured since the application starts till it is completed. - If an app is not completed an estimated completion time would be computed. -5. _SQL DF duration_: wall-Clock time duration that includes only SQL-Dataframe queries. -6. _GPU Opportunity_: wall-Clock time that shows how much of the SQL duration and ML functions(if applicable) can be accelerated on the GPU. -7. _Estimated GPU Duration_: predicted runtime of the app if it was run on GPU. It is the sum of the accelerated - operator durations and ML functions durations(if applicable) along with durations that could not run on GPU because they are unsupported operators or not SQL/Dataframe. -8. _Estimated GPU Speed-up_: the speed-up is simply the original CPU duration of the app divided by the - estimated GPU duration. That will estimate how much faster the application would run on GPU. -9. _Estimated GPU Time Saved_: estimated wall-Clock time saved if it was run on the GPU. -10. _SQL Dataframe Task Duration_: amount of time spent in tasks of SQL Dataframe operations. -11. _Executor CPU Time Percent_: this is an estimate at how much time the tasks spent doing processing on the CPU vs waiting on IO. - This is not always a good indicator because sometimes the IO that is encrypted and the CPU has to do work to decrypt it, - so the environment you are running on needs to be taken into account. -12. _SQL Ids with Failures_: SQL Ids of queries with failed jobs. -13. _Unsupported Read File Formats and Types_: looks at the Read Schema and - reports the file formats along with types which may not be fully supported. - Example: `JDBC[*]`. Note that this is based on the current version of the plugin and - future versions may add support for more file formats and types. -14. _Unsupported Write Data Format_: reports the data format which we currently don’t support, i.e. - if the result is written in JSON or CSV format. -15. _Complex Types_: looks at the Read Schema and reports if there are any complex types(array, struct or maps) in the schema. -16. _Nested Complex Types_: nested complex types are complex types which - contain other complex types (Example: `array>`). - Note that it can read all the schemas for DataSource V1. The Data Source V2 truncates the schema, - so if you see "`...`", then the full schema is not available. - For such schemas we read until `...` and report if there are any complex types and nested complex types in that. -17. _Potential Problems_: some UDFs and nested complex types. Please keep in mind that the tool is only able to detect certain issues. -18. _Longest SQL Duration_: the maximum amount of time spent in a single task of SQL Dataframe operations. -19. _NONSQL Task Duration Plus Overhead_: Time duration that does not span any running SQL task. -20. _Unsupported Task Duration_: sum of task durations for any unsupported operators. -21. _Supported SQL DF Task Duration_: sum of task durations that are supported by RAPIDS GPU acceleration. -22. _Task Speedup Factor_: the average speed-up of all stages. -23. _App Duration Estimated_: True or False indicates if we had to estimate the application duration. - If we had to estimate it, the value will be `True` and it means the event log was missing the application finished - event, so we will use the last job or sql execution time we find as the end time used to calculate the duration. -24. _Unsupported Execs_: reports all the execs that are not supported by GPU in this application. Note that an Exec name may be - printed in this column if any of the expressions within this Exec is not supported by GPU. If the resultant string - exceeds maximum limit (25), then ... is suffixed to the STDOUT and full output can be found in the CSV file. -25. _Unsupported Expressions_: reports all expressions not supported by GPU in this application. -26. _Read Schema_: shows the datatypes and read formats. This field is only listed when the argument `--report-read-schema` - is passed to the CLI. -27. _Estimated Frequency_: application executions per month assuming uniform distribution, default frequency is daily (30 times per month) - and minimum frequency is monthly (1 time per month). For a given log set, determines a logging window using the earliest start time - and last end time of all logged applications. Counts the number of executions of a specific `App Name` over the logging window - and converts the frequency to per month (30 days). Applications that are only ran once are assigned the default frequency. - -**Note:** the Qualification tool won't catch all UDFs, and some of the UDFs can be handled with additional steps. -Please refer to [Supported Operators](https://github.com/NVIDIA/spark-rapids/blob/main/docs/supported_ops.md) for more details on UDF. - -By default, the applications and queries are sorted in descending order by the following fields: -- _Recommendation_; -- _Estimated GPU Speed-up_; -- _Estimated GPU Time Saved_; and -- _End Time_. - -### Stages report - -For each stage used in SQL operations, the Qualification tool generates the following information: - -1. _App ID_ -2. _Stage ID_ -3. _Average Speedup Factor_: the average estimated speed-up of all the operators in the given stage. -4. _Stage Task Duration_: amount of time spent in tasks of SQL Dataframe operations for the given stage. -5. _Unsupported Task Duration_: sum of task durations for the unsupported operators. For more details, - see [Supported Operators](https://github.com/NVIDIA/spark-rapids/blob/main/docs/supported_ops.md). -6. _Stage Estimated_: True or False indicates if we had to estimate the stage duration. - -### Execs report - -The Qualification tool generates a report of the "Exec" in the "_SparkPlan_" or "_Executor Nodes_" along with the estimated -acceleration on the GPU. Please refer to the [Supported Operators](https://github.com/NVIDIA/spark-rapids/blob/main/docs/supported_ops.md) guide for more -details on limitations on UDFs and unsupported operators. - -1. _App ID_ -2. _SQL ID_ -3. _Exec Name_: example `Filter`, `HashAggregate` -4. _Expression Name_ -5. _Task Speedup Factor_: it is simply the average acceleration of the operators - based on the original CPU duration of the operator divided by the GPU duration. The tool uses historical queries and benchmarks to estimate a speed-up at - an individual operator level to calculate how much a specific operator would accelerate on GPU. -6. _Exec Duration_: wall-Clock time measured since the operator starts till it is completed. -7. _SQL Node Id_ -8. _Exec Is Supported_: whether the Exec is supported by RAPIDS or not. Please refer to the - [Supported Operators](https://github.com/NVIDIA/spark-rapids/blob/main/docs/supported_ops.md) section. -9. _Exec Stages_: an array of stage IDs -10. _Exec Children_ -11. _Exec Children Node Ids_ -12. _Exec Should Remove_: whether the Op is removed from the migrated plan. - -**Parsing Expressions within each Exec** - -The Qualification tool looks at the expressions in each _Exec_ to provide a fine-grained assessment of -RAPIDS' support. -Note that it is not possible to extract the expressions for each available _Exec_: -- some Execs do not take any expressions, and -- some execs may not show the expressions in the _eventlog_. - -The following table lists the exec's name and the status of parsing their expressions where: -- "_Expressions Unavailable_" marks the _Execs_ that do not show expressions in the _eventlog_; -- "_Fully Parsed_" marks the _Execs_ that have their expressions fully parsed by the Qualification tool; -- "_In Progress_" marks the _Execs_ that are still being investigated; therefore, a set of the - marked _Execs_ may be fully parsed in future releases. - -| **Exec** | **Expressions Unavailable** | **Fully Parsed** | **In Progress** | -|---------------------------------------|:---------------------------:|:----------------:|:---------------:| -| AggregateInPandasExec | - | - | x | -| AQEShuffleReadExec | - | - | x | -| ArrowEvalPythonExec | - | - | x | -| BatchScanExec | - | - | x | -| BroadcastExchangeExec | - | - | x | -| BroadcastHashJoinExec | - | - | x | -| BroadcastNestedLoopJoinExec | - | - | x | -| CartesianProductExec | - | - | x | -| CoalesceExec | - | - | x | -| CollectLimitExec | x | - | - | -| CreateDataSourceTableAsSelectCommand | - | - | x | -| CustomShuffleReaderExec | - | - | x | -| DataWritingCommandExec | - | - | x | -| ExpandExec | - | - | x | -| FileSourceScanExec | - | - | x | -| FilterExec | - | x | - | -| FlatMapGroupsInPandasExec | - | - | x | -| GenerateExec | - | - | x | -| GlobalLimitExec | x | - | - | -| HashAggregateExec | - | x | - | -| InMemoryTableScanExec | - | - | x | -| InsertIntoHadoopFsRelationCommand | - | - | x | -| LocalLimitExec | x | - | - | -| MapInPandasExec | - | - | x | -| ObjectHashAggregateExec | - | x | - | -| ProjectExec | - | x | - | -| RangeExec | x | - | - | -| SampleExec | - | - | x | -| ShuffledHashJoinExec | - | - | x | -| ShuffleExchangeExec | - | - | x | -| SortAggregateExec | - | x | - | -| SortExec | - | x | - | -| SortMergeJoinExec | - | - | x | -| SubqueryBroadcastExec | - | - | x | -| TakeOrderedAndProjectExec | - | - | x | -| UnionExec | x | - | - | -| WindowExec | - | x | - | -| WindowInPandasExec | - | - | x | - -### MLFunctions report -The Qualification tool generates a report if there are SparkML or Spark XGBoost functions used in the eventlog. -The functions in "*spark.ml.*" or "*spark.XGBoost.*" packages are displayed in the report. - -1. _App ID_ -2. _Stage ID_ -3. _ML Functions_: List of ML functions used in the corresponding stage. -4. _Stage Task Duration_: amount of time spent in tasks containing ML functions for the given stage. - -### MLFunctions total duration report -The Qualification tool generates a report of total duration across all stages for ML functions which -are supported on GPU. - -1. _App ID_ -2. _Stage_Ids : Stage Id's corresponding to the given ML function. -3. _ML Function Name_: ML function name supported on GPU. -4. _Total Duration_: total duration across all stages for the corresponding ML function. - -## Output Formats - -The Qualification tool generates the output as CSV/log files. Starting from "_22.06_", the default -is to generate the report into two different formats: CSV/log files; and HTML. - -### HTML Report - -Starting with release _"22.06"_, the HTML report is generated by default under the output directory -`${OUTPUT_FOLDER}/rapids_4_spark_qualification_output/ui`. -The HTML report is disabled by passing `--no-html-report` as described in the -[Qualification tool options](#Qualification-tool-options) section above. -To browse the content of the html report: - -1. For HDFS or remote node, copy the directory of `${OUTPUT_FOLDER}/rapids_4_spark_qualification_output/ui` to your local node. -2. Open `rapids_4_spark_qualification_output/ui/index.html` in your local machine's web-browser (Chrome/Firefox are recommended). - -The HTML view renders the detailed information into tables that allow following features: - -- searching -- ordering by specific column -- exporting table into CSV file -- interactive filter by recommendations and/or user-name. - -By default, all tables show 20 entries per page, which can be changed by selecting a different page-size in the table's navigation bar. - -The following sections describe the HTML views. - -#### Application Recommendations Summary - -`index.html` shows the summary of the estimated GPU performance. The "_GPU Recommendations Table_" -lists the processed applications ranked by the "_Estimated GPU Speed-up_" along with the ability to search, and filter -the results. By clicking the "_App ID_" link of a specific app, you navigate to the details view of that app which is -described in [App-Details View](#app-details-view) section. - -The summary report contains the following components: - -1. **Stats-Row**: statistics card summarizing the following information: - 1. "_Total Applications_": total number of applications analyzed by the Qualification tool and the total execution - time. - 2. "_RAPIDS Candidates_": marks the number applications that are either "_Recommended_", or "_Strongly Recommended_". - 3. "_GPU Opportunity_": shows the total of "_GPU Opportunity_" and "_SQL DF duration_" fields across all the apps. -2. **GPU Recommendations Table**: this table lists all the analyzed applications along with subset of fields that are - directly involved in calculating the GPU performance estimate. Each row expands showing more fields by clicking on - the control column. -3. The _searchPanes_ with the capability to search the app list by selecting rows in the panes. - The "_Recommendations_" and "_Spark User_" filters are cascaded which allows the panes to be filtered based on the - values selected in the other pane. -4. Text Search field that allows further filtering, removing data from the result set as keywords are entered. The - search box will match on multiple columns including: "_App ID_", "_App Name_", "_Recommendation_" -5. HTML5 export button saves the table to CSV file into the browser's default download folder. -6. The `Raw Data` link in the left navigation bar redirects to a detailed report. -7. The `Per-SQL Data` link in the left navigation bar redirects to a summary report that shows - the _per-SQL_ estimated GPU performance. - -![Qualification-HTML-Recommendation-View](img/Tools/qualification-tool-recommendation-indexview-with-persql.png) - -#### App-Details View - -When you click the "_App ID_" of a specific row in the "_GPU Recommendations Table_", the browser navigates to -this view which shows the metrics and estimated GPU performance for the given application. -It contains the following main components: - -1. **Card title**: contains the application name and the Recommendation. -2. **Stats-Row**: statistics card summarizing the following information: - 1. "_App Duration_": the total execution time of the app, marking the start and end time. - 2. "_GPU Opportunity_": the wall-Clock time that shows how much of the SQL duration can be accelerated on the GPU. It - shows the actual wall-Clock time duration that includes only SQL-Dataframe queries including non-supported ops, - dubbed "_SQL DF Duration_". This is followed by "_Task Speed-up Factor_" which represents the average speed-up - of all app stages. - 3. "_Estimated GPU Duration_": the predicted runtime of the app if it was run on GPU. For convenience, it calculates - the estimated wall-clock time difference between the CPU and GPU executions. The original CPU duration of the app - divided by the estimated GPU duration and displayed as "_App Speed-up_". -3. **Application Details**: this table lists all the fields described previously in - the [Detailed App report](#detailed-app-report) section. Note that this table has more columns than can fit in a - normal browser window. Therefore, the UI - application dynamically optimizes the layout of the table to fit the browser screen. By clicking on the control - column, the row expands to show the remaining hidden columns. - ![Qualification-HTML-App-Details-View-Header](img/Tools/qualification-tool-app-view-01.png) -4. **Stage Details Table**: lists all the app stages with set of columns listed in [Stages report](#stages-report) - section. The HTML5 export button saves the table to CSV file into the browser's default download folder. - ![Qualification-HTML-App-Details-View-Stages](img/Tools/qualification-tool-app-view-02.png) - The table has cascaded _searchPanes_, which means that the table allows the panes - to be filtered based on the values selected in the other panes. - There are three searchPanes: - 1. "_Is Stage Estimated_": it splits the stages into two groups based on whether the stage duration time was estimated - or not. - 2. "_Speed-up_": groups the stages by their "average speed-up". Each stage can belong to one of the following - predefined speed-up ranges: `1.0 (No Speed-up)`; `]1.0, 1.3[`; `[1.3, 2.5[`; `[2.5, 5[`; and `[5, _]`. The - search-pane does not show a range bucket if its count is 0. - 3. "_Tasks GPU Support_": this filter can be used to find stages having all their execs supported by the GPU. -5. **Execs Details Table**: lists all the app Execs with set of columns listed in [Execs report](#execs-report) - section. The HTML5 export button saves the table to CSV file into the browser's default - download folder. - ![Qualification-HTML-App-Details-View-Execs](img/Tools/qualification-tool-app-view-03.png) - The table has cascaded _searchPanes_, which means that the table allows the panes - to be filtered based on the values selected in the other panes. - There are three _searchPanes_: - 1. "_Exec_": filters the rows by exec name. This filter also allows text searching by typing into the filter-title as - a text input. - 2. "_Speed-up_": groups the stages by their "average speed-up". Each stage can belong to one of the following - predefined speed-up ranges: `1.0 (No Speed-up)`; `]1.0, 1.3[`; `[1.3, 2.5[`; `[2.5, 5[`; and `[5, _]`. The - search-pane does not show a range bucket if its count is 0. - 3. "_GPU Support_": filters the execs whether an exec is supported by GPU or not. - 4. "_Stage ID_": filters rows by the stage ID. It also allows text-searching by typing into the filter-title as a text - input. - 5. "_Is Exec Removed_": filters rows that were removed from the migrated plan. - 6. **SQL Details Table**: lists _Per-SQL_ GPU recommendation. The HTML5 export button saves the table to CSV file into - the browser's default download folder. The rows in the table can be filtered by "_SQL Description_", "_SQL ID_", - or "_Recommendation_". - -#### Raw Data - -`raw.html` displays all the fields listed in "_Detailed App Report_" in more readable format. -Columns representing "_time duration_" are rounded to nearest "ms", "seconds", "minutes", and "hours". -The search box will match on multiple columns including: "_App ID_", "_App Name_", "_Recommendation_", -"_User Name_", "_Unsupported Write Data Format_", "_Complex Types_", "_Nested Complex Types_", and "_Read Schema_". -The detailed table can also be exported as a CSV file into the browser's default download folder. - -Note that this table has more columns than can fit in a normal browser window. Therefore, the UI application dynamically -optimizes the layout of the table -to fit the browser screen. By clicking on the control column, the row expands to show the remaining hidden columns. - -#### Per-SQL Data - -`sql-recommendation.html` displays a summary of the estimate GPU performance for each query. Note that the -SQL queries across all the apps are combined in a single view; therefore, the "_SQL ID_" field may not be -unique. - -### Text and CSV files - -The Qualification tool generates a set of log/CSV files in the output folder -`${OUTPUT_FOLDER}/rapids_4_spark_qualification_output`. The content of each -file is summarized in the following two sections. - -Note: Starting with release "_23.06_", CSV output uses escapes and quotes to avoid parsing errors. -Spark's default CSV escape character is the backslash(`\`), but standard spreadsheets use the double quote(`"`). -The Qualification tool output uses the double quote(`"`) escape character to avoid breaking -spreadsheet software functionality. As a result, the data source must be configured using -`option("ESCAPE", "\"")` when reading the Qualification tool CSV files into Spark. - -#### Application Report Summary - -The Qualification tool generates a brief summary that includes the projected application's performance -if the application is run on the GPU. Beside sending the summary to `STDOUT`, the Qualification tool -generates _text_ as `rapids_4_spark_qualification_output.log` - -The summary report outputs the following information: "_App Name_", "_App ID_", "_App Duration_", "_SQL DF duration_", -"_GPU Opportunity_", "_Estimated GPU Duration_", "_Estimated GPU Speed-up_", "_Estimated GPU Time Saved_", and -"_Recommendation_". - -Note: the duration(s) reported are in milliseconds. -Sample output in text: - -``` -+------------+--------------+----------+----------+-------------+-----------+-----------+-----------+--------------------+-------------------------------------------------------+ -| App Name | App ID | App | SQL DF | GPU | Estimated | Estimated | Estimated | Recommendation | Unsupported Execs |Unsupported Expressions| -| | | Duration | Duration | Opportunity | GPU | GPU | GPU | | | | -| | | | | | Duration | Speedup | Time | | | | -| | | | | | | | Saved | | | | -+============+==============+==========+==========+=============+===========+===========+===========+====================+=======================================================+ -| appName-01 | app-ID-01-01 | 898429| 879422| 879422| 273911.92| 3.27| 624517.06|Strongly Recommended| | | -+------------+--------------+----------+----------+-------------+-----------+-----------+-----------+--------------------+-------------------------------------------------------+ -| appName-02 | app-ID-02-01 | 9684| 1353| 1353| 8890.09| 1.08| 793.9| Not Recommended|Filter;SerializeFromObject;S...| hex | -+------------+--------------+----------+----------+-------------+-----------+-----------+-----------+--------------------+-------------------------------------------------------+ -``` - -In the above example, two application event logs were analyzed. “app-ID-01-01” is "_Strongly Recommended_" -because `Estimated GPU Speedup` is ~3.27. On the other hand, the estimated acceleration running -“app-ID-02-01” on the GPU is not high enough; hence the app is not recommended. - -#### Per SQL Query Report Summary - -The Qualification tool has an option to generate a report at the per SQL query level. It generates a brief summary -that includes the projected queries performance if the query is run on the GPU. Beside sending the summary to `STDOUT`, -the Qualification tool generates _text_ as `rapids_4_spark_qualification_output_persql.log` - -The summary report outputs the following information: "_App Name_", "_App ID_", "_SQL ID_", "_SQL Description_", "_SQL DF duration_", -"_GPU Opportunity_", "_Estimated GPU Duration_", "_Estimated GPU Speed-up_", "_Estimated GPU Time Saved_", and -"_Recommendation_". - -Note: the duration(s) reported are in milliseconds. -Sample output in text: - -``` -+------------+--------------+----------+---------------+----------+-------------+-----------+-----------+-----------+--------------------+ -| App Name | App ID | SQL ID | SQL | SQL DF | GPU | Estimated | Estimated | Estimated | Recommendation | -| | | | Description | Duration | Opportunity | GPU | GPU | GPU | | -| | | | | | | Duration | Speedup | Time | | -| | | | | | | | | Saved | | -+============+==============+==========+===============+==========+=============+===========+===========+===========+====================+ -| appName-01 | app-ID-01-01 | 1| query41| 571| 571| 187.21| 3.05| 383.78|Strongly Recommended| -+------------+--------------+----------+---------------+----------+-------------+-----------+-----------+-----------+--------------------+ -| appName-02 | app-ID-02-01 | 3| query44| 1116| 0| 1115.98| 1.0| 0.01| Not Recommended| -+------------+--------------+----------+---------------+----------+-------------+-----------+-----------+-----------+--------------------+ -``` - -#### Detailed App Report - -**1. Entire App report** - -The first part of the detailed report is saved as `rapids_4_spark_qualification_output.csv`. -The apps are processed and ranked by the `Estimated GPU Speed-up`. -In addition to the fields listed in the "_Report Summary_", it shows all the app fields. -The duration(s) are reported are in milliseconds. - -**2. Per SQL report** - -The second file is saved as `rapids_4_spark_qualification_output_persql.csv`. This contains the -per SQL query report in CSV format. - -Sample output in text: -``` -+---------------+-----------------------+------+----------------------------------------------------------+---------------+---------------+----------------------+---------------------+------------------------+--------------------+ -| App Name| App ID|SQL ID| SQL Description|SQL DF Duration|GPU Opportunity|Estimated GPU Duration|Estimated GPU Speedup|Estimated GPU Time Saved| Recommendation| -+===============+=======================+======+==========================================================+===============+===============+======================+=====================+========================+====================+ -|NDS - Power Run|app-20220702220255-0008| 103| query87| 15871| 15871| 4496.03| 3.53| 11374.96|Strongly Recommended| -|NDS - Power Run|app-20220702220255-0008| 106| query38| 11077| 11077| 3137.96| 3.53| 7939.03|Strongly Recommended| -+---------------+-----------------------+------+----------------------------------------------------------+---------------+---------------+----------------------+---------------------+------------------------+--------------------+ -``` - - -**3. Stages report** - -The third file is saved as `rapids_4_spark_qualification_output_stages.csv`. - -Sample output in text: -``` -+--------------+----------+-----------------+------------+---------------+-----------+ -| App ID | Stage ID | Average Speedup | Stage Task | Unsupported | Stage | -| | | Factor | Duration | Task Duration | Estimated | -+==============+==========+=================+============+===============+===========+ -| app-ID-01-01 | 25 | 2.1 | 23 | 0 | false | -+--------------+----------+-----------------+------------+---------------+-----------+ -| app-ID-02-01 | 29 | 1.86 | 0 | 0 | true | -+--------------+----------+-----------------+------------+---------------+-----------+ -``` - -**4. Execs report** - -The last file is saved `rapids_4_spark_qualification_output_execs.csv`. Similar to the app and stage information, -the table shows estimated GPU performance of the SQL Dataframe operations. - -Sample output in text: -``` -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -| App ID | SQL ID | Exec Name | Expression Name | Task Speedup | Exec | SQL Node | Exec Is | Exec | Exec Children | Exec Children | Exec Should | -| | | | | Factor | Duration | Id | Supported | Stages | | Node Ids | Remove | -+==============+========+===========================+=======================+==============+==========+==========+===========+========+============================+===============+=============+ -| app-ID-02-01 | 7 | Execute CreateViewCommand | | 1.0 | 0 | 0 | false | | | | false | -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -| app-ID-02-01 | 24 | Project | | 2.0 | 0 | 21 | true | | | | false | -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -| app-ID-02-01 | 24 | Scan parquet | | 2.0 | 260 | 36 | true | 24 | | | false | -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -| app-ID-02-01 | 15 | Execute CreateViewCommand | | 1.0 | 0 | 0 | false | | | | false | -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -| app-ID-02-01 | 24 | Project | | 2.0 | 0 | 14 | true | | | | false | -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -| app-ID-02-01 | 24 | WholeStageCodegen (6) | WholeStageCodegen (6) | 2.8 | 272 | 2 | true | 30 | Project:BroadcastHashJoin: | 3:4:5 | false | -| | | | | | | | | | HashAggregate | | | -+--------------+--------+---------------------------+-----------------------+--------------+----------+----------+-----------+--------+----------------------------+---------------+-------------+ -``` - -## How to compile the tools jar - -See instructions here: https://github.com/NVIDIA/spark-rapids-tools/tree/main/core#build - -If any input is a S3 file path or directory path, 2 extra steps are needed to access S3 in Spark: -1. Download the matched jars based on the Hadoop version: - - `hadoop-aws-.jar` - - `aws-java-sdk-.jar` - -2. Take Hadoop 2.7.4 for example, we can download and include below jars in the '--jars' option to spark-shell or spark-submit: - [hadoop-aws-2.7.4.jar](https://repo.maven.apache.org/maven2/org/apache/hadoop/hadoop-aws/2.7.4/hadoop-aws-2.7.4.jar) and - [aws-java-sdk-1.7.4.jar](https://repo.maven.apache.org/maven2/com/amazonaws/aws-java-sdk/1.7.4/aws-java-sdk-1.7.4.jar) - -3. In $SPARK_HOME/conf, create `hdfs-site.xml` with below AWS S3 keys inside: - -```xml - - - - fs.s3a.access.key - xxx - - - fs.s3a.secret.key - xxx - - -``` - -Please refer to this [doc](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html) on -more options about integrating hadoop-aws module with S3. 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 7d428a14b..9b78a5b41 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 @@ -899,7 +899,8 @@ class AutoTuner( "Spark applications utilizing RAPIDS Accelerator for Apache Spark") if (!isPluginLoaded) { appendComment("RAPIDS Accelerator for Apache Spark jar is missing in \"spark.plugins\". " + - "Please refer to https://nvidia.github.io/spark-rapids/Getting-Started") + "Please refer to " + + "https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/overview.html") } if (!rapidsEnabled) { appendComment("Please enable Spark RAPIDS Accelerator for Apache Spark by setting " + diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala index 0a9172ca8..36831386b 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/AutoTunerSuite.scala @@ -797,7 +797,7 @@ class AutoTunerSuite extends FunSuite with BeforeAndAfterEach with Logging { | |Comments: |- AutoTuner recommendations only support eventlogs generated by Spark applications utilizing RAPIDS Accelerator for Apache Spark - |- RAPIDS Accelerator for Apache Spark jar is missing in "spark.plugins". Please refer to https://nvidia.github.io/spark-rapids/Getting-Started + |- RAPIDS Accelerator for Apache Spark jar is missing in "spark.plugins". Please refer to https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/overview.html |""".stripMargin // scalastyle:on line.size.limit assert(expectedResults == autoTunerOutput) @@ -835,7 +835,7 @@ class AutoTunerSuite extends FunSuite with BeforeAndAfterEach with Logging { | |Comments: |- AutoTuner recommendations only support eventlogs generated by Spark applications utilizing RAPIDS Accelerator for Apache Spark - |- RAPIDS Accelerator for Apache Spark jar is missing in "spark.plugins". Please refer to https://nvidia.github.io/spark-rapids/Getting-Started + |- RAPIDS Accelerator for Apache Spark jar is missing in "spark.plugins". Please refer to https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/overview.html |""".stripMargin // scalastyle:on line.size.limit assert(expectedResults == autoTunerOutput) diff --git a/user_tools/docs/index.md b/user_tools/docs/index.md index e47e83078..32d9e096e 100644 --- a/user_tools/docs/index.md +++ b/user_tools/docs/index.md @@ -13,7 +13,7 @@ to build analysis reports on Amazon EMR, GCloud Dataproc, and Databricks. ### Qualification -Provides a wrapper to simplify the execution of [RAPIDS Qualification tool](../../core/docs/spark-qualification-tool.md) +Provides a wrapper to simplify the execution of [RAPIDS Qualification tool](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html). The latter analyzes Spark events generated from CPU based Spark applications to help quantify the expected acceleration and costs savings of migrating a Spark application or query to GPU. @@ -21,7 +21,7 @@ query to GPU. The tool will process each app individually, but will group apps with the same name into the same output row after averaging duration metrics accordingly. For more details, please visit the -[Qualification Tool on GitHub pages](https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html). +[Qualification Tool guide](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html). ### Bootstrap @@ -35,7 +35,7 @@ Apache Spark default configurations. ### Profiling -Provides a wrapper to simplify the execution of [RAPIDS Profiling tool](../../core/docs/spark-profiling-tool.md). +Provides a wrapper to simplify the execution of [RAPIDS Profiling tool](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html). The latter analyzes both CPU or GPU generated event logs and generates information which can be used for debugging and profiling Apache Spark applications. The tool also will recommend setting for the application assuming that the job will be able to use all the cluster resources (CPU and GPU) when @@ -45,7 +45,7 @@ In addition, the wrapper output provides optimized RAPIDS configurations based o information. For more details, please visit the -[Profiling Tool on GitHub pages](https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html). +[Profiling Tool guide](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html). ### Diagnostic diff --git a/user_tools/docs/user-tools-aws-emr.md b/user_tools/docs/user-tools-aws-emr.md index 5c786ad3a..5928474ed 100644 --- a/user_tools/docs/user-tools-aws-emr.md +++ b/user_tools/docs/user-tools-aws-emr.md @@ -82,7 +82,7 @@ The local deployment runs on the local development machine. It requires: | **gpu_discount** | A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **global_discount** | A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Qualification tool options](../../core/docs/spark-qualification-tool.md#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | +| **rapids_options**** | A list of valid [Qualification tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | #### Use case scenario @@ -183,7 +183,7 @@ The local deployment runs on the local development machine. It requires: | Option | Description | Default | Required | |----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------| -| **gpu_cluster** | The EMR-cluster on which the Spark applications were executed. The argument can be an EMR-cluster or a valid path to the cluster's properties file (json format) generated by the AWS CLI command `aws emr describe-cluster` | If missing, then the argument worker_info has to be provided. | N | +| **gpu_cluster** | The EMR-cluster on which the Spark applications were executed. The argument can be an EMR-cluster or a valid path to the cluster's properties file (json format) generated by the AWS CLI command `aws emr describe-cluster` | If missing, then the argument worker_info may be provided. | N | | **worker_info** | A path pointing to a yaml file containing the system information of a worker node. It is assumed that all workers are homogenous. The format of the file is described in the following section. | None | N | | **eventlogs** | A comma seperated list of S3 urls pointing to event logs or S3 directory | Reads the Spark's property `spark.eventLog.dir` defined in `gpu_cluster`. This property should be included in the output of `emr describe-cluster`. Note that the wrapper will raise an exception if the property is not set. | N | | **remote_folder** | The S3 folder where the output of the wrapper's output is copied. If missing, the output will be available only on local disk | N/A | N | @@ -192,9 +192,9 @@ The local deployment runs on the local development machine. It requires: | **jvm_heap_size** | The maximum heap size of the JVM in gigabytes | 24 | N | | **tools_jar** | Path to a bundled jar including RAPIDS tool. The path is a local filesystem, or remote S3 url | Downloads the latest `rapids-4-spark-tools_*.jar` from mvn repo | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Profiling tool options](../../core/docs/spark-profiling-tool.md#qualification-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | +| **rapids_options**** | A list of valid [Profiling tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | -If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file must be +If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file can be provided through the arg `worker_info`. The `worker_info` is a yaml file that contains the HW description of the workers. It must contain the following properties: @@ -306,6 +306,9 @@ The CLI is triggered by providing the location where the yaml file is stored `-- --remote_folder $REMOTE_FOLDER ``` +Note that if the user does not supply a cluster or worker properties file, the autotuner will still recommend +tuning settings based on the job event log. + ## Bootstrap command ``` diff --git a/user_tools/docs/user-tools-databricks-aws.md b/user_tools/docs/user-tools-databricks-aws.md index 766080460..8e94e654d 100644 --- a/user_tools/docs/user-tools-databricks-aws.md +++ b/user_tools/docs/user-tools-databricks-aws.md @@ -81,7 +81,7 @@ The local deployment runs on the local development machine. It requires: | **gpu_discount** | A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **global_discount** | A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Qualification tool options](../../core/docs/spark-qualification-tool.md#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | +| **rapids_options**** | A list of valid [Qualification tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | #### Use case scenario @@ -182,7 +182,7 @@ The local deployment runs on the local development machine. It requires: | Option | Description | Default | Required | |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------| -| **gpu_cluster** | The Databricks-cluster on which the Apache Spark applications were executed. Accepted values are an Databricks-cluster id, or a valid path to the cluster properties file (json format) generated by Databricks CLI command `databricks clusters get CLUSTER_ID [flags]` | If missing, then the argument `worker_info` has to be provided. | N | +| **gpu_cluster** | The Databricks-cluster on which the Apache Spark applications were executed. Accepted values are an Databricks-cluster id, or a valid path to the cluster properties file (json format) generated by Databricks CLI command `databricks clusters get CLUSTER_ID [flags]` | If missing, then the argument `worker_info` may be provided. | N | | **worker_info** | A path pointing to a yaml file containing the system information of a worker node. It is assumed that all workers are homogenous. The format of the file is described in the following section. | None | N | | **eventlogs** | A comma seperated list of S3 urls pointing to event logs or S3 directory | Reads the Spark's property `spark.eventLog.dir` defined in `gpu_cluster`. This property should be included in the output of `databricks clusters get CLUSTER_ID [flags]`. Note that the wrapper will raise an exception if the property is not set. | N | | **remote_folder** | The S3 folder where the output of the wrapper's output is copied. If missing, the output will be available only on local disk | N/A | N | @@ -193,9 +193,9 @@ The local deployment runs on the local development machine. It requires: | **tools_jar** | Path to a bundled jar including RAPIDS tool. The path is a local filesystem, or remote S3 url | Downloads the latest `rapids-4-spark-tools_*.jar` from mvn repo | N | | **credentials_file** | The local path of JSON file that contains the application credentials | If missing, loads the env variable `DATABRICKS_CONFIG_FILE` if any. Otherwise, it uses the default path `~/.databrickscfg` on Unix, Linux, or macOS | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Profiling tool options](../../core/docs/spark-profiling-tool.md#qualification-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | +| **rapids_options**** | A list of valid [Profiling tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | -If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file must be +If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file may be provided through the arg `worker_info`. The `worker_info` is a yaml file that contains the HW description of the workers. It must contain the following properties: @@ -310,6 +310,9 @@ The CLI is triggered by providing the location where the yaml file is stored `-- --remote_folder $REMOTE_FOLDER ``` +Note that if the user does not supply a cluster or worker properties file, the autotuner will still recommend +tuning settings based on the job event log. + ## Diagnostic command ``` diff --git a/user_tools/docs/user-tools-databricks-azure.md b/user_tools/docs/user-tools-databricks-azure.md index 435a861b2..2605b70e8 100644 --- a/user_tools/docs/user-tools-databricks-azure.md +++ b/user_tools/docs/user-tools-databricks-azure.md @@ -83,7 +83,7 @@ The local deployment runs on the local development machine. It requires: | **gpu_discount** | A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **global_discount** | A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Qualification tool options](../../core/docs/spark-qualification-tool.md#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | +| **rapids_options**** | A list of valid [Qualification tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | #### Use case scenario @@ -184,7 +184,7 @@ The local deployment runs on the local development machine. It requires: | Option | Description | Default | Required | |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------| -| **gpu_cluster** | The Databricks-cluster on which the Apache Spark applications were executed. Accepted values are an Databricks-cluster id, or a valid path to the cluster properties file (json format) generated by Databricks CLI command `databricks clusters get CLUSTER_ID [flags]` | If missing, then the argument `worker_info` has to be provided. | N | +| **gpu_cluster** | The Databricks-cluster on which the Apache Spark applications were executed. Accepted values are an Databricks-cluster id, or a valid path to the cluster properties file (json format) generated by Databricks CLI command `databricks clusters get CLUSTER_ID [flags]` | If missing, then the argument `worker_info` may be provided. | N | | **worker_info** | A path pointing to a yaml file containing the system information of a worker node. It is assumed that all workers are homogenous. The format of the file is described in the following section. | None | N | | **eventlogs** | A comma seperated list of ABFS urls pointing to event logs or ABFS directory | Reads the Spark's property `spark.eventLog.dir` defined in `gpu_cluster`. This property should be included in the output of `databricks clusters get CLUSTER_ID [flags]`. Note that the wrapper will raise an exception if the property is not set. | N | | **remote_folder** | The ABFS folder where the output of the wrapper's output is copied. If missing, the output will be available only on local disk | N/A | N | @@ -194,9 +194,9 @@ The local deployment runs on the local development machine. It requires: | **tools_jar** | Path to a bundled jar including RAPIDS tool. The path is a local filesystem, or remote ABFS url | Downloads the latest `rapids-4-spark-tools_*.jar` from mvn repo | N | | **credentials_file** | The local path of JSON file that contains the application credentials | If missing, loads the env variable `DATABRICKS_CONFIG_FILE` if any. Otherwise, it uses the default path `~/.databrickscfg` on Unix, Linux, or macOS | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Profiling tool options](../../core/docs/spark-profiling-tool.md#qualification-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | +| **rapids_options**** | A list of valid [Profiling tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | -If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file must be +If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file may be provided through the arg `worker_info`. The `worker_info` is a yaml file that contains the HW description of the workers. It must contain the following properties: @@ -311,6 +311,8 @@ The CLI is triggered by providing the location where the yaml file is stored `-- --remote_folder $REMOTE_FOLDER ``` +Note that if the user does not supply a cluster or worker properties file, the autotuner will still recommend +tuning settings based on the job event log. ## Diagnostic command @@ -351,4 +353,4 @@ The steps to run the command: --cluster my-cluster-id ``` -If the connection to Databricks instances cannot be established through SSH, the command will raise error. \ No newline at end of file +If the connection to Databricks instances cannot be established through SSH, the command will raise error. diff --git a/user_tools/docs/user-tools-dataproc-gke.md b/user_tools/docs/user-tools-dataproc-gke.md index 58d5105d3..9b8549e9c 100644 --- a/user_tools/docs/user-tools-dataproc-gke.md +++ b/user_tools/docs/user-tools-dataproc-gke.md @@ -82,7 +82,7 @@ The local deployment runs on the local development machine. It requires: | **gpu_discount** | A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **global_discount** | A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Qualification tool options](../../core/docs/spark-qualification-tool.md#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | +| **rapids_options**** | A list of valid [Qualification tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | #### Use case scenario diff --git a/user_tools/docs/user-tools-dataproc.md b/user_tools/docs/user-tools-dataproc.md index ef6806be5..3e50e048a 100644 --- a/user_tools/docs/user-tools-dataproc.md +++ b/user_tools/docs/user-tools-dataproc.md @@ -83,7 +83,7 @@ The local deployment runs on the local development machine. It requires: | **gpu_discount** | A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **global_discount** | A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Qualification tool options](../../core/docs/spark-qualification-tool.md#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | +| **rapids_options**** | A list of valid [Qualification tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | #### Use case scenario @@ -199,7 +199,7 @@ The local deployment runs on the local development machine. It requires: | Option | Description | Default | Required | |----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------| -| **gpu_cluster** | The Dataproc-cluster on which the Spark applications were executed. The argument can be a Dataproc-cluster or a valid path to the cluster's properties file (json format) generated by the gcloud-CLI | If missing, then the argument `worker_info` has to be provided. | N | +| **gpu_cluster** | The Dataproc-cluster on which the Spark applications were executed. The argument can be a Dataproc-cluster or a valid path to the cluster's properties file (json format) generated by the gcloud-CLI | If missing, then the argument `worker_info` may be provided. | N | | **worker_info** | A path pointing to a yaml file containing the system information of a worker node. It is assumed that all workers are homogenous. The format of the file is described in the following section. | None | N | | **eventlogs** | A comma seperated list of gs urls pointing to event logs or gs directory | Reads the Spark's property `spark.eventLog.dir` defined in `gpu_cluster`. This property should be included in the output of `dataproc clusters describe`. Note that the wrapper will raise an exception if the property is not set. | N | | **remote_folder** | The gs folder where the output of the wrapper's output is copied. If missing, the output will be available only on local disk | N/A | N | @@ -208,9 +208,9 @@ The local deployment runs on the local development machine. It requires: | **tools_jar** | Path to a bundled jar including RAPIDS tool. The path is a local filesystem, or remote gs url | Downloads the latest `rapids-4-spark-tools_*.jar` from mvn repo | N | | **credentials_file** | The local path of JSON file that contains the application credentials | If missing, loads the env variable `GOOGLE_APPLICATION_CREDENTIALS` if any. Otherwise, it uses the default path `_$HOME/.config/gcloud/application_default_credentials.json_` | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Profiling tool options](../../core/docs/spark-profiling-tool.md#qualification-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | +| **rapids_options**** | A list of valid [Profiling tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | -If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file must be +If the CLI does not provide an argument `gpu_cluster`, then a valid path to yaml file may be provided through the arg `worker_info`. The `worker_info` is a yaml file that contains the HW description of the workers. It must contain the following properties: @@ -323,6 +323,9 @@ The CLI is triggered by providing the location where the yaml file is stored `-- --remote_folder $REMOTE_FOLDER ``` +Note that if the user does not supply a cluster or worker properties file, the autotuner will still recommend +tuning settings based on the job event log. + ## Bootstrap command ``` diff --git a/user_tools/docs/user-tools-onprem.md b/user_tools/docs/user-tools-onprem.md index 87a63c1ad..a88d6ad8e 100644 --- a/user_tools/docs/user-tools-onprem.md +++ b/user_tools/docs/user-tools-onprem.md @@ -62,7 +62,7 @@ The local deployment runs on the local development machine. It requires: | **gpu_discount** | A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **global_discount** | A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount) | N/A | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Qualification tool options](../../core/docs/spark-qualification-tool.md#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | +| **rapids_options**** | A list of valid [Qualification tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options). Note that (`output-directory`, `platform`) flags are ignored, and that multiple "spark-property" is not supported. | N/A | N | ### Use case scenario to run qualification tool for on-premises Cluster @@ -241,15 +241,16 @@ The local deployment runs on the local development machine. It requires: | Option | Description | Default | Required | |----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|----------| -| **worker_info** | A path pointing to a yaml file containing the system information of a worker node. It is assumed that all workers are homogenous. The format of the file is described in the following section. | None | Y | +| **worker_info** | A path pointing to a yaml file containing the system information of a worker node. It is assumed that all workers are homogenous. The format of the file is described in the following section. | None | N | | **eventlogs** | A comma separated list to event logs or directory | None | N | | **local_folder** | Local work-directory path to store the output and to be used as root directory for temporary folders/files. The final output will go into a subdirectory named `prof-${EXEC_ID}` where `exec_id` is an auto-generated unique identifier of the execution. | If the argument is NONE, the default value is the env variable `RAPIDS_USER_TOOLS_OUTPUT_DIRECTORY` if any; or the current working directory. | N | | **jvm_heap_size** | The maximum heap size of the JVM in gigabytes | 24 | N | | **tools_jar** | Path to a bundled jar including RAPIDS tool. The path is a local filesystem. | Downloads the latest `rapids-4-spark-tools_*.jar` from mvn repo | N | | **verbose** | True or False to enable verbosity to the wrapper script | False if `RAPIDS_USER_TOOLS_LOG_DEBUG` is not set | N | -| **rapids_options**** | A list of valid [Profiling tool options](../../core/docs/spark-profiling-tool.md#qualification-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | +| **rapids_options**** | A list of valid [Profiling tool options](https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options). Note that (`output-directory`, `auto-tuner`, `combined`) flags are ignored | N/A | N | + +If the CLI does not provide an argument `worker_info`, the tool will generate recommendations only based on the job event log. -If the CLI does not provide an argument `worker_info`, the tool will throw an error and exit. The `worker_info` is a yaml file that contains the HW description of the workers. It must contain the following properties: - `system.numCores`: number of cores of a single worker node 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 3376b1b60..b32c32acc 100644 --- a/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/profiling-conf.yaml @@ -10,7 +10,7 @@ sparkRapids: mvnUrl: 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12' repoUrl: '{}/{}/rapids-4-spark-tools_2.12-{}.jar' mainClass: 'com.nvidia.spark.rapids.tool.profiling.ProfileMain' - outputDocURL: 'https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#understanding-profiling-tool-detailed-output-and-examples' + outputDocURL: 'https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#understanding-profiling-tool-detailed-output-and-examples' cli: toolOptions: - csv 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 ebdc72369..408bf714a 100644 --- a/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml +++ b/user_tools/src/spark_rapids_pytools/resources/qualification-conf.yaml @@ -42,7 +42,7 @@ sparkRapids: mvnUrl: 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12' repoUrl: '{}/{}/rapids-4-spark-tools_2.12-{}.jar' mainClass: 'com.nvidia.spark.rapids.tool.qualification.QualificationMain' - outputDocURL: 'https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#understanding-the-qualification-tool-output' + outputDocURL: 'https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#understanding-the-qualification-tool-output' gpu: device: 't4' workersPerNode: 2 diff --git a/user_tools/src/spark_rapids_pytools/wrappers/databricks_aws_wrapper.py b/user_tools/src/spark_rapids_pytools/wrappers/databricks_aws_wrapper.py index ce1f6ca70..8411eb6db 100644 --- a/user_tools/src/spark_rapids_pytools/wrappers/databricks_aws_wrapper.py +++ b/user_tools/src/spark_rapids_pytools/wrappers/databricks_aws_wrapper.py @@ -103,7 +103,7 @@ def qualification(cpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: # when debug is set to true set it in the environment. @@ -190,7 +190,7 @@ def profiling(gpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. For more details on Profiling tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options """ if verbose: # when debug is set to true set it in the environment. diff --git a/user_tools/src/spark_rapids_pytools/wrappers/databricks_azure_wrapper.py b/user_tools/src/spark_rapids_pytools/wrappers/databricks_azure_wrapper.py index cff76e536..197c42a04 100644 --- a/user_tools/src/spark_rapids_pytools/wrappers/databricks_azure_wrapper.py +++ b/user_tools/src/spark_rapids_pytools/wrappers/databricks_azure_wrapper.py @@ -101,7 +101,7 @@ def qualification(cpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: # when debug is set to true set it in the environment. @@ -184,7 +184,7 @@ def profiling(gpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. For more details on Profiling tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options """ if verbose: # when debug is set to true set it in the environment. diff --git a/user_tools/src/spark_rapids_pytools/wrappers/dataproc_gke_wrapper.py b/user_tools/src/spark_rapids_pytools/wrappers/dataproc_gke_wrapper.py index 87b9e4bfe..251347a28 100644 --- a/user_tools/src/spark_rapids_pytools/wrappers/dataproc_gke_wrapper.py +++ b/user_tools/src/spark_rapids_pytools/wrappers/dataproc_gke_wrapper.py @@ -98,7 +98,7 @@ def qualification(cpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: # when debug is set to true set it in the environment. diff --git a/user_tools/src/spark_rapids_pytools/wrappers/dataproc_wrapper.py b/user_tools/src/spark_rapids_pytools/wrappers/dataproc_wrapper.py index 65b03e0b4..a02fee408 100644 --- a/user_tools/src/spark_rapids_pytools/wrappers/dataproc_wrapper.py +++ b/user_tools/src/spark_rapids_pytools/wrappers/dataproc_wrapper.py @@ -100,7 +100,7 @@ def qualification(cpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: # when debug is set to true set it in the environment. @@ -181,7 +181,7 @@ def profiling(gpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. For more details on Profiling tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options """ if verbose: # when debug is set to true set it in the environment. diff --git a/user_tools/src/spark_rapids_pytools/wrappers/emr_wrapper.py b/user_tools/src/spark_rapids_pytools/wrappers/emr_wrapper.py index 225075236..fea22b044 100644 --- a/user_tools/src/spark_rapids_pytools/wrappers/emr_wrapper.py +++ b/user_tools/src/spark_rapids_pytools/wrappers/emr_wrapper.py @@ -98,7 +98,7 @@ def qualification(cpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: # when debug is set to true set it in the environment. @@ -175,7 +175,7 @@ def profiling(gpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. For more details on Profiling tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options """ if verbose: # when debug is set to true set it in the environment. diff --git a/user_tools/src/spark_rapids_pytools/wrappers/onprem_wrapper.py b/user_tools/src/spark_rapids_pytools/wrappers/onprem_wrapper.py index ac8bf8454..4d50b0c28 100644 --- a/user_tools/src/spark_rapids_pytools/wrappers/onprem_wrapper.py +++ b/user_tools/src/spark_rapids_pytools/wrappers/onprem_wrapper.py @@ -78,7 +78,7 @@ def qualification(cpu_cluster: str = None, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: # when debug is set to true set it in the environment. @@ -156,7 +156,7 @@ def profiling(worker_info: str = None, Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. For more details on Profiling tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options """ if verbose: 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 5499bc961..238f83a89 100644 --- a/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py +++ b/user_tools/src/spark_rapids_tools/cmdli/tools_cli.py @@ -103,7 +103,7 @@ def qualification(self, Note that the wrapper ignores ["output-directory", "platform"] flags, and it does not support multiple "spark-property" arguments. For more details on Qualification tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-qualification-tool.html#qualification-tool-options """ if verbose: ToolLogging.enable_debug_mode() @@ -157,7 +157,7 @@ def profiling(self, Note that the wrapper ignores ["output-directory", "worker-info"] flags, and it does not support multiple "spark-property" arguments. For more details on Profiling tool options, please visit - https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options + https://docs.nvidia.com/spark-rapids/user-guide/latest/spark-profiling-tool.html#profiling-tool-options """ if verbose: ToolLogging.enable_debug_mode() 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 49d8f9cec..0e5d496d0 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 @@ -167,7 +167,7 @@ def test_cluster_props_no_eventlogs_on_prem(self, capsys, tool_name): assert pytest_wrapped_e.type == SystemExit captured = capsys.readouterr() # Verify there is no URL in error message except for the one from the documentation - assert 'https://' not in captured.err or 'nvidia.github.io' in captured.err + assert 'https://' not in captured.err or 'docs.nvidia.com' in captured.err @pytest.mark.skip(reason='Unit tests are not completed yet') def test_arg_cases_coverage(self): From b61e307d19fb6bcb7c3fb9b157507b5029122451 Mon Sep 17 00:00:00 2001 From: Niranjan Artal <50492963+nartal1@users.noreply.github.com> Date: Wed, 15 Nov 2023 11:12:26 -0800 Subject: [PATCH 9/9] Profiling tool: Add support for driver log as input to generate unsupported operators report (#654) * Profiler tool: Add support for driver log as input Signed-off-by: Niranjan Artal * update driverlog * addressed review comments * addressed review comment * Update typo Co-authored-by: Thomas Graves * Update typo Co-authored-by: Thomas Graves * update eventlog check --------- Signed-off-by: Niranjan Artal Co-authored-by: Thomas Graves --- .../tool/profiling/ClassWarehouse.scala | 14 + .../tool/profiling/DriverLogProcessor.scala | 48 +++ .../rapids/tool/profiling/ProfileArgs.scala | 12 +- .../rapids/tool/profiling/ProfileMain.scala | 18 +- .../rapids/tool/profiling/Profiler.scala | 15 + .../spark-events-profiling/driverlog | 298 ++++++++++++++++++ .../tool/profiling/ApplicationInfoSuite.scala | 26 ++ 7 files changed, 426 insertions(+), 5 deletions(-) create mode 100644 core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/DriverLogProcessor.scala create mode 100644 core/src/test/resources/spark-events-profiling/driverlog diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ClassWarehouse.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ClassWarehouse.scala index cf3b2e2ec..c6f38c279 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ClassWarehouse.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ClassWarehouse.scala @@ -168,6 +168,20 @@ extends ProfileResult { } } +case class DriverLogUnsupportedOperators( + operatorName: String, count: Int, reason: String) extends ProfileResult { + override val outputHeaders = Seq("operatorName", "count", "reason") + + override def convertToSeq: Seq[String] = { + Seq(operatorName, count.toString, reason) + } + + override def convertToCSVSeq: Seq[String] = { + Seq(StringUtils.reformatCSVString(operatorName), count.toString, + StringUtils.reformatCSVString(reason)) + } +} + class StageInfoClass(val info: StageInfo) { var completionTime: Option[Long] = None var failureReason: Option[String] = None diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/DriverLogProcessor.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/DriverLogProcessor.scala new file mode 100644 index 000000000..6ead13466 --- /dev/null +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/DriverLogProcessor.scala @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2023, 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.profiling + +import scala.io.Source + +import org.apache.spark.internal.Logging + +class DriverLogProcessor(driverlogPath: String) extends Logging { + def processDriverLog(): Seq[DriverLogUnsupportedOperators] = { + val source = Source.fromFile(driverlogPath) + // Create a map to store the counts for each operator and reason + var countsMap = Map[(String, String), Int]().withDefaultValue(0) + try { + // Process each line in the file + for (line <- source.getLines()) { + // condition to check if the line contains unsupported operators + if (line.contains("cannot run on GPU") && + !line.contains("not all expressions can be replaced")) { + val operatorName = line.split("<")(1).split(">")(0) + val reason = line.split("because")(1).trim() + val key = (operatorName, reason) + countsMap += key -> (countsMap(key) + 1) + } + } + } catch { + case e: Exception => + logError(s"Unexpected exception processing driver log: $driverlogPath", e) + } finally { + source.close() + } + countsMap.map(x => DriverLogUnsupportedOperators(x._1._1, x._2, x._1._2)).toSeq + } +} \ No newline at end of file diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileArgs.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileArgs.scala index f553bcb73..b1044a4ed 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileArgs.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileArgs.scala @@ -28,7 +28,7 @@ Profiling Tool for the RAPIDS Accelerator and Apache Spark Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* com.nvidia.spark.rapids.tool.profiling.ProfileMain [options] - + [eventlogs | eventlog directories ...] """) val outputDirectory: ScallopOption[String] = @@ -38,8 +38,11 @@ Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* " rapids_4_spark_profile. It will overwrite any existing files" + " with the same name.", default = Some(".")) + val driverlog: ScallopOption[String] = + opt[String](required = false, + descr = "Driver log filename - eg: /path/to/driverlog. Default is empty.") val eventlog: ScallopOption[List[String]] = - trailArg[List[String]](required = true, + trailArg[List[String]](required = false, descr = "Event log filenames(space separated) or directories containing event logs." + " eg: s3a:///eventlog1 /path/to/eventlog2") val filterCriteria: ScallopOption[String] = @@ -143,6 +146,11 @@ Usage: java -cp rapids-4-spark-tools_2.12-.jar:$SPARK_HOME/jars/* Right(Unit) } + // verify that either driverlog or eventlog is specified + validateOpt(driverlog, eventlog) { + case (None, None) => Left("Error, one of driverlog or eventlog must be specified") + case _ => Right(Unit) + } verify() override def onError(e: Throwable) = e match { diff --git a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileMain.scala b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileMain.scala index b86b49525..d839f76a8 100644 --- a/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileMain.scala +++ b/core/src/main/scala/com/nvidia/spark/rapids/tool/profiling/ProfileMain.scala @@ -42,7 +42,8 @@ object ProfileMain extends Logging { def mainInternal(appArgs: ProfileArgs, enablePB: Boolean = false): (Int, Int) = { // Parsing args - val eventlogPaths = appArgs.eventlog() + val eventlogPaths = appArgs.eventlog.getOrElse(List.empty[String]) + val driverLog = appArgs.driverlog.getOrElse("") val filterN = appArgs.filterCriteria val matchEventLogs = appArgs.matchEventLogs val hadoopConf = RapidsToolsConfUtil.newHadoopConf @@ -62,13 +63,24 @@ object ProfileMain extends Logging { eventLogFsFiltered } - if (filteredLogs.isEmpty) { - logWarning("No event logs to process after checking paths, exiting!") + if (filteredLogs.isEmpty && driverLog.isEmpty) { + logWarning("No event logs to process after checking paths and no driver log " + + "to process, exiting!") + return (0, filteredLogs.size) + } + + // Check that only one eventlog is provided when driver log is passed + if (driverLog.nonEmpty && filteredLogs.size > 1) { + logWarning("Only a single eventlog should be provided for processing " + + "when a driver log is passed, exiting!") return (0, filteredLogs.size) } val profiler = new Profiler(hadoopConf, appArgs, enablePB) profiler.profile(eventLogFsFiltered) + if (driverLog.nonEmpty){ + profiler.profileDriver(driverLog) + } (0, filteredLogs.size) } 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 b61b58a97..44528966b 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 @@ -124,6 +124,20 @@ class Profiler(hadoopConf: Configuration, appArgs: ProfileArgs, enablePB: Boolea progressBar.foreach(_.finishAll()) } + def profileDriver(driverLogInfos: String): Unit = { + val profileOutputWriter = new ProfileOutputWriter(s"$outputDir/driver", + Profiler.DRIVER_LOG_NAME, numOutputRows, true) + + try { + val driverLogProcessor = new DriverLogProcessor(driverLogInfos) + val unsupportedDrivers = driverLogProcessor.processDriverLog() + profileOutputWriter.write(s"Unsupported operators in driver log", + unsupportedDrivers) + } finally { + profileOutputWriter.close() + } + } + private def errorHandler(error: Throwable, path: EventLogInfo) = { error match { case oom: OutOfMemoryError => @@ -530,6 +544,7 @@ class Profiler(hadoopConf: Configuration, appArgs: ProfileArgs, enablePB: Boolea object Profiler { // This tool's output log file name val PROFILE_LOG_NAME = "profile" + val DRIVER_LOG_NAME = "driver" val COMPARE_LOG_FILE_NAME_PREFIX = "rapids_4_spark_tools_compare" val COMBINED_LOG_FILE_NAME_PREFIX = "rapids_4_spark_tools_combined" val SUBDIR = "rapids_4_spark_profile" diff --git a/core/src/test/resources/spark-events-profiling/driverlog b/core/src/test/resources/spark-events-profiling/driverlog new file mode 100644 index 000000000..e608d1e06 --- /dev/null +++ b/core/src/test/resources/spark-events-profiling/driverlog @@ -0,0 +1,298 @@ +23/11/07 10:59:24.335 main INFO DriverLogger: Added a local log appender at: /tmp/spark-33ef7368-7a1e-44a1-ab42-ccbd843dabf4/__driver_logs__/driver.log +23/11/07 10:59:24.345 main INFO ResourceUtils: ============================================================== +23/11/07 10:59:24.345 main INFO ResourceUtils: No custom resources configured for spark.driver. +23/11/07 10:59:24.346 main INFO ResourceUtils: ============================================================== +23/11/07 10:59:24.346 main INFO SparkContext: Submitted application: Spark shell +23/11/07 10:59:24.358 main INFO ResourceProfile: Default ResourceProfile created, executor resources: Map(cores -> name: cores, amount: 1, script: , vendor: , memory -> name: memory, amount: 1024, script: , vendor: , offHeap -> name: offHeap, amount: 0, script: , vendor: ), task resources: Map(cpus -> name: cpus, amount: 1.0) +23/11/07 10:59:24.367 main INFO ResourceProfile: Limiting resource is cpu +23/11/07 10:59:24.368 main INFO ResourceProfileManager: Added ResourceProfile id: 0 +23/11/07 10:59:24.397 main INFO SecurityManager: Changing view acls to: test +23/11/07 10:59:24.397 main INFO SecurityManager: Changing modify acls to: test +23/11/07 10:59:24.397 main INFO SecurityManager: Changing view acls groups to: +23/11/07 10:59:24.397 main INFO SecurityManager: Changing modify acls groups to: +23/11/07 10:59:24.398 main INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(test); groups with view permissions: Set(); users with modify permissions: Set(test); groups with modify permissions: Set() +23/11/07 10:59:24.555 main INFO Utils: Successfully started service 'sparkDriver' on port 35365. +23/11/07 10:59:24.573 main INFO SparkEnv: Registering MapOutputTracker +23/11/07 10:59:24.591 main INFO SparkEnv: Registering BlockManagerMaster +23/11/07 10:59:24.606 main INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information +23/11/07 10:59:24.606 main INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up +23/11/07 10:59:24.608 main INFO SparkEnv: Registering BlockManagerMasterHeartbeat +23/11/07 10:59:24.622 main INFO DiskBlockManager: Created local directory at /tmp/blockmgr-385b425e-945b-4542-b103-53b1fd7b99ef +23/11/07 10:59:24.632 main INFO MemoryStore: MemoryStore started with capacity 366.3 MiB +23/11/07 10:59:24.642 main INFO SparkEnv: Registering OutputCommitCoordinator +23/11/07 10:59:24.793 main WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041. +23/11/07 10:59:24.803 main INFO Utils: Successfully started service 'SparkUI' on port 4041. +23/11/07 10:59:24.834 main INFO SparkContext: Added JAR file:///home/test/spark-rapids-Mar6/spark-rapids/dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar at spark://localhost:35365/jars/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar with timestamp 1699383564319 +23/11/07 10:59:24.835 main INFO SparkContext: Added JAR file:/home/test/apache-spark-3.3/spark-3.3.0-bin-hadoop3.2/jars/HikariCP-2.5.1.jar at spark://localhost:35365/jars/HikariCP-2.5.1.jar with timestamp 1699383564319 +23/11/07 10:59:24.852 main INFO SparkContext: Added JAR file:/home/test/apache-spark-3.3/spark-3.3.0-bin-hadoop3.2/jars/py4j-0.10.9.5.jar at spark://localhost:35365/jars/py4j-0.10.9.5.jar with timestamp 1699383564319 +23/11/07 10:59:24.852 main INFO SparkContext: Added JAR file:/home/test/apache-spark-3.3/spark-3.3.0-bin-hadoop3.2/jars/rocksdbjni-6.20.3.jar at spark://localhost:35365/jars/rocksdbjni-6.20.3.jar with timestamp 1699383564319 +23/11/07 10:59:24.852 main INFO SparkContext: Added JAR file:/home/test/apache-spark-3.3/spark-3.3.0-bin-hadoop3.2/jars/scala-collection-compat_2.12-2.1.1.jar at spark://localhost:35365/jars/scala-collection-compat_2.12-2.1.1.jar with timestamp 1699383564319 +23/11/07 10:59:24.856 main INFO SparkContext: Added JAR file:/home/test/apache-spark-3.3/spark-3.3.0-bin-hadoop3.2/jars/zstd-jni-1.5.2-1.jar at spark://localhost:35365/jars/zstd-jni-1.5.2-1.jar with timestamp 1699383564319 +23/11/07 10:59:24.870 main INFO ShimLoader: Loading shim for Spark version: 3.3.0 +23/11/07 10:59:24.870 main INFO ShimLoader: Complete Spark build info: 3.3.0, https://github.com/apache/spark, HEAD, f74867bddfbcdd4d08076db36851e88b15e66556, 2022-06-09T19:58:58Z +23/11/07 10:59:24.886 main INFO ShimLoader: findURLClassLoader found an immutable scala.tools.nsc.interpreter.IMain$TranslatingClassLoader@dbed7fd, trying parent=scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@3211cc84 +23/11/07 10:59:24.886 main INFO ShimLoader: findURLClassLoader found a URLClassLoader scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@3211cc84 +23/11/07 10:59:24.887 main INFO ShimLoader: Updating spark classloader scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@3211cc84 with the URLs: jar:file:/home/test/spark-rapids-Mar6/spark-rapids/dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar!/spark3xx-common/, jar:file:/home/test/spark-rapids-Mar6/spark-rapids/dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar!/spark330/ +23/11/07 10:59:24.890 main INFO ShimLoader: Spark classLoader scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@3211cc84 updated successfully +23/11/07 10:59:24.890 main INFO ShimLoader: Updating spark classloader scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@3211cc84 with the URLs: jar:file:/home/test/spark-rapids-Mar6/spark-rapids/dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar!/spark3xx-common/, jar:file:/home/test/spark-rapids-Mar6/spark-rapids/dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar!/spark330/ +23/11/07 10:59:24.891 main INFO ShimLoader: Spark classLoader scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@3211cc84 updated successfully +23/11/07 10:59:24.896 main INFO RapidsPluginUtils: RAPIDS Accelerator build: {version=23.10.0-SNAPSHOT, user=test, url=git@github.com:NVIDIA/spark-rapids.git, date=2023-11-07T01:56:43Z, revision=7c331d7b805da50c264f71722434d70a26917bd7, cudf_version=23.10.0, branch=Nov6-23.10} +23/11/07 10:59:24.897 main INFO RapidsPluginUtils: RAPIDS Accelerator JNI build: {version=23.10.0, user=, url=https://github.com/NVIDIA/spark-rapids-jni.git, date=2023-10-12T02:48:23Z, revision=e5fb14eb4bd4087be9b5a7e960edb27fc76ffc2d, branch=HEAD} +23/11/07 10:59:24.897 main INFO RapidsPluginUtils: cudf build: {version=23.10.0, user=, url=https://github.com/rapidsai/cudf.git, date=2023-10-12T02:48:23Z, revision=9f0c2f452f1cf318c3f7fe2c6f7e07fc513fc335, branch=HEAD} +23/11/07 10:59:24.897 main WARN RapidsPluginUtils: RAPIDS Accelerator 23.10.0-SNAPSHOT using cudf 23.10.0. +23/11/07 10:59:24.927 main WARN RapidsPluginUtils: RAPIDS Accelerator is enabled, to disable GPU support set `spark.rapids.sql.enabled` to false. +23/11/07 10:59:24.927 main WARN RapidsPluginUtils: spark.rapids.sql.explain is set to `ALL`. Set it to 'NONE' to suppress the diagnostics logging about the query placement on the GPU. +23/11/07 10:59:24.944 main INFO DriverPluginContainer: Initialized driver component for plugin com.nvidia.spark.SQLPlugin. +23/11/07 10:59:24.980 main INFO Executor: Starting executor ID driver on host localhost +23/11/07 10:59:24.984 main INFO Executor: Starting executor with user classpath (userClassPathFirst = false): '' +23/11/07 10:59:24.985 main INFO Executor: Using REPL class URI: spark://localhost:35365/classes +23/11/07 10:59:24.996 main INFO Executor: Fetching spark://localhost:35365/jars/aopalliance-repackaged-2.6.1.jar with timestamp 1699383564319 +23/11/07 10:59:25.039 main INFO TransportClientFactory: Successfully created connection to /localhost:35365 after 25 ms (0 ms spent in bootstraps) +23/11/07 10:59:25.045 main INFO Utils: Fetching spark://localhost:35365/jars/aopalliance-repackaged-2.6.1.jar to /tmp/spark-33ef7368-7a1e-44a1-ab42-ccbd843dabf4/userFiles-348d1b75-5c34-4b0b-9359-9585690c1931/fetchFileTemp6383081176374586614.tmp +23/11/07 10:59:25.063 main INFO Executor: Adding file:/tmp/spark-33ef7368-7a1e-44a1-ab42-ccbd843dabf4/userFiles-348d1b75-5c34-4b0b-9359-9585690c1931/aopalliance-repackaged-2.6.1.jar to class loader +23/11/07 10:59:25.063 main INFO Executor: Fetching spark://localhost:35365/jars/zjsonpatch-0.3.0.jar with timestamp 1699383564319 +23/11/07 10:59:25.064 main INFO Utils: Fetching spark://localhost:35365/jars/zjsonpatch-0.3.0.jar to /tmp/spark-33ef7368-7a1e-44a1-ab42-ccbd843dabf4/userFiles-348d1b75-5c34-4b0b-9359-9585690c1931/fetchFileTemp7766683870504964453.tmp +23/11/07 11:00:23.983 dag-scheduler-event-loop INFO DAGScheduler: Got job 0 (parquet at :23) with 1824 output partitions +23/11/07 11:00:23.984 dag-scheduler-event-loop INFO DAGScheduler: Final stage: ResultStage 0 (parquet at :23) +23/11/07 11:00:23.984 dag-scheduler-event-loop INFO DAGScheduler: Parents of final stage: List() +23/11/07 11:00:23.985 dag-scheduler-event-loop INFO DAGScheduler: Missing parents: List() +23/11/07 11:00:23.988 dag-scheduler-event-loop INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[2] at parquet at :23), which has no missing parents +23/11/07 11:00:24.056 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 164.2 KiB, free 366.1 MiB) +23/11/07 11:00:24.075 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 49.6 KiB, free 366.1 MiB) +23/11/07 11:00:24.077 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:34199 (size: 49.6 KiB, free: 366.3 MiB) +23/11/07 11:00:24.080 dag-scheduler-event-loop INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1513 +23/11/07 11:00:24.092 dag-scheduler-event-loop INFO DAGScheduler: Submitting 1824 missing tasks from ResultStage 0 (MapPartitionsRDD[2] at parquet at :23) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) +23/11/07 11:00:24.092 dag-scheduler-event-loop INFO TaskSchedulerImpl: Adding task set 0.0 with 1824 tasks resource profile 0 +23/11/07 11:00:24.144 dispatcher-event-loop-25 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0) (localhost, executor driver, partition 0, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:24.147 dispatcher-event-loop-25 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1) (localhost, executor driver, partition 1, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:24.182 Executor task launch worker for task 30.0 in stage 0.0 (TID 30) INFO Executor: Running task 30.0 in stage 0.0 (TID 30) +23/11/07 11:00:24.182 Executor task launch worker for task 52.0 in stage 0.0 (TID 52) INFO Executor: Running task 52.0 in stage 0.0 (TID 52) +23/11/07 11:00:24.182 Executor task launch worker for task 24.0 in stage 0.0 (TID 24) INFO Executor: Running task 24.0 in stage 0.0 (TID 24) +23/11/07 11:00:24.929 Executor task launch worker for task 43.0 in stage 0.0 (TID 43) INFO Executor: Finished task 43.0 in stage 0.0 (TID 43). 1998 bytes result sent to driver +23/11/07 11:00:24.929 Executor task launch worker for task 37.0 in stage 0.0 (TID 37) INFO Executor: Finished task 37.0 in stage 0.0 (TID 37). 2041 bytes result sent to driver +23/11/07 11:00:24.929 Executor task launch worker for task 23.0 in stage 0.0 (TID 23) INFO Executor: Finished task 23.0 in stage 0.0 (TID 23). 2041 bytes result sent to driver +23/11/07 11:00:24.932 Executor task launch worker for task 29.0 in stage 0.0 (TID 29) INFO Executor: Finished task 29.0 in stage 0.0 (TID 29). 1998 bytes result sent to driver +23/11/07 11:00:24.932 Executor task launch worker for task 48.0 in stage 0.0 (TID 48) INFO Executor: Finished task 48.0 in stage 0.0 (TID 48). 2041 bytes result sent to driver +23/11/07 11:00:24.936 dispatcher-event-loop-27 INFO TaskSetManager: Starting task 64.0 in stage 0.0 (TID 64) (localhost, executor driver, partition 64, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:24.936 Executor task launch worker for task 64.0 in stage 0.0 (TID 64) INFO Executor: Running task 64.0 in stage 0.0 (TID 64) +23/11/07 11:00:24.937 dispatcher-event-loop-27 INFO TaskSetManager: Starting task 65.0 in stage 0.0 (TID 65) (localhost, executor driver, partition 65, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:26.196 Executor task launch worker for task 1058.0 in stage 0.0 (TID 1058) INFO Executor: Running task 1058.0 in stage 0.0 (TID 1058) +23/11/07 11:00:26.196 Executor task launch worker for task 1001.0 in stage 0.0 (TID 1001) INFO Executor: Finished task 1001.0 in stage 0.0 (TID 1001). 1955 bytes result sent to driver +23/11/07 11:00:26.196 dispatcher-event-loop-48 INFO TaskSetManager: Starting task 1059.0 in stage 0.0 (TID 1059) (localhost, executor driver, partition 1059, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:26.196 Executor task launch worker for task 1059.0 in stage 0.0 (TID 1059) INFO Executor: Running task 1059.0 in stage 0.0 (TID 1059) +23/11/07 11:00:26.196 task-result-getter-3 INFO TaskSetManager: Finished task 1001.0 in stage 0.0 (TID 1001) in 81 ms on localhost (executor driver) (996/1824) +23/11/07 11:00:26.196 Executor task launch worker for task 998.0 in stage 0.0 (TID 998) INFO Executor: Finished task 998.0 in stage 0.0 (TID 998). 1955 bytes result sent to driver +23/11/07 11:00:26.196 dispatcher-event-loop-43 INFO TaskSetManager: Starting task 1060.0 in stage 0.0 (TID 1060) (localhost, executor driver, partition 1060, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:26.197 Executor task launch worker for task 1060.0 in stage 0.0 (TID 1060) INFO Executor: Running task 1060.0 in stage 0.0 (TID 1060) +23/11/07 11:00:26.197 task-result-getter-1 INFO TaskSetManager: Finished task 998.0 in stage 0.0 (TID 998) in 85 ms on localhost (executor driver) (997/1824) +23/11/07 11:00:27.192 task-result-getter-1 INFO TaskSetManager: Finished task 1606.0 in stage 0.0 (TID 1606) in 148 ms on localhost (executor driver) (1630/1824) +23/11/07 11:00:27.192 Executor task launch worker for task 1693.0 in stage 0.0 (TID 1693) INFO Executor: Running task 1693.0 in stage 0.0 (TID 1693) +23/11/07 11:00:27.193 Executor task launch worker for task 1635.0 in stage 0.0 (TID 1635) INFO Executor: Finished task 1635.0 in stage 0.0 (TID 1635). 1955 bytes result sent to driver +23/11/07 11:00:27.194 dispatcher-event-loop-35 INFO TaskSetManager: Starting task 1694.0 in stage 0.0 (TID 1694) (localhost, executor driver, partition 1694, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.194 task-result-getter-0 INFO TaskSetManager: Finished task 1635.0 in stage 0.0 (TID 1635) in 89 ms on localhost (executor driver) (1631/1824) +23/11/07 11:00:27.194 Executor task launch worker for task 1694.0 in stage 0.0 (TID 1694) INFO Executor: Running task 1694.0 in stage 0.0 (TID 1694) +23/11/07 11:00:27.194 Executor task launch worker for task 1617.0 in stage 0.0 (TID 1617) INFO Executor: Finished task 1617.0 in stage 0.0 (TID 1617). 1955 bytes result sent to driver +23/11/07 11:00:27.194 dispatcher-event-loop-25 INFO TaskSetManager: Starting task 1695.0 in stage 0.0 (TID 1695) (localhost, executor driver, partition 1695, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.194 task-result-getter-2 INFO TaskSetManager: Finished task 1617.0 in stage 0.0 (TID 1617) in 142 ms on localhost (executor driver) (1632/1824) +23/11/07 11:00:27.194 Executor task launch worker for task 1695.0 in stage 0.0 (TID 1695) INFO Executor: Running task 1695.0 in stage 0.0 (TID 1695) +23/11/07 11:00:27.194 Executor task launch worker for task 1624.0 in stage 0.0 (TID 1624) INFO Executor: Finished task 1624.0 in stage 0.0 (TID 1624). 1955 bytes result sent to driver +23/11/07 11:00:27.195 dispatcher-event-loop-53 INFO TaskSetManager: Starting task 1696.0 in stage 0.0 (TID 1696) (localhost, executor driver, partition 1696, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.195 task-result-getter-3 INFO TaskSetManager: Finished task 1624.0 in stage 0.0 (TID 1624) in 97 ms on localhost (executor driver) (1633/1824) +23/11/07 11:00:27.195 Executor task launch worker for task 1696.0 in stage 0.0 (TID 1696) INFO Executor: Running task 1696.0 in stage 0.0 (TID 1696) +23/11/07 11:00:27.196 Executor task launch worker for task 1645.0 in stage 0.0 (TID 1645) INFO Executor: Finished task 1645.0 in stage 0.0 (TID 1645). 1955 bytes result sent to driver +23/11/07 11:00:27.196 Executor task launch worker for task 1643.0 in stage 0.0 (TID 1643) INFO Executor: Finished task 1643.0 in stage 0.0 (TID 1643). 1955 bytes result sent to driver +23/11/07 11:00:27.196 dispatcher-event-loop-49 INFO TaskSetManager: Starting task 1697.0 in stage 0.0 (TID 1697) (localhost, executor driver, partition 1697, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.197 Executor task launch worker for task 1697.0 in stage 0.0 (TID 1697) INFO Executor: Running task 1697.0 in stage 0.0 (TID 1697) +23/11/07 11:00:27.197 dispatcher-event-loop-49 INFO TaskSetManager: Starting task 1698.0 in stage 0.0 (TID 1698) (localhost, executor driver, partition 1698, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.197 task-result-getter-1 INFO TaskSetManager: Finished task 1645.0 in stage 0.0 (TID 1645) in 83 ms on localhost (executor driver) (1634/1824) +23/11/07 11:00:27.197 Executor task launch worker for task 1698.0 in stage 0.0 (TID 1698) INFO Executor: Running task 1698.0 in stage 0.0 (TID 1698) +23/11/07 11:00:27.197 task-result-getter-0 INFO TaskSetManager: Finished task 1643.0 in stage 0.0 (TID 1643) in 84 ms on localhost (executor driver) (1635/1824) +23/11/07 11:00:27.197 Executor task launch worker for task 1662.0 in stage 0.0 (TID 1662) INFO Executor: Finished task 1662.0 in stage 0.0 (TID 1662). 1955 bytes result sent to driver +23/11/07 11:00:27.197 dispatcher-event-loop-51 INFO TaskSetManager: Starting task 1699.0 in stage 0.0 (TID 1699) (localhost, executor driver, partition 1699, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.198 Executor task launch worker for task 1699.0 in stage 0.0 (TID 1699) INFO Executor: Running task 1699.0 in stage 0.0 (TID 1699) +23/11/07 11:00:27.198 task-result-getter-2 INFO TaskSetManager: Finished task 1662.0 in stage 0.0 (TID 1662) in 76 ms on localhost (executor driver) (1636/1824) +23/11/07 11:00:27.198 Executor task launch worker for task 1638.0 in stage 0.0 (TID 1638) INFO Executor: Finished task 1638.0 in stage 0.0 (TID 1638). 1955 bytes result sent to driver +23/11/07 11:00:27.198 dispatcher-event-loop-42 INFO TaskSetManager: Starting task 1700.0 in stage 0.0 (TID 1700) (localhost, executor driver, partition 1700, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.198 Executor task launch worker for task 1700.0 in stage 0.0 (TID 1700) INFO Executor: Running task 1700.0 in stage 0.0 (TID 1700) +23/11/07 11:00:27.198 task-result-getter-3 INFO TaskSetManager: Finished task 1638.0 in stage 0.0 (TID 1638) in 92 ms on localhost (executor driver) (1637/1824) +23/11/07 11:00:27.198 Executor task launch worker for task 1648.0 in stage 0.0 (TID 1648) INFO Executor: Finished task 1648.0 in stage 0.0 (TID 1648). 1955 bytes result sent to driver +23/11/07 11:00:27.199 dispatcher-event-loop-30 INFO TaskSetManager: Starting task 1701.0 in stage 0.0 (TID 1701) (localhost, executor driver, partition 1701, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.199 Executor task launch worker for task 1701.0 in stage 0.0 (TID 1701) INFO Executor: Running task 1701.0 in stage 0.0 (TID 1701) +23/11/07 11:00:27.199 task-result-getter-1 INFO TaskSetManager: Finished task 1648.0 in stage 0.0 (TID 1648) in 84 ms on localhost (executor driver) (1638/1824) +23/11/07 11:00:27.200 Executor task launch worker for task 1628.0 in stage 0.0 (TID 1628) INFO Executor: Finished task 1628.0 in stage 0.0 (TID 1628). 1955 bytes result sent to driver +23/11/07 11:00:27.200 dispatcher-event-loop-60 INFO TaskSetManager: Starting task 1702.0 in stage 0.0 (TID 1702) (localhost, executor driver, partition 1702, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.200 Executor task launch worker for task 1702.0 in stage 0.0 (TID 1702) INFO Executor: Running task 1702.0 in stage 0.0 (TID 1702) +23/11/07 11:00:27.200 task-result-getter-0 INFO TaskSetManager: Finished task 1628.0 in stage 0.0 (TID 1628) in 99 ms on localhost (executor driver) (1639/1824) +23/11/07 11:00:27.203 task-result-getter-3 INFO TaskSetManager: Finished task 1647.0 in stage 0.0 (TID 1647) in 88 ms on localhost (executor driver) (1645/1824) +23/11/07 11:00:27.205 Executor task launch worker for task 1673.0 in stage 0.0 (TID 1673) INFO Executor: Finished task 1673.0 in stage 0.0 (TID 1673). 1955 bytes result sent to driver +23/11/07 11:00:27.205 dispatcher-event-loop-6 INFO TaskSetManager: Starting task 1709.0 in stage 0.0 (TID 1709) (localhost, executor driver, partition 1709, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.205 Executor task launch worker for task 1660.0 in stage 0.0 (TID 1660) INFO Executor: Finished task 1660.0 in stage 0.0 (TID 1660). 1955 bytes result sent to driver +23/11/07 11:00:27.205 Executor task launch worker for task 1632.0 in stage 0.0 (TID 1632) INFO Executor: Finished task 1632.0 in stage 0.0 (TID 1632). 1955 bytes result sent to driver +23/11/07 11:00:27.205 Executor task launch worker for task 1709.0 in stage 0.0 (TID 1709) INFO Executor: Running task 1709.0 in stage 0.0 (TID 1709) +23/11/07 11:00:27.205 Executor task launch worker for task 1631.0 in stage 0.0 (TID 1631) INFO Executor: Finished task 1631.0 in stage 0.0 (TID 1631). 1955 bytes result sent to driver +23/11/07 11:00:27.205 Executor task launch worker for task 1642.0 in stage 0.0 (TID 1642) INFO Executor: Finished task 1642.0 in stage 0.0 (TID 1642). 1955 bytes result sent to driver +23/11/07 11:00:27.205 dispatcher-event-loop-10 INFO TaskSetManager: Starting task 1710.0 in stage 0.0 (TID 1710) (localhost, executor driver, partition 1710, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.205 task-result-getter-1 INFO TaskSetManager: Finished task 1673.0 in stage 0.0 (TID 1673) in 73 ms on localhost (executor driver) (1646/1824) +23/11/07 11:00:27.205 Executor task launch worker for task 1710.0 in stage 0.0 (TID 1710) INFO Executor: Running task 1710.0 in stage 0.0 (TID 1710) +23/11/07 11:00:27.205 dispatcher-event-loop-10 INFO TaskSetManager: Starting task 1711.0 in stage 0.0 (TID 1711) (localhost, executor driver, partition 1711, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.205 Executor task launch worker for task 1711.0 in stage 0.0 (TID 1711) INFO Executor: Running task 1711.0 in stage 0.0 (TID 1711) +23/11/07 11:00:27.287 Executor task launch worker for task 1728.0 in stage 0.0 (TID 1728) INFO Executor: Finished task 1728.0 in stage 0.0 (TID 1728). 1955 bytes result sent to driver +23/11/07 11:00:27.302 Executor task launch worker for task 1745.0 in stage 0.0 (TID 1745) INFO Executor: Finished task 1745.0 in stage 0.0 (TID 1745). 1955 bytes result sent to driver +23/11/07 11:00:27.302 Executor task launch worker for task 1790.0 in stage 0.0 (TID 1790) INFO Executor: Running task 1790.0 in stage 0.0 (TID 1790) +23/11/07 11:00:27.322 Executor task launch worker for task 1822.0 in stage 0.0 (TID 1822) INFO Executor: Running task 1822.0 in stage 0.0 (TID 1822) +23/11/07 11:00:27.322 task-result-getter-0 INFO TaskSetManager: Finished task 1779.0 in stage 0.0 (TID 1779) in 30 ms on localhost (executor driver) (1759/1824) +23/11/07 11:00:27.322 Executor task launch worker for task 1764.0 in stage 0.0 (TID 1764) INFO Executor: Finished task 1764.0 in stage 0.0 (TID 1764). 1912 bytes result sent to driver +23/11/07 11:00:27.322 dispatcher-event-loop-36 INFO TaskSetManager: Starting task 1823.0 in stage 0.0 (TID 1823) (localhost, executor driver, partition 1823, PROCESS_LOCAL, 4550 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.322 Executor task launch worker for task 1823.0 in stage 0.0 (TID 1823) INFO Executor: Running task 1823.0 in stage 0.0 (TID 1823) +23/11/07 11:00:27.322 task-result-getter-3 INFO TaskSetManager: Finished task 1764.0 in stage 0.0 (TID 1764) in 42 ms on localhost (executor driver) (1760/1824) +23/11/07 11:00:27.324 Executor task launch worker for task 1760.0 in stage 0.0 (TID 1760) INFO Executor: Finished task 1760.0 in stage 0.0 (TID 1760). 1955 bytes result sent to driver +23/11/07 11:00:27.324 Executor task launch worker for task 1754.0 in stage 0.0 (TID 1754) INFO Executor: Finished task 1754.0 in stage 0.0 (TID 1754). 1955 bytes result sent to driver +23/11/07 11:00:27.367 task-result-getter-3 INFO TaskSetManager: Finished task 1765.0 in stage 0.0 (TID 1765) in 86 ms on localhost (executor driver) (1763/1824) +23/11/07 11:00:27.367 task-result-getter-0 INFO TaskSetManager: Finished task 1744.0 in stage 0.0 (TID 1744) in 142 ms on localhost (executor driver) (1764/1824) +23/11/07 11:00:27.367 task-result-getter-2 INFO TaskSetManager: Finished task 1783.0 in stage 0.0 (TID 1783) in 73 ms on localhost (executor driver) (1765/1824) +23/11/07 11:00:27.367 task-result-getter-3 INFO TaskSetManager: Finished task 1781.0 in stage 0.0 (TID 1781) in 75 ms on localhost (executor driver) (1766/1824) +23/11/07 11:00:27.367 task-result-getter-1 INFO TaskSetManager: Finished task 1736.0 in stage 0.0 (TID 1736) in 145 ms on localhost (executor driver) (1767/1824) +23/11/07 11:00:27.386 Executor task launch worker for task 1821.0 in stage 0.0 (TID 1821) INFO Executor: Finished task 1821.0 in stage 0.0 (TID 1821). 1955 bytes result sent to driver +23/11/07 11:00:27.386 task-result-getter-0 INFO TaskSetManager: Finished task 1821.0 in stage 0.0 (TID 1821) in 65 ms on localhost (executor driver) (1817/1824) +23/11/07 11:00:27.386 Executor task launch worker for task 1822.0 in stage 0.0 (TID 1822) INFO Executor: Finished task 1822.0 in stage 0.0 (TID 1822). 1955 bytes result sent to driver +23/11/07 11:00:27.387 task-result-getter-2 INFO TaskSetManager: Finished task 1822.0 in stage 0.0 (TID 1822) in 65 ms on localhost (executor driver) (1818/1824) +23/11/07 11:00:27.387 task-result-getter-3 INFO TaskSetManager: Finished task 1811.0 in stage 0.0 (TID 1811) in 72 ms on localhost (executor driver) (1819/1824) +23/11/07 11:00:27.387 Executor task launch worker for task 1803.0 in stage 0.0 (TID 1803) INFO Executor: Finished task 1803.0 in stage 0.0 (TID 1803). 1955 bytes result sent to driver +23/11/07 11:00:27.387 task-result-getter-1 INFO TaskSetManager: Finished task 1798.0 in stage 0.0 (TID 1798) in 81 ms on localhost (executor driver) (1820/1824) +23/11/07 11:00:27.387 task-result-getter-0 INFO TaskSetManager: Finished task 1823.0 in stage 0.0 (TID 1823) in 65 ms on localhost (executor driver) (1821/1824) +23/11/07 11:00:27.387 task-result-getter-2 INFO TaskSetManager: Finished task 1813.0 in stage 0.0 (TID 1813) in 71 ms on localhost (executor driver) (1822/1824) +23/11/07 11:00:27.387 task-result-getter-3 INFO TaskSetManager: Finished task 1803.0 in stage 0.0 (TID 1803) in 77 ms on localhost (executor driver) (1823/1824) +23/11/07 11:00:27.387 Executor task launch worker for task 1812.0 in stage 0.0 (TID 1812) INFO Executor: Finished task 1812.0 in stage 0.0 (TID 1812). 1955 bytes result sent to driver +23/11/07 11:00:27.387 task-result-getter-1 INFO TaskSetManager: Finished task 1812.0 in stage 0.0 (TID 1812) in 72 ms on localhost (executor driver) (1824/1824) +23/11/07 11:00:27.388 task-result-getter-1 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool +23/11/07 11:00:27.389 dag-scheduler-event-loop INFO DAGScheduler: ResultStage 0 (parquet at :23) finished in 3.376 s +23/11/07 11:00:27.391 dag-scheduler-event-loop INFO DAGScheduler: Job 0 is finished. Cancelling potential speculative or zombie tasks for this job +23/11/07 11:00:27.391 dag-scheduler-event-loop INFO TaskSchedulerImpl: Killing all running tasks in stage 0: Stage finished +23/11/07 11:00:27.393 main INFO DAGScheduler: Job 0 finished: parquet at :23, took 3.424451 s +23/11/07 11:00:27.429 main INFO InMemoryFileIndex: It took 3774 ms to list leaf files for 1 paths. +23/11/07 11:00:27.676 main INFO SparkContext: Starting job: parquet at :23 +23/11/07 11:00:27.676 dag-scheduler-event-loop INFO DAGScheduler: Got job 1 (parquet at :23) with 1 output partitions +23/11/07 11:00:27.677 dag-scheduler-event-loop INFO DAGScheduler: Final stage: ResultStage 1 (parquet at :23) +23/11/07 11:00:27.677 dag-scheduler-event-loop INFO DAGScheduler: Parents of final stage: List() +23/11/07 11:00:27.677 dag-scheduler-event-loop INFO DAGScheduler: Missing parents: List() +23/11/07 11:00:27.678 dag-scheduler-event-loop INFO DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[4] at parquet at :23), which has no missing parents +23/11/07 11:00:27.688 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 164.4 KiB, free 365.9 MiB) +23/11/07 11:00:27.689 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 49.8 KiB, free 365.9 MiB) +23/11/07 11:00:27.690 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on localhost:34199 (size: 49.8 KiB, free: 366.2 MiB) +23/11/07 11:00:27.690 dag-scheduler-event-loop INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1513 +23/11/07 11:00:27.690 dag-scheduler-event-loop INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[4] at parquet at :23) (first 15 tasks are for partitions Vector(0)) +23/11/07 11:00:27.690 dag-scheduler-event-loop INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks resource profile 0 +23/11/07 11:00:27.691 dispatcher-event-loop-38 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1824) (localhost, executor driver, partition 0, PROCESS_LOCAL, 4714 bytes) taskResourceAssignments Map() +23/11/07 11:00:27.692 Executor task launch worker for task 0.0 in stage 1.0 (TID 1824) INFO Executor: Running task 0.0 in stage 1.0 (TID 1824) +23/11/07 11:00:27.893 Executor task launch worker for task 0.0 in stage 1.0 (TID 1824) INFO Executor: Finished task 0.0 in stage 1.0 (TID 1824). 4029 bytes result sent to driver +23/11/07 11:00:27.893 task-result-getter-0 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 1824) in 202 ms on localhost (executor driver) (1/1) +23/11/07 11:00:27.893 task-result-getter-0 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool +23/11/07 11:00:27.894 dag-scheduler-event-loop INFO DAGScheduler: ResultStage 1 (parquet at :23) finished in 0.214 s +23/11/07 11:00:27.894 dag-scheduler-event-loop INFO DAGScheduler: Job 1 is finished. Cancelling potential speculative or zombie tasks for this job +23/11/07 11:00:27.894 dag-scheduler-event-loop INFO TaskSchedulerImpl: Killing all running tasks in stage 1: Stage finished +23/11/07 11:00:27.894 main INFO DAGScheduler: Job 1 finished: parquet at :23, took 0.218049 s +23/11/07 11:00:28.642 main INFO DataSourceStrategy: Pruning directories with: +23/11/07 11:00:28.643 main INFO FileSourceStrategy: Pushed Filters: +23/11/07 11:00:28.644 main INFO FileSourceStrategy: Post-Scan Filters: +23/11/07 11:00:28.645 main INFO FileSourceStrategy: Output Data Schema: struct +23/11/07 11:00:28.714 main WARN GpuOverrides: +!Exec cannot run on GPU because the Exec CollectLimitExec has been disabled, and is disabled by default because Collect Limit replacement can be slower on the GPU, if huge number of rows in a batch it could help by limiting the number of rows transferred from GPU to CPU. Set spark.rapids.sql.exec.CollectLimitExec to true if you wish to enable it + @Partitioning could run on GPU + !Exec cannot run on GPU because not all expressions can be replaced + @Expression cast(ws_sold_time_sk#0 as string) AS ws_sold_time_sk#78 could run on GPU + @Expression cast(ws_sold_time_sk#0 as string) could run on GPU + @Expression ws_sold_time_sk#0 could run on GPU + @Expression cast(ws_ship_date_sk#1 as string) AS ws_ship_date_sk#79 could run on GPU + @Expression cast(ws_ship_date_sk#1 as string) could run on GPU + @Expression ws_ship_date_sk#1 could run on GPU + @Expression format_number(cast(ws_ship_date_sk#1 as double), 2) AS formatted_number#80 could run on GPU + !Expression format_number(cast(ws_ship_date_sk#1 as double), 2) cannot run on GPU because format_number with floating point types on the GPU returns results that have a different precision than the default results of Spark. To enable this operation on the GPU, set spark.rapids.sql.formatNumberFloat.enabled to true. + @Expression cast(ws_ship_date_sk#1 as double) could run on GPU + @Expression ws_ship_date_sk#1 could run on GPU + @Expression 2 could run on GPU + *Exec will run on GPU + +23/11/07 11:00:28.731 main INFO GpuOverrides: Plan conversion to the GPU took 67.30 ms +23/11/07 11:00:28.762 main INFO GpuOverrides: GPU plan transition optimization took 14.49 ms +23/11/07 11:00:28.968 main INFO CodeGenerator: Code generated in 135.771897 ms +23/11/07 11:00:28.982 main INFO GpuFileSourceScanExec: Planning scan with bin packing, max size: 128044734 bytes, open cost is considered as scanning 4194304 bytes. +23/11/07 11:00:29.017 main INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 475.1 KiB, free 365.4 MiB) +23/11/07 11:00:29.023 main INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 46.0 KiB, free 365.4 MiB) +23/11/07 11:00:29.024 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on localhost:34199 (size: 46.0 KiB, free: 366.2 MiB) +23/11/07 11:00:29.024 main INFO SparkContext: Created broadcast 2 from internalDoExecuteColumnar at GpuExec.scala:349 +23/11/07 11:00:29.058 main INFO SparkContext: Starting job: show at :31 +23/11/07 11:00:29.058 dag-scheduler-event-loop INFO DAGScheduler: Got job 2 (show at :31) with 1 output partitions +23/11/07 11:00:29.058 dag-scheduler-event-loop INFO DAGScheduler: Final stage: ResultStage 2 (show at :31) +23/11/07 11:00:29.058 dag-scheduler-event-loop INFO DAGScheduler: Parents of final stage: List() +23/11/07 11:00:29.059 dag-scheduler-event-loop INFO DAGScheduler: Missing parents: List() +23/11/07 11:00:29.059 dag-scheduler-event-loop INFO DAGScheduler: Submitting ResultStage 2 (MapPartitionsRDD[10] at show at :31), which has no missing parents +23/11/07 11:00:29.098 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_3 stored as values in memory (estimated size 21.6 KiB, free 365.4 MiB) +23/11/07 11:00:29.099 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated size 10.0 KiB, free 365.3 MiB) +23/11/07 11:00:29.100 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Added broadcast_3_piece0 in memory on localhost:34199 (size: 10.0 KiB, free: 366.1 MiB) +23/11/07 11:00:29.100 dag-scheduler-event-loop INFO SparkContext: Created broadcast 3 from broadcast at DAGScheduler.scala:1513 +23/11/07 11:00:29.100 dag-scheduler-event-loop INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 2 (MapPartitionsRDD[10] at show at :31) (first 15 tasks are for partitions Vector(0)) +23/11/07 11:00:29.100 dag-scheduler-event-loop INFO TaskSchedulerImpl: Adding task set 2.0 with 1 tasks resource profile 0 +23/11/07 11:00:29.103 dispatcher-event-loop-51 INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 1825) (localhost, executor driver, partition 0, PROCESS_LOCAL, 11104 bytes) taskResourceAssignments Map() +23/11/07 11:00:29.104 Executor task launch worker for task 0.0 in stage 2.0 (TID 1825) INFO Executor: Running task 0.0 in stage 2.0 (TID 1825) +23/11/07 11:00:29.151 Executor task launch worker for task 0.0 in stage 2.0 (TID 1825) INFO CodeGenerator: Code generated in 16.566694 ms +23/11/07 11:00:29.162 Executor task launch worker for task 0.0 in stage 2.0 (TID 1825) INFO GpuParquetMultiFilePartitionReaderFactory: Using the coalesce multi-file Parquet reader, files: file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451181/part-00132-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451546/part-00112-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452277/part-00059-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451491/part-00006-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452641/part-00061-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451492/part-00001-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452221/part-00119-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451162/part-00003-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452614/part-00119-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451170/part-00105-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452608/part-00020-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451503/part-00109-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452258/part-00027-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451155/part-00063-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452595/part-00047-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451126/part-00125-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451138/part-00034-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452260/part-00011-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452261/part-00003-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451148/part-00071-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451908/part-00059-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452587/part-00095-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451530/part-00036-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451160/part-00036-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451500/part-00097-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451145/part-00046-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452244/part-00103-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet task attemptid: 1825 +23/11/07 11:00:29.262 Executor task launch worker for task 0.0 in stage 2.0 (TID 1825) WARN MultiFileReaderThreadPool: Configuring the file reader thread pool with a max of 64 threads instead of spark.rapids.sql.multiThreadedRead.numThreads = 20 +23/11/07 11:00:29.438 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Removed broadcast_0_piece0 on localhost:34199 in memory (size: 49.6 KiB, free: 366.2 MiB) +23/11/07 11:00:29.442 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Removed broadcast_1_piece0 on localhost:34199 in memory (size: 49.8 KiB, free: 366.2 MiB) +23/11/07 11:00:29.489 Executor task launch worker for task 0.0 in stage 2.0 (TID 1825) INFO Executor: Finished task 0.0 in stage 2.0 (TID 1825). 3072 bytes result sent to driver +23/11/07 11:00:29.490 task-result-getter-2 INFO TaskSetManager: Finished task 0.0 in stage 2.0 (TID 1825) in 389 ms on localhost (executor driver) (1/1) +23/11/07 11:00:29.490 task-result-getter-2 INFO TaskSchedulerImpl: Removed TaskSet 2.0, whose tasks have all completed, from pool +23/11/07 11:00:29.491 dag-scheduler-event-loop INFO DAGScheduler: ResultStage 2 (show at :31) finished in 0.429 s +23/11/07 11:00:29.491 dag-scheduler-event-loop INFO DAGScheduler: Job 2 is finished. Cancelling potential speculative or zombie tasks for this job +23/11/07 11:00:29.491 dag-scheduler-event-loop INFO TaskSchedulerImpl: Killing all running tasks in stage 2: Stage finished +23/11/07 11:00:29.491 main INFO DAGScheduler: Job 2 finished: show at :31, took 0.433102 s +23/11/07 11:00:29.516 main INFO CodeGenerator: Code generated in 11.518671 ms +23/11/07 11:00:29.544 main INFO DataSourceStrategy: Pruning directories with: +23/11/07 11:00:29.545 main INFO FileSourceStrategy: Pushed Filters: +23/11/07 11:00:29.545 main INFO FileSourceStrategy: Post-Scan Filters: +23/11/07 11:00:29.545 main INFO FileSourceStrategy: Output Data Schema: struct +23/11/07 11:00:29.550 main WARN GpuOverrides: +!Exec cannot run on GPU because the Exec CollectLimitExec has been disabled, and is disabled by default because Collect Limit replacement can be slower on the GPU, if huge number of rows in a batch it could help by limiting the number of rows transferred from GPU to CPU. Set spark.rapids.sql.exec.CollectLimitExec to true if you wish to enable it + @Partitioning could run on GPU + !Exec cannot run on GPU because not all expressions can be replaced + @Expression cast(ws_sold_time_sk#0 as string) AS ws_sold_time_sk#94 could run on GPU + @Expression cast(ws_sold_time_sk#0 as string) could run on GPU + @Expression ws_sold_time_sk#0 could run on GPU + @Expression hex(cast(ws_sold_time_sk#0 as bigint)) AS hex_value#95 could run on GPU + ! hex(cast(ws_sold_time_sk#0 as bigint)) cannot run on GPU because GPU does not currently support the operator class org.apache.spark.sql.catalyst.expressions.Hex + @Expression cast(ws_sold_time_sk#0 as bigint) could run on GPU + @Expression ws_sold_time_sk#0 could run on GPU + *Exec will run on GPU + +23/11/07 11:00:29.550 main INFO GpuOverrides: Plan conversion to the GPU took 4.76 ms +23/11/07 11:00:29.551 main INFO GpuOverrides: GPU plan transition optimization took 0.42 ms +23/11/07 11:00:29.575 main INFO CodeGenerator: Code generated in 14.419084 ms +23/11/07 11:00:29.580 main INFO GpuFileSourceScanExec: Planning scan with bin packing, max size: 128044734 bytes, open cost is considered as scanning 4194304 bytes. +23/11/07 11:00:29.589 main INFO MemoryStore: Block broadcast_4 stored as values in memory (estimated size 475.1 KiB, free 365.3 MiB) +23/11/07 11:00:29.594 main INFO MemoryStore: Block broadcast_4_piece0 stored as bytes in memory (estimated size 46.0 KiB, free 365.3 MiB) +23/11/07 11:00:29.595 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Added broadcast_4_piece0 in memory on localhost:34199 (size: 46.0 KiB, free: 366.2 MiB) +23/11/07 11:00:29.595 main INFO SparkContext: Created broadcast 4 from internalDoExecuteColumnar at GpuExec.scala:349 +23/11/07 11:00:29.604 main INFO SparkContext: Starting job: show at :36 +23/11/07 11:00:29.605 dag-scheduler-event-loop INFO DAGScheduler: Got job 3 (show at :36) with 1 output partitions +23/11/07 11:00:29.605 dag-scheduler-event-loop INFO DAGScheduler: Final stage: ResultStage 3 (show at :36) +23/11/07 11:00:29.605 dag-scheduler-event-loop INFO DAGScheduler: Parents of final stage: List() +23/11/07 11:00:29.605 dag-scheduler-event-loop INFO DAGScheduler: Missing parents: List() +23/11/07 11:00:29.606 dag-scheduler-event-loop INFO DAGScheduler: Submitting ResultStage 3 (MapPartitionsRDD[16] at show at :36), which has no missing parents +23/11/07 11:00:29.611 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_5 stored as values in memory (estimated size 20.2 KiB, free 365.2 MiB) +23/11/07 11:00:29.612 dag-scheduler-event-loop INFO MemoryStore: Block broadcast_5_piece0 stored as bytes in memory (estimated size 9.5 KiB, free 365.2 MiB) +23/11/07 11:00:29.612 dispatcher-BlockManagerMaster INFO BlockManagerInfo: Added broadcast_5_piece0 in memory on localhost:34199 (size: 9.5 KiB, free: 366.2 MiB) +23/11/07 11:00:29.613 dag-scheduler-event-loop INFO SparkContext: Created broadcast 5 from broadcast at DAGScheduler.scala:1513 +23/11/07 11:00:29.613 dag-scheduler-event-loop INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 3 (MapPartitionsRDD[16] at show at :36) (first 15 tasks are for partitions Vector(0)) +23/11/07 11:00:29.613 dag-scheduler-event-loop INFO TaskSchedulerImpl: Adding task set 3.0 with 1 tasks resource profile 0 +23/11/07 11:00:29.614 dispatcher-event-loop-43 INFO TaskSetManager: Starting task 0.0 in stage 3.0 (TID 1826) (localhost, executor driver, partition 0, PROCESS_LOCAL, 11104 bytes) taskResourceAssignments Map() +23/11/07 11:00:29.614 Executor task launch worker for task 0.0 in stage 3.0 (TID 1826) INFO Executor: Running task 0.0 in stage 3.0 (TID 1826) +23/11/07 11:00:29.630 Executor task launch worker for task 0.0 in stage 3.0 (TID 1826) INFO CodeGenerator: Code generated in 11.002146 ms +23/11/07 11:00:29.633 Executor task launch worker for task 0.0 in stage 3.0 (TID 1826) INFO GpuParquetMultiFilePartitionReaderFactory: Using the coalesce multi-file Parquet reader, files: file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451181/part-00132-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451546/part-00112-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452277/part-00059-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451491/part-00006-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452641/part-00061-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451492/part-00001-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452221/part-00119-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451162/part-00003-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452614/part-00119-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451170/part-00105-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452608/part-00020-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451503/part-00109-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452258/part-00027-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451155/part-00063-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452595/part-00047-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451126/part-00125-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451138/part-00034-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452260/part-00011-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452261/part-00003-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451148/part-00071-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451908/part-00059-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452587/part-00095-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451530/part-00036-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451160/part-00036-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451500/part-00097-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2451145/part-00046-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet,file:/home/test/data_parquet_sf10_decimal/parquet_sf10_decimal/web_sales/ws_sold_date_sk=2452244/part-00103-1694ffb1-67ca-4c7a-864e-dea8f457a824.c000.snappy.parquet task attemptid: 1826 +23/11/07 11:00:29.682 Executor task launch worker for task 0.0 in stage 3.0 (TID 1826) INFO Executor: Finished task 0.0 in stage 3.0 (TID 1826). 2860 bytes result sent to driver +23/11/07 11:00:29.682 task-result-getter-3 INFO TaskSetManager: Finished task 0.0 in stage 3.0 (TID 1826) in 69 ms on localhost (executor driver) (1/1) +23/11/07 11:00:29.683 task-result-getter-3 INFO TaskSchedulerImpl: Removed TaskSet 3.0, whose tasks have all completed, from pool +23/11/07 11:00:29.683 dag-scheduler-event-loop INFO DAGScheduler: ResultStage 3 (show at :36) finished in 0.076 s +23/11/07 11:00:29.683 dag-scheduler-event-loop INFO DAGScheduler: Job 3 is finished. Cancelling potential speculative or zombie tasks for this job +23/11/07 11:00:29.683 dag-scheduler-event-loop INFO TaskSchedulerImpl: Killing all running tasks in stage 3: Stage finished +23/11/07 11:00:29.684 main INFO DAGScheduler: Job 3 finished: show at :36, took 0.078834 s +23/11/07 11:00:29.697 main INFO CodeGenerator: Code generated in 9.866734 ms diff --git a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/ApplicationInfoSuite.scala b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/ApplicationInfoSuite.scala index dac22336b..f3d382e56 100644 --- a/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/ApplicationInfoSuite.scala +++ b/core/src/test/scala/com/nvidia/spark/rapids/tool/profiling/ApplicationInfoSuite.scala @@ -654,6 +654,32 @@ class ApplicationInfoSuite extends FunSuite with Logging { } } + test("test reading driver log") { + val driverlog = s"$logDir/driverlog" + TrampolineUtil.withTempDir { tempDir => + val appArgs = new ProfileArgs(Array( + "--driverlog", driverlog, + "--output-directory", + tempDir.getAbsolutePath)) + val (exit, _) = ProfileMain.mainInternal(appArgs) + assert(exit == 0) + val tempSubDir = new File(tempDir, s"${Profiler.SUBDIR}/driver") + val dotDirs = ToolTestUtils.listFilesMatching(tempSubDir, { f => + f.endsWith(".csv") + }) + assert(dotDirs.length === 1) + for (file <- dotDirs) { + assert(file.getAbsolutePath.endsWith(".csv")) + val df = sparkSession.read.option("header", "true").csv(file.getAbsolutePath) + val res = df.collect() + assert(res.nonEmpty) + val unsupportedHex = df.filter(df("operatorName") === "Hex").count() + assert(unsupportedHex == 1) + assert(res.size == 3) + } + } + } + test("test gds-ucx-parameters") { val apps: ArrayBuffer[ApplicationInfo] = ArrayBuffer[ApplicationInfo]() val appArgs =

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