FlatMapGroupsWithStateExec Unary Physical Operator

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


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.

FlatMapGroupsWithState unary logical operator represents KeyValueGroupedDataset.mapGroupsWithState and KeyValueGroupedDataset.flatMapGroupsWithState operators in a logical query plan.

FlatMapGroupsWithStateExec is created exclusively when FlatMapGroupsWithStateStrategy execution planning strategy is requested to plan a FlatMapGroupsWithState logical operator for execution.

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

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.

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

Add the following line to conf/log4j.properties:


Refer to Logging.

Creating FlatMapGroupsWithStateExec 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])

  • Key deserializer expression

  • Value deserializer expression

  • 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)

  • StatefulOperatorStateInfo

  • State encoder (ExpressionEncoder[Any])

  • State format version

  • OutputMode

  • GroupStateTimeout

  • Batch Processing Time

  • Event-time watermark

  • Child physical operator

FlatMapGroupsWithStateExec initializes the internal properties.

Performance Metrics (SQLMetrics)

FlatMapGroupsWithStateExec uses the performance metrics of StateStoreWriter.

FlatMapGroupsWithStateExec webui query details.png
Figure 1. FlatMapGroupsWithStateExec in web UI (Details for Query)

FlatMapGroupsWithStateExec as 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).

FlatMapGroupsWithStateExec with Streaming Event-Time Watermark Support (WatermarkSupport)

FlatMapGroupsWithStateExec is given the optional event time watermark when created.

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


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).


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).

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.

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.

FlatMapGroupsWithStateExec and StateManager — stateManager Property

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 (default: 2).

StateManagerImplV2 is the default StateManager.

The StateManager is used exclusively when FlatMapGroupsWithStateExec physical operator is executed (to generate a recipe for a distributed computation as an RDD[InternalRow]) for the following:

keyExpressions Method

keyExpressions: Seq[Attribute]
keyExpressions is part of the WatermarkSupport Contract to…​FIXME.

keyExpressions simply returns the grouping attributes.

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

doExecute(): RDD[InternalRow]
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]).

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.

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

shouldRunAnotherBatch(newMetadata: OffsetSeqMetadata): Boolean
shouldRunAnotherBatch is part of the StateStoreWriter Contract to indicate whether MicroBatchExecution should run another non-data batch (based on the updated OffsetSeqMetadata with the current event-time watermark and the batch timestamp).

shouldRunAnotherBatch uses the GroupStateTimeout as follows:

Internal Properties

Name Description


Flag that says whether the GroupStateTimeout is not NoTimeout

Used when:






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

Used exclusively when InputProcessor is requested to callFunctionAndUpdateState

results matching ""

    No results matching ""