Merge branch 'master' into storapi_schemaupdate_dynamicdest

.asf.yaml

@@ -49,6 +49,7 @@ github:
 
   protected_branches:
     master: {}
+    release-2.62.0: {}
     release-2.61.0: {}
     release-2.60.0: {}
     release-2.59.0: {}

.github/trigger_files/IO_Iceberg_Integration_Tests.json

@@ -1,4 +1,4 @@
 {
     "comment": "Modify this file in a trivial way to cause this test suite to run",
-    "modification": 5
+    "modification": 6
 }

.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"
 }

.github/workflows/beam_Python_CostBenchmarks_Dataflow.yml

@@ -92,7 +92,7 @@ jobs:
             -PloadTest.mainClass=apache_beam.testing.benchmarks.wordcount.wordcount \
             -Prunner=DataflowRunner \
             -PpythonVersion=3.10 \
-            '-PloadTest.args=${{ env.beam_Inference_Python_Benchmarks_Dataflow_test_arguments_1 }} --job_name=benchmark-tests-wordcount-python-${{env.NOW_UTC}} --output_file=gs://temp-storage-for-end-to-end-tests/wordcount/result_wordcount-${{env.NOW_UTC}}.txt' \
+            '-PloadTest.args=${{ env.beam_Python_Cost_Benchmarks_Dataflow_test_arguments_1 }} --job_name=benchmark-tests-wordcount-python-${{env.NOW_UTC}} --output_file=gs://temp-storage-for-end-to-end-tests/wordcount/result_wordcount-${{env.NOW_UTC}}.txt' \
       - name: Run Tensorflow MNIST Image Classification on Dataflow
         uses: ./.github/actions/gradle-command-self-hosted-action
         timeout-minutes: 30
@@ -102,4 +102,5 @@ jobs:
             -PloadTest.mainClass=apache_beam.testing.benchmarks.inference.tensorflow_mnist_classification_cost_benchmark \
             -Prunner=DataflowRunner \
             -PpythonVersion=3.10 \
-            '-PloadTest.args=${{ env.beam_Inference_Python_Benchmarks_Dataflow_test_arguments_2 }} --job_name=benchmark-tests-tf-mnist-classification-python-${{env.NOW_UTC}} --input_file=gs://apache-beam-ml/testing/inputs/it_mnist_data.csv --output_file=gs://temp-storage-for-end-to-end-tests/wordcount/result_tf_mnist-${{env.NOW_UTC}}.txt --model=gs://apache-beam-ml/models/tensorflow/mnist/' \
+            -PloadTest.requirementsTxtFile=apache_beam/ml/inference/tensorflow_tests_requirements.txt \
+            '-PloadTest.args=${{ env.beam_Python_Cost_Benchmarks_Dataflow_test_arguments_2 }} --job_name=benchmark-tests-tf-mnist-classification-python-${{env.NOW_UTC}} --input_file=gs://apache-beam-ml/testing/inputs/it_mnist_data.csv --output_file=gs://temp-storage-for-end-to-end-tests/inference/result_tf_mnist-${{env.NOW_UTC}}.txt --model=gs://apache-beam-ml/models/tensorflow/mnist/' \

CHANGES.md

@@ -31,7 +31,6 @@
 
 ## New Features / Improvements
 
-* The datetime module is now available for use in jinja templatization for yaml.
 * X feature added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Breaking Changes
@@ -54,7 +53,7 @@
 * ([#X](https://github.com/apache/beam/issues/X)).
 -->
 
-# [2.62.0] - Unreleased
+# [2.63.0] - Unreleased
 
 ## Highlights
 
@@ -63,23 +62,14 @@
 
 ## I/Os
 
-* gcs-connector config options can be set via GcsOptions (Java) ([#32769](https://github.com/apache/beam/pull/32769)).
 * Support for X source added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
-* [Managed Iceberg] Support partitioning by time (year, month, day, hour) for types `date`, `time`, `timestamp`, and `timestamp(tz)` ([#32939](https://github.com/apache/beam/pull/32939))
-* Upgraded the default version of Hadoop dependencies to 3.4.1. Hadoop 2.10.2 is still supported (Java) ([#33011](https://github.com/apache/beam/issues/33011)).
-* [BigQueryIO] Create managed BigLake tables dynamically ([#33125](https://github.com/apache/beam/pull/33125))
 
 ## New Features / Improvements
 
-* 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)).
 
 ## Breaking Changes
 
-* Upgraded ZetaSQL to 2024.11.1 ([#32902](https://github.com/apache/beam/pull/32902)). Java11+ is now needed if Beam's ZetaSQL component is used.
 * X behavior was changed ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Deprecations
@@ -94,12 +84,41 @@
 
 ## Security Fixes
 * Fixed (CVE-YYYY-NNNN)[https://www.cve.org/CVERecord?id=CVE-YYYY-NNNN] (Java/Python/Go) ([#X](https://github.com/apache/beam/issues/X)).
-* Fixed (CVE-2024-47561)[https://www.cve.org/CVERecord?id=CVE-2024-47561] (Java) by upgrading Avro version to 1.11.4
 
 ## Known Issues
 
 * ([#X](https://github.com/apache/beam/issues/X)).
 
+# [2.62.0] - Unreleased
+
+## I/Os
+
+* gcs-connector config options can be set via GcsOptions (Java) ([#32769](https://github.com/apache/beam/pull/32769)).
+* [Managed Iceberg] Support partitioning by time (year, month, day, hour) for types `date`, `time`, `timestamp`, and `timestamp(tz)` ([#32939](https://github.com/apache/beam/pull/32939))
+* Upgraded the default version of Hadoop dependencies to 3.4.1. Hadoop 2.10.2 is still supported (Java) ([#33011](https://github.com/apache/beam/issues/33011)).
+* [BigQueryIO] Create managed BigLake tables dynamically ([#33125](https://github.com/apache/beam/pull/33125))
+
+## 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)).
+* The datetime module is now available for use in jinja templatization for yaml.
+* Improved batch performance of SparkRunner's GroupByKey ([#20943](https://github.com/apache/beam/pull/20943)).
+* 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)).
+
+## Breaking Changes
+
+* Upgraded ZetaSQL to 2024.11.1 ([#32902](https://github.com/apache/beam/pull/32902)). Java11+ is now needed if Beam's ZetaSQL component is used.
+
+## Bugfixes
+
+* Fixed EventTimeTimer ordering in Prism. ([#32222](https://github.com/apache/beam/issues/32222)).
+
+## Security Fixes
+
+* Fixed (CVE-2024-47561)[https://www.cve.org/CVERecord?id=CVE-2024-47561] (Java) by upgrading Avro version to 1.11.4
+
 # [2.61.0] - 2024-11-25
 
 ## Highlights

contributor-docs/code-change-guide.md

@@ -375,6 +375,9 @@ Follow these steps for Maven projects.
       <id>Maven-Snapshot</id>
       <name>maven snapshot repository</name>
       <url>https://repository.apache.org/content/groups/snapshots/</url>
+      <releases>
+        <enabled>false</enabled>
+      </releases>
     </repository>
     ```
 

gradle.properties

@@ -30,8 +30,8 @@ signing.gnupg.useLegacyGpg=true
 # buildSrc/src/main/groovy/org/apache/beam/gradle/BeamModulePlugin.groovy.
 # To build a custom Beam version make sure you change it in both places, see
 # https://github.com/apache/beam/issues/21302.
-version=2.62.0-SNAPSHOT
-sdk_version=2.62.0.dev
+version=2.63.0-SNAPSHOT
+sdk_version=2.63.0.dev
 
 javaVersion=1.8
 

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) {