[
https://issues.apache.org/jira/browse/BEAM-3193?focusedWorklogId=140891&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-140891
]
ASF GitHub Bot logged work on BEAM-3193:
----------------------------------------
Author: ASF GitHub Bot
Created on: 04/Sep/18 12:46
Start Date: 04/Sep/18 12:46
Worklog Time Spent: 10m
Work Description: echauchot closed pull request #5945: [BEAM-3193] Add
SparkCoGroupByKeyStreaming validates runner to test CoGroupByKay bahavior in
streaming mode on spark runner
URL: https://github.com/apache/beam/pull/5945
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
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diff --git
a/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/SparkCoGroupByKeyStreamingTest.java
b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/SparkCoGroupByKeyStreamingTest.java
new file mode 100644
index 00000000000..b89519fe0ff
--- /dev/null
+++
b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/SparkCoGroupByKeyStreamingTest.java
@@ -0,0 +1,172 @@
+/*
+ * 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.translation.streaming;
+
+import static org.hamcrest.Matchers.containsInAnyOrder;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertThat;
+import static org.junit.Assert.fail;
+
+import com.google.common.collect.Iterables;
+import org.apache.beam.runners.spark.ReuseSparkContextRule;
+import org.apache.beam.runners.spark.SparkPipelineOptions;
+import org.apache.beam.runners.spark.StreamingTest;
+import org.apache.beam.runners.spark.io.CreateStream;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.coders.VarIntCoder;
+import org.apache.beam.sdk.testing.PAssert;
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.transforms.SerializableFunction;
+import org.apache.beam.sdk.transforms.join.CoGbkResult;
+import org.apache.beam.sdk.transforms.join.CoGroupByKey;
+import org.apache.beam.sdk.transforms.join.KeyedPCollectionTuple;
+import org.apache.beam.sdk.transforms.windowing.FixedWindows;
+import org.apache.beam.sdk.transforms.windowing.Window;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.TimestampedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.experimental.categories.Category;
+
+/** A test that verifies that CoGroupByKey works in streaming mode in spark
runner. */
+public class SparkCoGroupByKeyStreamingTest {
+
+ private static final TupleTag<Integer> INPUT1_TAG = new TupleTag<>("input1");
+ private static final TupleTag<Integer> INPUT2_TAG = new TupleTag<>("input2");
+
+ @Rule public final transient ReuseSparkContextRule noContextResue =
ReuseSparkContextRule.no();
+
+ @Rule public final TestPipeline pipeline = TestPipeline.create();
+
+ private Duration batchDuration() {
+ return Duration.millis(
+
(pipeline.getOptions().as(SparkPipelineOptions.class)).getBatchIntervalMillis());
+ }
+
+ @Category(StreamingTest.class)
+ @Test
+ public void testInStreamingMode() throws Exception {
+ Instant instant = new Instant(0);
+ CreateStream<KV<Integer, Integer>> source1 =
+ CreateStream.of(KvCoder.of(VarIntCoder.of(), VarIntCoder.of()),
batchDuration())
+ .emptyBatch()
+ .advanceWatermarkForNextBatch(instant)
+ .nextBatch(
+ TimestampedValue.of(KV.of(1, 1), instant),
+ TimestampedValue.of(KV.of(1, 2), instant),
+ TimestampedValue.of(KV.of(1, 3), instant))
+
.advanceWatermarkForNextBatch(instant.plus(Duration.standardSeconds(1L)))
+ .nextBatch(
+ TimestampedValue.of(KV.of(2, 4),
instant.plus(Duration.standardSeconds(1L))),
+ TimestampedValue.of(KV.of(2, 5),
instant.plus(Duration.standardSeconds(1L))),
+ TimestampedValue.of(KV.of(2, 6),
instant.plus(Duration.standardSeconds(1L))))
+ .advanceNextBatchWatermarkToInfinity();
+
+ CreateStream<KV<Integer, Integer>> source2 =
+ CreateStream.of(KvCoder.of(VarIntCoder.of(), VarIntCoder.of()),
batchDuration())
+ .emptyBatch()
+ .advanceWatermarkForNextBatch(instant)
+ .nextBatch(
+ TimestampedValue.of(KV.of(1, 11), instant),
+ TimestampedValue.of(KV.of(1, 12), instant),
+ TimestampedValue.of(KV.of(1, 13), instant))
+
.advanceWatermarkForNextBatch(instant.plus(Duration.standardSeconds(1L)))
+ .nextBatch(
+ TimestampedValue.of(KV.of(2, 14),
instant.plus(Duration.standardSeconds(1L))),
+ TimestampedValue.of(KV.of(2, 15),
instant.plus(Duration.standardSeconds(1L))),
+ TimestampedValue.of(KV.of(2, 16),
instant.plus(Duration.standardSeconds(1L))))
+ .advanceNextBatchWatermarkToInfinity();
+
+ PCollection<KV<Integer, Integer>> input1 =
+ pipeline
+ .apply("create source1", source1)
+ .apply(
+ "window input1",
+ Window.<KV<Integer,
Integer>>into(FixedWindows.of(Duration.standardSeconds(3L)))
+ .withAllowedLateness(Duration.ZERO));
+
+ PCollection<KV<Integer, Integer>> input2 =
+ pipeline
+ .apply("create source2", source2)
+ .apply(
+ "window input2",
+ Window.<KV<Integer,
Integer>>into(FixedWindows.of(Duration.standardSeconds(3L)))
+ .withAllowedLateness(Duration.ZERO));
+
+ PCollection<KV<Integer, CoGbkResult>> output =
+ KeyedPCollectionTuple.of(INPUT1_TAG, input1)
+ .and(INPUT2_TAG, input2)
+ .apply(CoGroupByKey.create());
+ PAssert.that("Wrong output of the join using CoGroupByKey in streaming
mode", output)
+ .satisfies(
+ (SerializableFunction<Iterable<KV<Integer, CoGbkResult>>, Void>)
+ input -> {
+ assertEquals("Wrong size of the output PCollection", 2,
Iterables.size(input));
+ for (KV<Integer, CoGbkResult> element : input) {
+ if (element.getKey() == 1) {
+ Iterable<Integer> input1Elements =
element.getValue().getAll(INPUT1_TAG);
+ assertEquals(
+ "Wrong number of values for output elements for tag
input1 and key 1",
+ 3,
+ Iterables.size(input1Elements));
+ assertThat(
+ "Elements of PCollection input1 for key \"1\" are
not present in the output PCollection",
+ input1Elements,
+ containsInAnyOrder(1, 2, 3));
+ Iterable<Integer> input2Elements =
element.getValue().getAll(INPUT2_TAG);
+ assertEquals(
+ "Wrong number of values for output elements for tag
input2 and key 1",
+ 3,
+ Iterables.size(input2Elements));
+ assertThat(
+ "Elements of PCollection input2 for key \"1\" are
not present in the output PCollection",
+ input2Elements,
+ containsInAnyOrder(11, 12, 13));
+ } else if (element.getKey() == 2) {
+ Iterable<Integer> input1Elements =
element.getValue().getAll(INPUT1_TAG);
+ assertEquals(
+ "Wrong number of values for output elements for tag
input1 and key 2",
+ 3,
+ Iterables.size(input1Elements));
+ assertThat(
+ "Elements of PCollection input1 for key \"2\" are
not present in the output PCollection",
+ input1Elements,
+ containsInAnyOrder(4, 5, 6));
+ Iterable<Integer> input2Elements =
element.getValue().getAll(INPUT2_TAG);
+ assertEquals(
+ "Wrong number of values for output elements for tag
input2 and key 2",
+ 3,
+ Iterables.size(input2Elements));
+ assertThat(
+ "Elements of PCollection input2 for key \"2\" are
not present in the output PCollection",
+ input2Elements,
+ containsInAnyOrder(14, 15, 16));
+ } else {
+ fail("Unknown key in the output PCollection");
+ }
+ }
+ return null;
+ });
+ pipeline.run();
+ }
+}
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Issue Time Tracking
-------------------
Worklog Id: (was: 140891)
Time Spent: 3h 50m (was: 3h 40m)
> CoGroupByKey doesn't work in streaming mode
> -------------------------------------------
>
> Key: BEAM-3193
> URL: https://issues.apache.org/jira/browse/BEAM-3193
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Reporter: Jean-Baptiste Onofré
> Assignee: Etienne Chauchot
> Priority: Major
> Time Spent: 3h 50m
> Remaining Estimate: 0h
>
> The CoGroupByKey PTransform doesn't throw an exception but doesn't actually
> perform the grouping when used in streaming mode. I will attach a test
> pipeline.
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