spark-sql-streaming-StateStoreSaveExec.adoc

StateStoreSaveExec Unary Physical Operator — Saving State of Streaming Aggregates

StateStoreSaveExec is a unary physical operator that writes a streaming state to a state store with support for streaming watermark.

Note	A unary physical operator (UnaryExecNode) is a physical operator with a single child physical operator. Read up on UnaryExecNode (and physical operators in general) in The Internals of Spark SQL book.

StateStoreSaveExec is created exclusively when StatefulAggregationStrategy execution planning strategy is requested to plan a streaming aggregation for execution (Aggregate logical operators in the logical plan of a streaming query).

Figure 1. StateStoreSaveExec and StatefulAggregationStrategy

The optional properties, i.e. the StatefulOperatorStateInfo, the output mode, and the event-time watermark, are initially undefined when StateStoreSaveExec is created. StateStoreSaveExec is updated to hold execution-specific configuration when IncrementalExecution is requested to prepare the logical plan (of a streaming query) for execution (when the state preparation rule is executed).

Figure 2. StateStoreSaveExec and IncrementalExecution

Note	Unlike StateStoreRestoreExec operator, StateStoreSaveExec takes output mode and event time watermark when created.

When executed, StateStoreSaveExec creates a StateStoreRDD to map over partitions with storeUpdateFunction that manages the StateStore.

Figure 3. StateStoreSaveExec creates StateStoreRDD

Figure 4. StateStoreSaveExec and StateStoreRDD (after streamingBatch.toRdd.count)

Note	The number of partitions of StateStoreRDD (and hence the number of Spark tasks) is what was defined for the child physical plan. There will be that many StateStores as there are partitions in StateStoreRDD.

Note	StateStoreSaveExec behaves differently per output mode.

When executed, StateStoreSaveExec executes the child physical operator and creates a StateStoreRDD (with storeUpdateFunction specific to the output mode).

The output schema of StateStoreSaveExec is exactly the child's output schema.

The output partitioning of StateStoreSaveExec is exactly the child's output partitioning.

StateStoreRestoreExec uses a StreamingAggregationStateManager (that is created for the keyExpressions, the output of the child physical operator and the stateFormatVersion).

Tip	Enable ALL logging level for org.apache.spark.sql.execution.streaming.StateStoreSaveExec to see what happens inside. Add the following line to conf/log4j.properties: log4j.logger.org.apache.spark.sql.execution.streaming.StateStoreSaveExec=ALL Refer to Logging.

Demo: StateStoreSaveExec Operator in Physical Plan of Streaming Aggregation (Dataset.groupBy Operator)

// START: Only for easier debugging
// The state is then only for one partition
// which should make monitoring easier
val numShufflePartitions = 1
import org.apache.spark.sql.internal.SQLConf.SHUFFLE_PARTITIONS
spark.sessionState.conf.setConf(SHUFFLE_PARTITIONS, numShufflePartitions)

assert(spark.sessionState.conf.numShufflePartitions == numShufflePartitions)
// END: Only for easier debugging

val counts = spark
  .readStream
  .format("rate")
  .load
  .groupBy(window($"timestamp", "5 seconds") as "group")
  .agg(count("value") as "value_count") // <-- creates an Aggregate logical operator
  .orderBy("group")  // <-- makes for easier checking
scala> counts.explain
== Physical Plan ==
*(5) Sort [group#5 ASC NULLS FIRST], true, 0
+- Exchange rangepartitioning(group#5 ASC NULLS FIRST, 1)
   +- *(4) HashAggregate(keys=[window#11], functions=[count(value#1L)])
      +- StateStoreSave [window#11], state info [ checkpoint = <unknown>, runId = 3c4d9c60-5fda-4053-aeef-4ddd2b485605, opId = 0, ver = 0, numPartitions = 1], Append, 0, 2
         +- *(3) HashAggregate(keys=[window#11], functions=[merge_count(value#1L)])
            +- StateStoreRestore [window#11], state info [ checkpoint = <unknown>, runId = 3c4d9c60-5fda-4053-aeef-4ddd2b485605, opId = 0, ver = 0, numPartitions = 1], 2
               +- *(2) HashAggregate(keys=[window#11], functions=[merge_count(value#1L)])
                  +- Exchange hashpartitioning(window#11, 1)
                     +- *(1) HashAggregate(keys=[window#11], functions=[partial_count(value#1L)])
                        +- *(1) Project [named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) as double) = (cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) THEN (CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) + 1) ELSE CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) END + 0) - 1) * 5000000) + 0), LongType, TimestampType), end, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) as double) = (cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) THEN (CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) + 1) ELSE CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / 5000000.0)) END + 0) - 1) * 5000000) + 5000000), LongType, TimestampType)) AS window#11, value#1L]
                           +- *(1) Filter isnotnull(timestamp#0)
                              +- StreamingRelation rate, [timestamp#0, value#1L]

// Start the query and hence execute StateStoreSaveExec
import org.apache.spark.sql.streaming.OutputMode
val queryName = "rate2memory"
val checkpointLocation = s"/tmp/checkpoint-$queryName"
val query = counts
  .writeStream
  .format("memory")
  .queryName(queryName)
  .option("checkpointLocation", checkpointLocation)
  .outputMode(OutputMode.Complete)
  .start

// Give the streaming query a moment (one micro-batch)
// So lastExecution is available for the checkpointLocation
import scala.concurrent.duration._
query.awaitTermination(1.second.toMillis)

import org.apache.spark.sql.execution.streaming.StreamingQueryWrapper
val lastExecution = query
  .asInstanceOf[StreamingQueryWrapper]
  .streamingQuery
  .lastExecution
val logicalPlan = lastExecution.logical
scala> println(logicalPlan.numberedTreeString)
00 Sort [group#5 ASC NULLS FIRST], true
01 +- Aggregate [window#11], [window#11 AS group#5, count(value#1L) AS value_count#10L]
02    +- Filter isnotnull(timestamp#0)
03       +- Project [named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) as double) = (cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) THEN (CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) END + cast(0 as bigint)) - cast(1 as bigint)) * 5000000) + 0), LongType, TimestampType), end, precisetimestampconversion((((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) as double) = (cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) THEN (CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) + cast(1 as bigint)) ELSE CEIL((cast((precisetimestampconversion(timestamp#0, TimestampType, LongType) - 0) as double) / cast(5000000 as double))) END + cast(0 as bigint)) - cast(1 as bigint)) * 5000000) + 0) + 5000000), LongType, TimestampType)) AS window#11, timestamp#0, value#1L]
04          +- Project [timestamp#249 AS timestamp#0, value#250L AS value#1L]
05             +- Streaming RelationV2 rate[timestamp#249, value#250L]

Performance Metrics (SQLMetrics)

StateStoreSaveExec uses the performance metrics of the parent StateStoreWriter.

Key	Name (in UI)	Description
allUpdatesTimeMs
allRemovalsTimeMs
commitTimeMs
numOutputRows
numTotalStateRows		Number of the state keys in the state store Corresponds to numRowsTotal in stateOperators in StreamingQueryProgress (and is available as sq.lastProgress.stateOperators(0).numRowsTotal for 0th operator).
numUpdatedStateRows		Number of the state keys that were stored as updates in the state store in a trigger and for the keys in the result rows of the upstream physical operator. In Complete output mode, numUpdatedStateRows is the number of input rows (which should be exactly the number of output rows from the upstream operator) Caution FIXME In Append output mode, numUpdatedStateRows is the number of input rows with keys that have not expired yet (per required watermark) In Update output mode, numUpdatedStateRows is exactly number of output rows, i.e. the number of keys that have not expired yet if watermark has been defined at all (which is optional). Caution FIXME Note You can see the current value as numRowsUpdated attribute in stateOperators in StreamingQueryProgress (that is available as StreamingQuery.lastProgress.stateOperators(n).numRowsUpdated for nth operator).
stateMemory		Memory used by the StateStore

Figure 5. StateStoreSaveExec in web UI (Details for Query)

Creating StateStoreSaveExec Instance

StateStoreSaveExec takes the following to be created:

• Key expressions, i.e. Catalyst attributes for the grouping keys

• Execution-specific StatefulOperatorStateInfo (default: None)

• Execution-specific output mode (default: None)

• Execution-specific event-time watermark (default: None)

• Version of the state format (based on the spark.sql.streaming.aggregation.stateFormatVersion configuration property)

• Child physical operator (SparkPlan)

Executing Physical Operator (Generating RDD[InternalRow]) — doExecute Method

doExecute(): RDD[InternalRow]

Note	doExecute is part of SparkPlan Contract to generate the runtime representation of an physical operator as a distributed computation over internal binary rows on Apache Spark (i.e. RDD[InternalRow]).

Internally, doExecute initializes metrics.

Note	doExecute requires that the optional outputMode is at this point defined (that should have happened when IncrementalExecution had prepared a streaming aggregation for execution).

doExecute executes child physical operator and creates a StateStoreRDD with storeUpdateFunction that:

1. Generates an unsafe projection to access the key field (using keyExpressions and the output schema of child).

2. Branches off per output mode.

Table 1. doExecute’s Behaviour per Output Mode

Output Mode	doExecute’s Behaviour
Append	Note Append is the default output mode when unspecified. Note Append output mode requires that a streaming query defines event time watermark (using withWatermark operator) on the event time column that is used in aggregation (directly or using window function). Finds late (aggregate) rows from child physical operator (that have expired per watermark) Stores the late rows in the state store (and increments numUpdatedStateRows metric) Gets all the added (late) rows from the state store Creates an iterator that removes the late rows from the state store when requested the next row and in the end commits the state updates Note numUpdatedStateRows metric is the number of rows that…​FIXME Tip Refer to Demo: StateStoreSaveExec with Append Output Mode for an example of StateStoreSaveExec in Append output mode. Caution FIXME When is "Filtering state store on:" printed out? Caution FIXME Track numUpdatedStateRows metric Uses watermarkPredicateForData predicate to exclude matching rows and (like in Complete output mode) stores all the remaining rows in StateStore. (like in Complete output mode) While storing the rows, increments numUpdatedStateRows metric (for every row) and records the total time in allUpdatesTimeMs metric. Takes all the rows from StateStore and returns a NextIterator that: In getNext, finds the first row that matches watermarkPredicateForKeys predicate, removes it from StateStore, and returns it back. If no row was found, getNext also marks the iterator as finished. In close, records the time to iterate over all the rows in allRemovalsTimeMs metric, commits the updates to StateStore followed by recording the time in commitTimeMs metric and recording StateStore metrics.
Complete	Takes all UnsafeRow rows (from the parent iterator) Stores the rows by key in the state store eagerly (i.e. all rows that are available in the parent iterator before proceeding) Commits the state updates In the end, doExecute reads the key-row pairs from the state store and passes the rows along (i.e. to the following physical operator) The number of keys stored in the state store is recorded in numUpdatedStateRows metric. Note In Complete output mode numOutputRows metric is exactly numTotalStateRows metric. Tip Refer to Demo: StateStoreSaveExec with Complete Output Mode for an example of StateStoreSaveExec in Complete output mode. Stores all rows (as UnsafeRow) in StateStore. While storing the rows, increments numUpdatedStateRows metric (for every row) and records the total time in allUpdatesTimeMs metric. Records 0 in allRemovalsTimeMs metric. Commits the state updates to StateStore and records the time in commitTimeMs metric. Records StateStore metrics. In the end, takes all the rows stored in StateStore and increments numOutputRows metric.
Update	Returns an iterator that filters out late aggregate rows (per watermark if defined) and stores the "young" rows in the state store (one by one, i.e. every next). With no more rows available, that removes the late rows from the state store (all at once) and commits the state updates. Tip Refer to Demo: StateStoreSaveExec with Update Output Mode for an example of StateStoreSaveExec in Update output mode. Returns Iterator of rows that uses watermarkPredicateForData predicate to filter out late rows. In hasNext, when rows are no longer available: Records the total time to iterate over all the rows in allUpdatesTimeMs metric. removeKeysOlderThanWatermark and records the time in allRemovalsTimeMs metric. Commits the updates to StateStore and records the time in commitTimeMs metric. Records StateStore metrics. In next, stores a row in StateStore and increments numOutputRows and numUpdatedStateRows metrics.

doExecute reports a UnsupportedOperationException when executed with an invalid output mode.

Invalid output mode: [outputMode]