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FlatMapGroupsWithStateExec Unary Physical Operator

FlatMapGroupsWithStateExec is a unary physical operator that represents FlatMapGroupsWithState logical operator at execution time.

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 online book.

FlatMapGroupsWithStateExec is an ObjectProducerExec physical operator and so produces a single output object.

Tip

Read up on ObjectProducerExec physical operator in The Internals of Spark SQL online book.

Note

FlatMapGroupsWithStateExec is given an OutputMode when created, but it does not seem to be used at all. Check out the question What's the purpose of OutputMode in flatMapGroupsWithState? How/where is it used? on StackOverflow.

Creating Instance

FlatMapGroupsWithStateExec takes the following to be created:

  • User-defined state function that is applied to every group (of type (Any, Iterator[Any], LogicalGroupState[Any]) => Iterator[Any])
  • Deserializer expression for keys
  • Deserializer expression for values
  • Grouping attributes (as used for grouping in KeyValueGroupedDataset for mapGroupsWithState or flatMapGroupsWithState operators)
  • Data attributes
  • Output object attribute (that is the reference to the single object field this operator outputs)
  • Optional StatefulOperatorStateInfo
  • State encoder (ExpressionEncoder[Any])
  • State format version
  • OutputMode
  • GroupStateTimeout
  • Optional Batch Processing Time
  • Optional Event-Time Watermark
  • Child physical operator

FlatMapGroupsWithStateExec is created when FlatMapGroupsWithStateStrategy execution planning strategy is executed (and plans a FlatMapGroupsWithState logical operator for execution).

Executing Physical Operator

doExecute(): RDD[InternalRow]

doExecute first initializes the metrics (which happens on the driver).

doExecute then requests the child physical operator to execute (and generate an RDD[InternalRow]).

doExecute uses StateStoreOps to create a StateStoreRDD with a storeUpdateFunction that does the following (for a partition):

  1. Creates an InputProcessor for a given StateStore

  2. (only when the GroupStateTimeout is EventTimeTimeout) Filters out late data based on the event-time watermark, i.e. rows from a given Iterator[InternalRow] that are older than the event-time watermark are excluded from the steps that follow

  3. Requests the InputProcessor to create an iterator of a new data processed from the (possibly filtered) iterator

  4. Requests the InputProcessor to create an iterator of a timed-out state data

  5. Creates an iterator by concatenating the above iterators (with the new data processed first)

  6. In the end, creates a CompletionIterator that executes a completion function (completionFunction) after it has successfully iterated through all the elements (i.e. when a client has consumed all the rows). The completion method requests the given StateStore to commit changes followed by setting the store-specific metrics

doExecute is part of Spark SQL's SparkPlan abstraction.

Performance Metrics

FlatMapGroupsWithStateExec uses the performance metrics of StateStoreWriter.

FlatMapGroupsWithStateExec in web UI (Details for Query)

StateStoreWriter

FlatMapGroupsWithStateExec is a stateful physical operator that can write to a state store (and MicroBatchExecution requests whether to run another batch or not based on the GroupStateTimeout).

FlatMapGroupsWithStateExec uses the GroupStateTimeout (and possibly the updated metadata) when asked whether to run another batch or not (when MicroBatchExecution is requested to construct the next streaming micro-batch when requested to run the activated streaming query).

Streaming Event-Time Watermark Support

FlatMapGroupsWithStateExec is a physical operator that supports streaming event-time watermark.

FlatMapGroupsWithStateExec is given the optional event time watermark when created.

The event-time watermark is initially undefined (None) when planned for execution (in FlatMapGroupsWithStateStrategy execution planning strategy).

Note

FlatMapGroupsWithStateStrategy converts FlatMapGroupsWithState unary logical operator to FlatMapGroupsWithStateExec physical operator with undefined StatefulOperatorStateInfo, batchTimestampMs, and eventTimeWatermark.

The event-time watermark (with the StatefulOperatorStateInfo and the batchTimestampMs) is only defined to the current event-time watermark of the given OffsetSeqMetadata when IncrementalExecution query execution pipeline is requested to apply the state preparation rule (as part of the preparations rules).

Note

The preparations rules are executed (applied to a physical query plan) at the executedPlan phase of Structured Query Execution Pipeline to generate an optimized physical query plan ready for execution).

Read up on Structured Query Execution Pipeline in The Internals of Spark SQL online book.

IncrementalExecution is used as the lastExecution of the available streaming query execution engines. It is created in the queryPlanning phase (of the MicroBatchExecution and ContinuousExecution execution engines) based on the current OffsetSeqMetadata.

Note

The optional event-time watermark can only be defined when the state preparation rule is executed which is at the executedPlan phase of Structured Query Execution Pipeline which is also part of the queryPlanning phase.

StateManager

stateManager: StateManager

While being created, FlatMapGroupsWithStateExec creates a StateManager (with the state encoder and the isTimeoutEnabled flag).

A StateManager is created per state format version that is given while creating a FlatMapGroupsWithStateExec (to choose between the available implementations).

The state format version is controlled by spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion internal configuration property.

The StateManager is used exclusively when FlatMapGroupsWithStateExec physical operator is executed for the following:

keyExpressions Method

keyExpressions: Seq[Attribute]

keyExpressions simply returns the grouping attributes.

keyExpressions is part of the WatermarkSupport abstraction.

Checking Out Whether Last Batch Execution Requires Another Non-Data Batch or Not

shouldRunAnotherBatch(
  newMetadata: OffsetSeqMetadata): Boolean

shouldRunAnotherBatch uses the GroupStateTimeout as follows:

shouldRunAnotherBatch is part of the StateStoreWriter abstraction.

Internal Properties

isTimeoutEnabled Flag

Flag that says whether the GroupStateTimeout is not NoTimeout

Used when:

watermarkPresent Flag

Flag that says whether the child physical operator has a watermark attribute (among the output attributes).

Used when InputProcessor is requested to callFunctionAndUpdateState

Logging

Enable ALL logging level for org.apache.spark.sql.execution.streaming.FlatMapGroupsWithStateExec logger to see what happens inside.

Add the following line to conf/log4j.properties:

log4j.logger.org.apache.spark.sql.execution.streaming.FlatMapGroupsWithStateExec=ALL

Refer to Logging.


Last update: 2021-02-07