Added support for SparkRunner streaming stateful processing (#33267)

.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark.json

@@ -3,5 +3,6 @@
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/31798": "noting that PR #31798 should run this test",
   "https://github.com/apache/beam/pull/32546": "noting that PR #32546 should run this test",
+  "https://github.com/apache/beam/pull/33267": "noting that PR #33267 should run this test",
   "https://github.com/apache/beam/pull/33322": "noting that PR #33322 should run this test"
 }

.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_SparkStructuredStreaming.json

@@ -2,5 +2,6 @@
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/31798": "noting that PR #31798 should run this test",
-  "https://github.com/apache/beam/pull/32546": "noting that PR #32546 should run this test"
+  "https://github.com/apache/beam/pull/32546": "noting that PR #32546 should run this test",
+  "https://github.com/apache/beam/pull/33267": "noting that PR #33267 should run this test"
 }

.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark_Java11.json

@@ -3,5 +3,6 @@
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/31798": "noting that PR #31798 should run this test",
   "https://github.com/apache/beam/pull/32546": "noting that PR #32546 should run this test",
+  "https://github.com/apache/beam/pull/33267": "noting that PR #33267 should run this test",
   "https://github.com/apache/beam/pull/33322": "noting that PR #33322 should run this test"
 }

CHANGES.md

@@ -71,11 +71,12 @@
 
 ## New Features / Improvements
 
+* Added support for stateful processing in Spark Runner for streaming pipelines. Timer functionality is not yet supported and will be implemented in a future release ([#33237](https://github.com/apache/beam/issues/33237)).
 * Improved batch performance of SparkRunner's GroupByKey ([#20943](https://github.com/apache/beam/pull/20943)).
-* X feature added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
 * Support OnWindowExpiration in Prism ([#32211](https://github.com/apache/beam/issues/32211)).
   * This enables initial Java GroupIntoBatches support.
 * Support OrderedListState in Prism ([#32929](https://github.com/apache/beam/issues/32929)).
+* X feature added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Breaking Changes
 

runners/spark/spark_runner.gradle

@@ -345,7 +345,7 @@ def validatesRunnerStreaming = tasks.register("validatesRunnerStreaming", Test)
     excludeCategories 'org.apache.beam.sdk.testing.UsesSdkHarnessEnvironment'
 
     // State and Timers
-    excludeCategories 'org.apache.beam.sdk.testing.UsesStatefulParDo'
+    excludeCategories 'org.apache.beam.sdk.testing.UsesTestStreamWithMultipleStages'
     excludeCategories 'org.apache.beam.sdk.testing.UsesTimersInParDo'
     excludeCategories 'org.apache.beam.sdk.testing.UsesTimerMap'
     excludeCategories 'org.apache.beam.sdk.testing.UsesLoopingTimer'

runners/spark/src/main/java/org/apache/beam/runners/spark/coders/SparkRunnerKryoRegistrator.java

@@ -21,7 +21,7 @@
 import java.util.ArrayList;
 import java.util.LinkedHashMap;
 import org.apache.beam.runners.spark.io.MicrobatchSource;
-import org.apache.beam.runners.spark.stateful.SparkGroupAlsoByWindowViaWindowSet.StateAndTimers;
+import org.apache.beam.runners.spark.stateful.StateAndTimers;
 import org.apache.beam.runners.spark.translation.ValueAndCoderKryoSerializer;
 import org.apache.beam.runners.spark.translation.ValueAndCoderLazySerializable;
 import org.apache.beam.runners.spark.util.ByteArray;

runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkGroupAlsoByWindowViaWindowSet.java

@@ -17,6 +17,9 @@
  */
 package org.apache.beam.runners.spark.stateful;
 
+import static org.apache.beam.runners.spark.translation.TranslationUtils.checkpointIfNeeded;
+import static org.apache.beam.runners.spark.translation.TranslationUtils.getBatchDuration;
+
 import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.Collection;
@@ -35,7 +38,6 @@
 import org.apache.beam.runners.core.metrics.MetricsContainerImpl;
 import org.apache.beam.runners.core.triggers.ExecutableTriggerStateMachine;
 import org.apache.beam.runners.core.triggers.TriggerStateMachines;
-import org.apache.beam.runners.spark.SparkPipelineOptions;
 import org.apache.beam.runners.spark.coders.CoderHelpers;
 import org.apache.beam.runners.spark.translation.ReifyTimestampsAndWindowsFunction;
 import org.apache.beam.runners.spark.translation.TranslationUtils;
@@ -60,10 +62,8 @@
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.AbstractIterator;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.FluentIterable;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Table;
 import org.apache.spark.api.java.JavaSparkContext$;
 import org.apache.spark.api.java.function.FlatMapFunction;
-import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.dstream.DStream;
@@ -100,27 +100,6 @@ public class SparkGroupAlsoByWindowViaWindowSet implements Serializable {
   private static final Logger LOG =
       LoggerFactory.getLogger(SparkGroupAlsoByWindowViaWindowSet.class);
 
-  /** State and Timers wrapper. */
-  public static class StateAndTimers implements Serializable {
-    // Serializable state for internals (namespace to state tag to coded value).
-    private final Table<String, String, byte[]> state;
-    private final Collection<byte[]> serTimers;
-
-    private StateAndTimers(
-        final Table<String, String, byte[]> state, final Collection<byte[]> timers) {
-      this.state = state;
-      this.serTimers = timers;
-    }
-
-    Table<String, String, byte[]> getState() {
-      return state;
-    }
-
-    Collection<byte[]> getTimers() {
-      return serTimers;
-    }
-  }
-
   private static class OutputWindowedValueHolder<K, V>
       implements OutputWindowedValue<KV<K, Iterable<V>>> {
     private final List<WindowedValue<KV<K, Iterable<V>>>> windowedValues = new ArrayList<>();
@@ -348,7 +327,7 @@ private Collection<TimerInternals.TimerData> filterTimersEligibleForProcessing(
 
             // empty outputs are filtered later using DStream filtering
             final StateAndTimers updated =
-                new StateAndTimers(
+                StateAndTimers.of(
                     stateInternals.getState(),
                     SparkTimerInternals.serializeTimers(
                         timerInternals.getTimers(), timerDataCoder));
@@ -466,21 +445,6 @@ private static <W extends BoundedWindow> TimerInternals.TimerDataCoderV2 timerDa
     return TimerInternals.TimerDataCoderV2.of(windowingStrategy.getWindowFn().windowCoder());
   }
 
-  private static void checkpointIfNeeded(
-      final DStream<Tuple2<ByteArray, Tuple2<StateAndTimers, List<byte[]>>>> firedStream,
-      final SerializablePipelineOptions options) {
-
-    final Long checkpointDurationMillis = getBatchDuration(options);
-
-    if (checkpointDurationMillis > 0) {
-      firedStream.checkpoint(new Duration(checkpointDurationMillis));
-    }
-  }
-
-  private static Long getBatchDuration(final SerializablePipelineOptions options) {
-    return options.get().as(SparkPipelineOptions.class).getCheckpointDurationMillis();
-  }
-
   private static <K, InputT> JavaDStream<WindowedValue<KV<K, Iterable<InputT>>>> stripStateValues(
       final DStream<Tuple2<ByteArray, Tuple2<StateAndTimers, List<byte[]>>>> firedStream,
       final Coder<K> keyCoder,

runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkStateInternals.java

@@ -63,7 +63,7 @@
 @SuppressWarnings({
   "nullness" // TODO(https://github.com/apache/beam/issues/20497)
 })
-class SparkStateInternals<K> implements StateInternals {
+public class SparkStateInternals<K> implements StateInternals {
 
   private final K key;
   // Serializable state for internals (namespace to state tag to coded value).
@@ -79,11 +79,11 @@ private SparkStateInternals(K key, Table<String, String, byte[]> stateTable) {
     this.stateTable = stateTable;
   }
 
-  static <K> SparkStateInternals<K> forKey(K key) {
+  public static <K> SparkStateInternals<K> forKey(K key) {
     return new SparkStateInternals<>(key);
   }
 
-  static <K> SparkStateInternals<K> forKeyAndState(
+  public static <K> SparkStateInternals<K> forKeyAndState(
       K key, Table<String, String, byte[]> stateTable) {
     return new SparkStateInternals<>(key, stateTable);
   }
@@ -412,17 +412,25 @@ public void put(MapKeyT key, MapValueT value) {
     @Override
     public ReadableState<MapValueT> computeIfAbsent(
         MapKeyT key, Function<? super MapKeyT, ? extends MapValueT> mappingFunction) {
-      Map<MapKeyT, MapValueT> sparkMapState = readValue();
+      Map<MapKeyT, MapValueT> sparkMapState = readAsMap();
       MapValueT current = sparkMapState.get(key);
       if (current == null) {
         put(key, mappingFunction.apply(key));
       }
       return ReadableStates.immediate(current);
     }
 
+    private Map<MapKeyT, MapValueT> readAsMap() {
+      Map<MapKeyT, MapValueT> mapState = readValue();
+      if (mapState == null) {
+        mapState = new HashMap<>();
+      }
+      return mapState;
+    }
+
     @Override
     public void remove(MapKeyT key) {
-      Map<MapKeyT, MapValueT> sparkMapState = readValue();
+      Map<MapKeyT, MapValueT> sparkMapState = readAsMap();
       sparkMapState.remove(key);
       writeValue(sparkMapState);
     }

runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkTimerInternals.java

@@ -107,7 +107,7 @@ public Collection<TimerData> getTimers() {
     return timers;
   }
 
-  void addTimers(Iterator<TimerData> timers) {
+  public void addTimers(Iterator<TimerData> timers) {
     while (timers.hasNext()) {
       TimerData timer = timers.next();
       this.timers.add(timer);
@@ -163,7 +163,8 @@ public void setTimer(
       Instant target,
       Instant outputTimestamp,
       TimeDomain timeDomain) {
-    throw new UnsupportedOperationException("Setting a timer by ID not yet supported.");
+    this.setTimer(
+        TimerData.of(timerId, timerFamilyId, namespace, target, outputTimestamp, timeDomain));
   }
 
   @Override

runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/StateAndTimers.java

@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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 org.apache.beam.runners.spark.stateful;
+
+import com.google.auto.value.AutoValue;
+import java.io.Serializable;
+import java.util.Collection;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Table;
+
+/** State and Timers wrapper. */
+@AutoValue
+public abstract class StateAndTimers implements Serializable {
+  public abstract Table<String, String, byte[]> getState();
+
+  public abstract Collection<byte[]> getTimers();
+
+  public static StateAndTimers of(
+      final Table<String, String, byte[]> state, final Collection<byte[]> timers) {
+    return new AutoValue_StateAndTimers.Builder().setState(state).setTimers(timers).build();
+  }
+
+  @AutoValue.Builder
+  abstract static class Builder {
+    abstract Builder setState(Table<String, String, byte[]> state);
+
+    abstract Builder setTimers(Collection<byte[]> timers);
+
+    abstract StateAndTimers build();
+  }
+}

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/DoFnRunnerWithMetrics.java

@@ -31,12 +31,12 @@
 import org.joda.time.Instant;
 
 /** DoFnRunner decorator which registers {@link MetricsContainerImpl}. */
-class DoFnRunnerWithMetrics<InputT, OutputT> implements DoFnRunner<InputT, OutputT> {
+public class DoFnRunnerWithMetrics<InputT, OutputT> implements DoFnRunner<InputT, OutputT> {
   private final DoFnRunner<InputT, OutputT> delegate;
   private final String stepName;
   private final MetricsContainerStepMapAccumulator metricsAccum;
 
-  DoFnRunnerWithMetrics(
+  public DoFnRunnerWithMetrics(
       String stepName,
       DoFnRunner<InputT, OutputT> delegate,
       MetricsContainerStepMapAccumulator metricsAccum) {

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java

@@ -47,7 +47,7 @@
  * Processes Spark's input data iterators using Beam's {@link
  * org.apache.beam.runners.core.DoFnRunner}.
  */
-interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+public interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
 
   /**
    * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkProcessContext.java

@@ -23,14 +23,14 @@
 import org.apache.beam.sdk.transforms.DoFn;
 
 /** Holds current processing context for {@link SparkInputDataProcessor}. */
-class SparkProcessContext<K, InputT, OutputT> {
+public class SparkProcessContext<K, InputT, OutputT> {
   private final String stepName;
   private final DoFn<InputT, OutputT> doFn;
   private final DoFnRunner<InputT, OutputT> doFnRunner;
   private final Iterator<TimerInternals.TimerData> timerDataIterator;
   private final K key;
 
-  SparkProcessContext(
+  public SparkProcessContext(
       String stepName,
       DoFn<InputT, OutputT> doFn,
       DoFnRunner<InputT, OutputT> doFnRunner,

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TranslationUtils.java

@@ -26,6 +26,8 @@
 import org.apache.beam.runners.core.InMemoryStateInternals;
 import org.apache.beam.runners.core.StateInternals;
 import org.apache.beam.runners.core.StateInternalsFactory;
+import org.apache.beam.runners.core.construction.SerializablePipelineOptions;
+import org.apache.beam.runners.spark.SparkPipelineOptions;
 import org.apache.beam.runners.spark.SparkRunner;
 import org.apache.beam.runners.spark.coders.CoderHelpers;
 import org.apache.beam.runners.spark.util.ByteArray;
@@ -54,8 +56,10 @@
 import org.apache.spark.api.java.function.PairFunction;
 import org.apache.spark.api.java.function.VoidFunction;
 import org.apache.spark.storage.StorageLevel;
+import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.dstream.DStream;
 import scala.Tuple2;
 
 /** A set of utilities to help translating Beam transformations into Spark transformations. */
@@ -258,6 +262,52 @@ public Boolean call(Tuple2<TupleTag<V>, WindowedValue<?>> input) {
     }
   }
 
+  /**
+   * Retrieves the batch duration in milliseconds from Spark pipeline options.
+   *
+   * @param options The serializable pipeline options containing Spark-specific settings
+   * @return The checkpoint duration in milliseconds as specified in SparkPipelineOptions
+   */
+  public static Long getBatchDuration(final SerializablePipelineOptions options) {
+    return options.get().as(SparkPipelineOptions.class).getCheckpointDurationMillis();
+  }
+
+  /**
+   * Reject timers {@link DoFn}.
+   *
+   * @param doFn the {@link DoFn} to possibly reject.
+   */
+  public static void rejectTimers(DoFn<?, ?> doFn) {
+    DoFnSignature signature = DoFnSignatures.getSignature(doFn.getClass());
+    if (signature.timerDeclarations().size() > 0
+        || signature.timerFamilyDeclarations().size() > 0) {
+      throw new UnsupportedOperationException(
+          String.format(
+              "Found %s annotations on %s, but %s cannot yet be used with timers in the %s.",
+              DoFn.TimerId.class.getSimpleName(),
+              doFn.getClass().getName(),
+              DoFn.class.getSimpleName(),
+              SparkRunner.class.getSimpleName()));
+    }
+  }
+
+  /**
+   * Checkpoints the given DStream if checkpointing is enabled in the pipeline options.
+   *
+   * @param dStream The DStream to be checkpointed
+   * @param options The SerializablePipelineOptions containing configuration settings including
+   *     batch duration
+   */
+  public static void checkpointIfNeeded(
+      final DStream<?> dStream, final SerializablePipelineOptions options) {
+
+    final Long checkpointDurationMillis = getBatchDuration(options);
+
+    if (checkpointDurationMillis > 0) {
+      dStream.checkpoint(new Duration(checkpointDurationMillis));
+    }
+  }
+
   /**
    * Reject state and timers {@link DoFn}.
    *