ibzib commented on a change in pull request #15267:
URL: https://github.com/apache/beam/pull/15267#discussion_r703719653
##########
File path:
sdks/java/io/elasticsearch-tests/elasticsearch-tests-5/src/test/java/org/apache/beam/sdk/io/elasticsearch/ElasticsearchIOTest.java
##########
@@ -83,6 +84,7 @@ public void setup() throws IOException {
@Rule public TestPipeline pipeline = TestPipeline.create();
+ @Ignore("Temporary ignore this test waiting for a fix because it is flaky")
Review comment:
I agree we should ignore these tests, but since they're unrelated to the
Spark changes we should do it in a separate PR.
Also make sure to include the JIRA number in the message.
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/batch/GroupByKeyTranslatorBatch.java
##########
@@ -17,74 +17,95 @@
*/
package org.apache.beam.runners.spark.structuredstreaming.translation.batch;
-import java.io.Serializable;
-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.SystemReduceFn;
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.beam.runners.core.Concatenate;
import
org.apache.beam.runners.spark.structuredstreaming.translation.AbstractTranslationContext;
import
org.apache.beam.runners.spark.structuredstreaming.translation.TransformTranslator;
-import
org.apache.beam.runners.spark.structuredstreaming.translation.batch.functions.GroupAlsoByWindowViaOutputBufferFn;
import
org.apache.beam.runners.spark.structuredstreaming.translation.helpers.EncoderHelpers;
import
org.apache.beam.runners.spark.structuredstreaming.translation.helpers.KVHelpers;
+import org.apache.beam.sdk.coders.CannotProvideCoderException;
import org.apache.beam.sdk.coders.Coder;
-import org.apache.beam.sdk.coders.IterableCoder;
import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.transforms.Combine;
import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
import org.apache.beam.sdk.util.WindowedValue;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.WindowingStrategy;
+import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.KeyValueGroupedDataset;
+import scala.Tuple2;
class GroupByKeyTranslatorBatch<K, V>
- implements TransformTranslator<
- PTransform<PCollection<KV<K, V>>, PCollection<KV<K, Iterable<V>>>>> {
+ implements TransformTranslator<
Review comment:
I am not sure why the indentation changed here. Do we not use Spotless
on the Spark structured streaming project?
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/batch/GroupByKeyTranslatorBatch.java
##########
@@ -17,74 +17,95 @@
*/
package org.apache.beam.runners.spark.structuredstreaming.translation.batch;
-import java.io.Serializable;
-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.SystemReduceFn;
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.beam.runners.core.Concatenate;
import
org.apache.beam.runners.spark.structuredstreaming.translation.AbstractTranslationContext;
import
org.apache.beam.runners.spark.structuredstreaming.translation.TransformTranslator;
-import
org.apache.beam.runners.spark.structuredstreaming.translation.batch.functions.GroupAlsoByWindowViaOutputBufferFn;
import
org.apache.beam.runners.spark.structuredstreaming.translation.helpers.EncoderHelpers;
import
org.apache.beam.runners.spark.structuredstreaming.translation.helpers.KVHelpers;
+import org.apache.beam.sdk.coders.CannotProvideCoderException;
import org.apache.beam.sdk.coders.Coder;
-import org.apache.beam.sdk.coders.IterableCoder;
import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.transforms.Combine;
import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
import org.apache.beam.sdk.util.WindowedValue;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.WindowingStrategy;
+import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.KeyValueGroupedDataset;
+import scala.Tuple2;
class GroupByKeyTranslatorBatch<K, V>
- implements TransformTranslator<
- PTransform<PCollection<KV<K, V>>, PCollection<KV<K, Iterable<V>>>>> {
+ implements TransformTranslator<
+ PTransform<PCollection<KV<K, V>>, PCollection<KV<K, Iterable<V>>>>> {
@Override
public void translateTransform(
PTransform<PCollection<KV<K, V>>, PCollection<KV<K, Iterable<V>>>>
transform,
AbstractTranslationContext context) {
@SuppressWarnings("unchecked")
- final PCollection<KV<K, V>> inputPCollection = (PCollection<KV<K, V>>)
context.getInput();
- Dataset<WindowedValue<KV<K, V>>> input =
context.getDataset(inputPCollection);
- WindowingStrategy<?, ?> windowingStrategy =
inputPCollection.getWindowingStrategy();
- KvCoder<K, V> kvCoder = (KvCoder<K, V>) inputPCollection.getCoder();
- Coder<V> valueCoder = kvCoder.getValueCoder();
+ final PCollection<KV<K, V>> input = (PCollection<KV<K, V>>)
context.getInput();
+ @SuppressWarnings("unchecked")
+ final PCollection<KV<K, List<V>>> output = (PCollection<KV<K, List<V>>>)
context.getOutput();
+ @SuppressWarnings("unchecked")
Review comment:
I don't think the suppression is necessary here. We're constructing a
fresh object so it shouldn't count as an unchecked cast.
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