[SPARK-47363][SS] Initial State without state reader implementation for State API v2. #45467

jingz-db · 2024-03-11T21:21:21Z

What changes were proposed in this pull request?

This PR adds support for users to provide a Dataframe that can be used to instantiate state for the query in the first batch for arbitrary state API v2.

Note that populating the initial state will only happen for the first batch of the new streaming query. Trying to re-initialize state for the same grouping key will result in an error.

Why are the changes needed?

These changes are needed to support initial state. The changes are part of the work around adding new stateful streaming operator for arbitrary state mgmt that provides a bunch of new features listed in the SPIP JIRA here - https://issues.apache.org/jira/browse/SPARK-45939

Does this PR introduce any user-facing change?

Yes.
This PR introduces a new function:

def transformWithState(
      statefulProcessor: StatefulProcessorWithInitialState[K, V, U, S],
      timeoutMode: TimeoutMode,
      outputMode: OutputMode,
      initialState: KeyValueGroupedDataset[K, S]): Dataset[U]

How was this patch tested?

Unit tests in TransformWithStateWithInitialStateSuite

Was this patch authored or co-authored using generative AI tooling?

No

anishshri-db · 2024-03-13T17:30:03Z

sql/api/src/main/scala/org/apache/spark/sql/streaming/StatefulProcessor.scala

@@ -85,3 +85,21 @@ private[sql] trait StatefulProcessor[K, I, O] extends Serializable {
    statefulProcessorHandle
  }
 }
+
+/**
+ * Similar usage as StatefulProcessor. Represents the arbitrary stateful logic that needs to


Maybe reword this - Stateful processor with support for specifying initial state. Accepts a user-defined type as initial state to be initialized in the first batch. This can be used for starting a new streaming query with existing state from a previous streaming query ?

anishshri-db · 2024-03-13T17:34:15Z

sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala

@@ -665,7 +665,8 @@ class KeyValueGroupedDataset[K, V] private[sql](
      outputMode: OutputMode = OutputMode.Append()): Dataset[U] = {
    Dataset[U](
      sparkSession,
-      TransformWithState[K, V, U](
+      // The last K type is only to silence compiler error


Any way to avoid this ?

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

anishshri-db · 2024-03-13T17:38:40Z

Error seems relevant on the MIMA checks -

problems with Sql module: 
method transformWithState(org.apache.spark.sql.streaming.StatefulProcessorWithInitialState,org.apache.spark.sql.streaming.TimeoutMode,org.apache.spark.sql.streaming.OutputMode,org.apache.spark.sql.KeyValueGroupedDataset,org.apache.spark.sql.Encoder,org.apache.spark.sql.Encoder)org.apache.spark.sql.Dataset in class org.apache.spark.sql.KeyValueGroupedDataset does not have a correspondent in client version

we probably need to update the Connect variants as well

anishshri-db · 2024-03-13T17:40:31Z

...core/src/test/scala/org/apache/spark/sql/streaming/TransformWithStateInitialStateSuite.scala

+      key: String,
+      initialState: (String, Double)): Unit = {
+    val initStateVal = initialState._2
+    _valState.update(initStateVal)


Can we simulate an actual case class for initial state that stores list/map and/or iterator for list values/iterator for map key-values ?

HeartSaVioR · 2024-03-14T05:12:35Z

sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala

+   *                          the query in the first batch.
+   *
+   */
+  def transformWithState[U: Encoder, S: Encoder](


private[sql]

We want to defer exposing the API to public till we complete the work.

common/utils/src/main/resources/error/error-classes.json

anishshri-db · 2024-03-21T00:46:55Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

-      child.execute().mapPartitionsWithStateStore[InternalRow](
+    if (hasInitialState) {
+      val storeConf = new StateStoreConf(session.sqlContext.sessionState.conf)
+      val hadoopConfBroadcast = sparkContext.broadcast(


Why do we need to do this ?

I am not 100% percent sure, but this will distribute the read-only variable hadoopConf to all executors - similar as here:

spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/state/StateScanBuilder.scala

Lines 55 to 57 in 74a9c6c

// A Hadoop Configuration can be about 10 KB, which is pretty big, so broadcast it

private val hadoopConfBroadcast = session.sparkContext.broadcast(

new SerializableConfiguration(session.sessionState.newHadoopConf()))

Yeah there is a code comment. The practice seems to be that it's better to use broadcast rather than task serialization as it could be huge.

anishshri-db · 2024-03-21T17:41:38Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

-      child.execute().mapPartitionsWithStateStore[InternalRow](
+    if (hasInitialState) {
+      val storeConf = new StateStoreConf(session.sqlContext.sessionState.conf)
+      val hadoopConfBroadcast =


I mean this was only needed for the batch support part right ?

We will also need this for StateStore.get here: https://github.com/apache/spark/blob/40465b6760fb120c9cc3ac1a4ee42a82843f4bc5/sql/[…]ache/spark/sql/execution/streaming/TransformWithStateExec.scala

HeartSaVioR

Not yet reviewed the test suite, though I guess Anish has reviewed in detail.

HeartSaVioR · 2024-03-27T04:54:03Z

sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala

+        child = logicalPlan,
+        initialState.groupingAttributes,
+        initialState.dataAttributes,
+        initialState.queryExecution.logical


Shall we follow the practice we did in flatMapGroupsWithState for safeness sake?

initialState.queryExecution.analyzed

HeartSaVioR · 2024-03-27T04:59:29Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala

@@ -268,11 +268,13 @@ class IncrementalExecution(
        )

      case t: TransformWithStateExec =>
+        val hasInitialState = (isFirstBatch && t.hasInitialState)


I don't think we want to allow adding state in the middle of the query lifecycle. Here isFirstBatch does not mean batch ID = 0 but mean this is the first batch in this query run.

This should follow the above logic we did for FlatMapGroupsWithStateExec, currentBatchId == 0L.

Please let me know if this is a different functionality than we had in flatMapGroupsWithState.

HeartSaVioR · 2024-03-27T05:07:03Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

-      child.execute().mapPartitionsWithStateStore[InternalRow](
+    if (hasInitialState) {
+      val storeConf = new StateStoreConf(session.sqlContext.sessionState.conf)
+      val hadoopConfBroadcast = sparkContext.broadcast(


Yeah there is a code comment. The practice seems to be that it's better to use broadcast rather than task serialization as it could be huge.

HeartSaVioR · 2024-03-27T05:09:18Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

+            processData(store, singleIterator)
+        }
+      } else {
+        // If the query is running in batch mode, we need to create a new StateStore and instantiate


nit: apply the same practice while we are here? broadcast

HeartSaVioR · 2024-03-27T05:17:57Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

+              useMultipleValuesPerKey = true)
+            val store = stateStoreProvider.getStore(0)
+
+            processDataWithInitialState(store, childDataIterator, initStateIterator)


We close the state store and state store provider in batch codepath (see below). Shall we do that here as well?

Also, this is a good representation that we have duplicated code. two batch parts have similarity on spinning up state store provider and state store, and also closing them. That could be extracted out.

Good advice! Refactored duplicated codes into initNewStateStoreAndProcessData().

HeartSaVioR · 2024-03-27T05:19:56Z

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala

+
+    // Check if is first batch
+    // Only process initial states for first batch
+    if (processorHandle.getQueryInfo().getBatchId == 0) {


OK I see we have multiple checks. Though still better to change the condition in IncrementalExecution as reader can misunderstand that there are inconsistency between flatMapGroupsWithState and transformWithState.

HeartSaVioR

+1 pending CI

HeartSaVioR · 2024-03-28T05:49:52Z

CI failure isn't related - only pyspark-connect failed.

HeartSaVioR · 2024-03-28T05:49:59Z

Thanks! Merging to master.

…or State API v2 ### What changes were proposed in this pull request? This PR adds support for users to provide a Dataframe that can be used to instantiate state for the query in the first batch for arbitrary state API v2. Note that populating the initial state will only happen for the first batch of the new streaming query. Trying to re-initialize state for the same grouping key will result in an error. ### Why are the changes needed? These changes are needed to support initial state. The changes are part of the work around adding new stateful streaming operator for arbitrary state mgmt that provides a bunch of new features listed in the SPIP JIRA here - https://issues.apache.org/jira/browse/SPARK-45939 ### Does this PR introduce _any_ user-facing change? Yes. This PR introduces a new function: ``` def transformWithState( statefulProcessor: StatefulProcessorWithInitialState[K, V, U, S], timeoutMode: TimeoutMode, outputMode: OutputMode, initialState: KeyValueGroupedDataset[K, S]): Dataset[U] ``` ### How was this patch tested? Unit tests in `TransformWithStateWithInitialStateSuite` ### Was this patch authored or co-authored using generative AI tooling? No Closes apache#45467 from jingz-db/initial-state-state-v2. Lead-authored-by: jingz-db <jing.zhan@databricks.com> Co-authored-by: Jing Zhan <135738831+jingz-db@users.noreply.github.com> Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>

github-actions bot added SQL STRUCTURED STREAMING labels Mar 11, 2024

jingz-db changed the title ~~WIP~~ [SS] Initial State without state reader implementation for State API v2. Mar 11, 2024

jingz-db force-pushed the initial-state-state-v2 branch from 8ec379a to 8aac855 Compare March 12, 2024 01:21

jingz-db marked this pull request as ready for review March 12, 2024 01:21

jingz-db changed the title ~~[SS] Initial State without state reader implementation for State API v2.~~ [SS][SPARK-47363] Initial State without state reader implementation for State API v2. Mar 12, 2024

jingz-db force-pushed the initial-state-state-v2 branch from cd8b827 to 8fbd501 Compare March 13, 2024 00:24

anishshri-db reviewed Mar 13, 2024

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jingz-db added 14 commits March 14, 2024 11:52

WIP

190bdbb

everything but batch works

ad2a3bd

process init state, need to support batch

bd9d2f9

batch with initial state succeed

5587b08

rebase on master

8a863a1

change in IncrementalExecution

4aebe30

fix batch

fd31d2b

fix scala style

c8cf3c3

add test variable

bd5d908

refactor test suite

e2b1875

rebase on mapstate

0e5e997

partially resolve comments, will refactor test case

7e56085

fix mima?

6465521

resolve conflicts

9f18601

jingz-db force-pushed the initial-state-state-v2 branch from 25d7bab to 9f18601 Compare March 14, 2024 19:33

jingz-db requested a review from anishshri-db March 14, 2024 19:51

jingz-db added 3 commits March 19, 2024 13:29

resolve comments

7fa7adb

scala style

e25a352

forbid reinitialization

c659d17

github-actions bot added the DOCS label Mar 20, 2024

nits

4424c00

jingz-db requested a review from anishshri-db March 20, 2024 22:58

anishshri-db reviewed Mar 21, 2024

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resolve comments

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jingz-db requested a review from anishshri-db March 21, 2024 17:40

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anishshri-db approved these changes Mar 21, 2024

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jingz-db and others added 2 commits March 21, 2024 11:25

hadoop broadcast

d91817a

Merge branch 'master' into initial-state-state-v2

b3394d0

jingz-db requested a review from HeartSaVioR March 26, 2024 21:11

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beliefer changed the title ~~[SS][SPARK-47363] Initial State without state reader implementation for State API v2.~~ [SPARK-47363][SS] Initial State without state reader implementation for State API v2. Mar 27, 2024

github-actions bot added the BUILD label Mar 27, 2024

jingz-db force-pushed the initial-state-state-v2 branch from e528dfd to b3394d0 Compare March 27, 2024 20:59

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jingz-db added 3 commits March 27, 2024 14:06

resolve comments

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reuse code in batch process

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[SPARK-47363][SS] Initial State without state reader implementation for State API v2. #45467

[SPARK-47363][SS] Initial State without state reader implementation for State API v2. #45467

jingz-db commented Mar 11, 2024 •

edited

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HeartSaVioR commented Mar 28, 2024

HeartSaVioR commented Mar 28, 2024

	// A Hadoop Configuration can be about 10 KB, which is pretty big, so broadcast it
	private val hadoopConfBroadcast = session.sparkContext.broadcast(
	new SerializableConfiguration(session.sessionState.newHadoopConf()))

[SPARK-47363][SS] Initial State without state reader implementation for State API v2. #45467

[SPARK-47363][SS] Initial State without state reader implementation for State API v2. #45467

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jingz-db commented Mar 11, 2024 • edited

What changes were proposed in this pull request?

Why are the changes needed?

Does this PR introduce any user-facing change?

How was this patch tested?

Was this patch authored or co-authored using generative AI tooling?

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anishshri-db commented Mar 13, 2024

anishshri-db Mar 13, 2024 • edited

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HeartSaVioR commented Mar 28, 2024

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jingz-db commented Mar 11, 2024 •

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