Merge branch 'master' into reorganize_iceberg_its

apache · Dec 23, 2024 · 4d39646 · 4d39646
2 parents 156ad2b + 7c86bf3
commit 4d39646
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diff --git a/.asf.yaml b/.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: {}

diff --git a/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Dataflow.json b/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Dataflow.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "modification": 2
+  "modification": 3
 }
diff --git a/.github/workflows/beam_Python_CostBenchmarks_Dataflow.yml b/.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/' \
diff --git a/CHANGES.md b/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
 
@@ -63,24 +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
 
-* 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)).
-* 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
 
-* 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
@@ -90,16 +79,44 @@
 ## Bugfixes
 
 * Fixed X (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
-* Fixed EventTimeTimer ordering in Prism. ([#32222](https://github.com/apache/beam/issues/32222)).
 
 ## 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

diff --git a/examples/multi-language/python/wordcount_external.py b/examples/multi-language/python/wordcount_external.py
@@ -18,8 +18,8 @@
 import logging
 
 import apache_beam as beam
-from apache_beam.io import ReadFromText
 from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.transforms.external import BeamJarExpansionService
 from apache_beam.transforms.external_transform_provider import ExternalTransformProvider
 from apache_beam.typehints.row_type import RowTypeConstraint
 """A Python multi-language pipeline that counts words using multiple Java SchemaTransforms.
@@ -60,39 +60,35 @@
       --expansion_service_port <PORT>
 """
 
-# Original Java transform is in ExtractWordsProvider.java
 EXTRACT_IDENTIFIER = "beam:schematransform:org.apache.beam:extract_words:v1"
-# Original Java transform is in JavaCountProvider.java
 COUNT_IDENTIFIER = "beam:schematransform:org.apache.beam:count:v1"
-# Original Java transform is in WriteWordsProvider.java
 WRITE_IDENTIFIER = "beam:schematransform:org.apache.beam:write_words:v1"
 
 
 def run(input_path, output_path, expansion_service_port, pipeline_args):
     pipeline_options = PipelineOptions(pipeline_args)
 
-    # Discover and get external transforms from this expansion service
-    provider = ExternalTransformProvider("localhost:" + expansion_service_port)
-    # Get transforms with identifiers, then use them as you would a regular
-    # native PTransform
-    Extract = provider.get_urn(EXTRACT_IDENTIFIER)
-    Count = provider.get_urn(COUNT_IDENTIFIER)
-    Write = provider.get_urn(WRITE_IDENTIFIER)
-
     with beam.Pipeline(options=pipeline_options) as p:
-        lines = p | 'Read' >> ReadFromText(input_path)
-
-        words = (lines
-                 | 'Prepare Rows' >> beam.Map(lambda line: beam.Row(line=line))
-                 | 'Extract Words' >> Extract())
-        word_counts = words | 'Count Words' >> Count()
-        formatted_words = (
-            word_counts
-            | 'Format Text' >> beam.Map(lambda row: beam.Row(line="%s: %s" % (
-                row.word, row.count))).with_output_types(
-                    RowTypeConstraint.from_fields([('line', str)])))
-
-        formatted_words | 'Write' >> Write(file_path_prefix=output_path)
+        expansion_service = BeamJarExpansionService(
+            "examples:multi-language:shadowJar")
+        if expansion_service_port:
+            expansion_service = "localhost:" + expansion_service_port
+
+        provider = ExternalTransformProvider(expansion_service)
+        # Retrieve portable transforms
+        Extract = provider.get_urn(EXTRACT_IDENTIFIER)
+        Count = provider.get_urn(COUNT_IDENTIFIER)
+        Write = provider.get_urn(WRITE_IDENTIFIER)
+
+        _ = (p
+             | 'Read' >> beam.io.ReadFromText(input_path)
+             | 'Prepare Rows' >> beam.Map(lambda line: beam.Row(line=line))
+             | 'Extract Words' >> Extract(drop=["king", "palace"])
+             | 'Count Words' >> Count()
+             | 'Format Text' >> beam.Map(lambda row: beam.Row(line="%s: %s" % (
+                 row.word, row.count))).with_output_types(
+                     RowTypeConstraint.from_fields([('line', str)]))
+             | 'Write' >> Write(file_path_prefix=output_path))
 
 
 if __name__ == '__main__':
@@ -110,8 +106,10 @@ def run(input_path, output_path, expansion_service_port, pipeline_args):
                         help='Output file')
     parser.add_argument('--expansion_service_port',
                         dest='expansion_service_port',
-                        required=True,
-                        help='Expansion service port')
+                        required=False,
+                        help='Expansion service port. If left empty, the '
+                        'existing multi-language examples service will '
+                        'be used by default.')
     known_args, pipeline_args = parser.parse_known_args()
 
     run(known_args.input, known_args.output, known_args.expansion_service_port,

diff --git a/...in/java/org/apache/beam/examples/multilanguage/schematransforms/ExtractWordsProvider.java b/...in/java/org/apache/beam/examples/multilanguage/schematransforms/ExtractWordsProvider.java
@@ -21,9 +21,12 @@
 
 import com.google.auto.service.AutoService;
 import com.google.auto.value.AutoValue;
+import java.util.Arrays;
+import java.util.List;
 import org.apache.beam.sdk.schemas.AutoValueSchema;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.annotations.DefaultSchema;
+import org.apache.beam.sdk.schemas.annotations.SchemaFieldDescription;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.apache.beam.sdk.schemas.transforms.TypedSchemaTransformProvider;
@@ -36,7 +39,6 @@
 /** Splits a line into separate words and returns each word. */
 @AutoService(SchemaTransformProvider.class)
 public class ExtractWordsProvider extends TypedSchemaTransformProvider<Configuration> {
-  public static final Schema OUTPUT_SCHEMA = Schema.builder().addStringField("word").build();
 
   @Override
   public String identifier() {
@@ -45,32 +47,60 @@ public String identifier() {
 
   @Override
   protected SchemaTransform from(Configuration configuration) {
-    return new SchemaTransform() {
-      @Override
-      public PCollectionRowTuple expand(PCollectionRowTuple input) {
-        return PCollectionRowTuple.of(
-            "output",
-            input.get("input").apply(ParDo.of(new ExtractWordsFn())).setRowSchema(OUTPUT_SCHEMA));
-      }
-    };
+    return new ExtractWordsTransform(configuration);
   }
 
-  static class ExtractWordsFn extends DoFn<Row, Row> {
-    @ProcessElement
-    public void processElement(@Element Row element, OutputReceiver<Row> receiver) {
-      // Split the line into words.
-      String line = Preconditions.checkStateNotNull(element.getString("line"));
-      String[] words = line.split("[^\\p{L}]+", -1);
+  static class ExtractWordsTransform extends SchemaTransform {
+    private static final Schema OUTPUT_SCHEMA = Schema.builder().addStringField("word").build();
+    private final List<String> drop;
 
-      for (String word : words) {
-        if (!word.isEmpty()) {
-          receiver.output(Row.withSchema(OUTPUT_SCHEMA).withFieldValue("word", word).build());
-        }
-      }
+    ExtractWordsTransform(Configuration configuration) {
+      this.drop = configuration.getDrop();
+    }
+
+    @Override
+    public PCollectionRowTuple expand(PCollectionRowTuple input) {
+      return PCollectionRowTuple.of(
+          "output",
+          input
+              .getSinglePCollection()
+              .apply(
+                  ParDo.of(
+                      new DoFn<Row, Row>() {
+                        @ProcessElement
+                        public void process(@Element Row element, OutputReceiver<Row> receiver) {
+                          // Split the line into words.
+                          String line = Preconditions.checkStateNotNull(element.getString("line"));
+                          String[] words = line.split("[^\\p{L}]+", -1);
+                          Arrays.stream(words)
+                              .filter(w -> !drop.contains(w))
+                              .forEach(
+                                  word ->
+                                      receiver.output(
+                                          Row.withSchema(OUTPUT_SCHEMA)
+                                              .withFieldValue("word", word)
+                                              .build()));
+                        }
+                      }))
+              .setRowSchema(OUTPUT_SCHEMA));
     }
   }
 
   @DefaultSchema(AutoValueSchema.class)
   @AutoValue
-  protected abstract static class Configuration {}
+  public abstract static class Configuration {
+    public static Builder builder() {
+      return new AutoValue_ExtractWordsProvider_Configuration.Builder();
+    }
+
+    @SchemaFieldDescription("List of words to drop.")
+    public abstract List<String> getDrop();
+
+    @AutoValue.Builder
+    public abstract static class Builder {
+      public abstract Builder setDrop(List<String> foo);
+
+      public abstract Configuration build();
+    }
+  }
 }
diff --git a/.../main/java/org/apache/beam/examples/multilanguage/schematransforms/JavaCountProvider.java b/.../main/java/org/apache/beam/examples/multilanguage/schematransforms/JavaCountProvider.java
@@ -44,35 +44,37 @@ public String identifier() {
 
   @Override
   protected SchemaTransform from(Configuration configuration) {
-    return new SchemaTransform() {
-      @Override
-      public PCollectionRowTuple expand(PCollectionRowTuple input) {
-        Schema outputSchema =
-            Schema.builder().addStringField("word").addInt64Field("count").build();
+    return new JavaCountTransform();
+  }
+
+  static class JavaCountTransform extends SchemaTransform {
+    static final Schema OUTPUT_SCHEMA =
+        Schema.builder().addStringField("word").addInt64Field("count").build();
 
-        PCollection<Row> wordCounts =
-            input
-                .get("input")
-                .apply(Count.perElement())
-                .apply(
-                    MapElements.into(TypeDescriptors.rows())
-                        .via(
-                            kv ->
-                                Row.withSchema(outputSchema)
-                                    .withFieldValue(
-                                        "word",
-                                        Preconditions.checkStateNotNull(
-                                            kv.getKey().getString("word")))
-                                    .withFieldValue("count", kv.getValue())
-                                    .build()))
-                .setRowSchema(outputSchema);
+    @Override
+    public PCollectionRowTuple expand(PCollectionRowTuple input) {
+      PCollection<Row> wordCounts =
+          input
+              .get("input")
+              .apply(Count.perElement())
+              .apply(
+                  MapElements.into(TypeDescriptors.rows())
+                      .via(
+                          kv ->
+                              Row.withSchema(OUTPUT_SCHEMA)
+                                  .withFieldValue(
+                                      "word",
+                                      Preconditions.checkStateNotNull(
+                                          kv.getKey().getString("word")))
+                                  .withFieldValue("count", kv.getValue())
+                                  .build()))
+              .setRowSchema(OUTPUT_SCHEMA);
 
-        return PCollectionRowTuple.of("output", wordCounts);
-      }
-    };
+      return PCollectionRowTuple.of("output", wordCounts);
+    }
   }
 
   @DefaultSchema(AutoValueSchema.class)
   @AutoValue
-  protected abstract static class Configuration {}
+  public abstract static class Configuration {}
 }