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https://issues.apache.org/jira/browse/BEAM-5392?focusedWorklogId=195595&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-195595
]
ASF GitHub Bot logged work on BEAM-5392:
----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Feb/19 11:07
Start Date: 07/Feb/19 11:07
Worklog Time Spent: 10m
Work Description: iemejia commented on pull request #7601: [BEAM-5392]
GroupByKey optimized for non-merging windows
URL: https://github.com/apache/beam/pull/7601#discussion_r254636383
##########
File path:
runners/spark/src/test/java/org/apache/beam/runners/spark/translation/GroupNonMergingWindowsFunctionsTest.java
##########
@@ -0,0 +1,133 @@
+/*
+ * 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;
+
+import static org.junit.Assert.assertEquals;
+
+import java.util.Arrays;
+import java.util.Iterator;
+import java.util.List;
+import org.apache.beam.runners.spark.coders.CoderHelpers;
+import
org.apache.beam.runners.spark.translation.GroupNonMergingWindowsFunctions.GroupByKeyIterator;
+import
org.apache.beam.runners.spark.translation.GroupNonMergingWindowsFunctions.WindowedKey;
+import org.apache.beam.sdk.coders.BigEndianIntegerCoder;
+import org.apache.beam.sdk.coders.ByteArrayCoder;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.coders.StringUtf8Coder;
+import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
+import org.apache.beam.sdk.transforms.windowing.GlobalWindows;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.util.WindowedValue.FullWindowedValueCoder;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.WindowingStrategy;
+import org.joda.time.Instant;
+import org.junit.Assert;
+import org.junit.Test;
+import scala.Tuple2;
+
+/** Unit tests of {@link GroupNonMergingWindowsFunctions}. */
+public class GroupNonMergingWindowsFunctionsTest {
+
+ @Test
+ public void testGroupByKeyIterator() {
+ GroupByKeyIterator<String, Integer, GlobalWindow> iteratorUnderTest =
createGbkIterator();
+
+ Assert.assertTrue(iteratorUnderTest.hasNext());
+ WindowedValue<KV<String, Iterable<Integer>>> k1Win =
iteratorUnderTest.next();
+ // testing that calling 2x hasNext doesn't move to next key iterator
+ Assert.assertTrue(iteratorUnderTest.hasNext());
+ Assert.assertTrue(iteratorUnderTest.hasNext());
+
+ Iterator<Integer> valuesIteratorForK1 =
k1Win.getValue().getValue().iterator();
+
+ Assert.assertTrue("Now we expect first value for K1 to pop up.",
valuesIteratorForK1.hasNext());
+ assertEquals(1L, valuesIteratorForK1.next().longValue());
+ Assert.assertTrue(valuesIteratorForK1.hasNext());
+ Assert.assertTrue(valuesIteratorForK1.hasNext());
+ assertEquals(2L, valuesIteratorForK1.next().longValue());
+
+ WindowedValue<KV<String, Iterable<Integer>>> k2Win =
iteratorUnderTest.next();
+ Iterator<Integer> valuesIteratorForK2 =
k2Win.getValue().getValue().iterator();
+ assertEquals(3L, valuesIteratorForK2.next().longValue());
+ }
+
+ @Test(expected = IllegalStateException.class)
+ public void testGbkIteratorValuesCannotBeReiterated() {
+ GroupByKeyIterator<String, Integer, GlobalWindow> iteratorUnderTest =
createGbkIterator();
+ WindowedValue<KV<String, Iterable<Integer>>> firstEl =
iteratorUnderTest.next();
+ Iterable<Integer> value = firstEl.getValue().getValue();
+ for (Integer i : value) {
Review comment:
I wonder if there is a cleaner way to do this so static analysis tools do
not complain about the empt[y loop and unused variable, but I could not find
quickly and alternative.
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Issue Time Tracking
-------------------
Worklog Id: (was: 195595)
Time Spent: 1.5h (was: 1h 20m)
> GroupByKey on Spark: All values for a single key need to fit in-memory at once
> ------------------------------------------------------------------------------
>
> Key: BEAM-5392
> URL: https://issues.apache.org/jira/browse/BEAM-5392
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Affects Versions: 2.6.0
> Reporter: David Moravek
> Assignee: David Moravek
> Priority: Major
> Labels: performance, triaged
> Time Spent: 1.5h
> Remaining Estimate: 0h
>
> Currently, when using GroupByKey, all values for a single key need to fit
> in-memory at once.
>
> There are following issues, that need to be addressed:
> a) We can not use Spark's _groupByKey_, because it requires all values to fit
> in memory for a single key (it is implemented as "list combiner")
> b) _ReduceFnRunner_ iterates over values multiple times in order to group
> also by window
>
> Solution:
>
> In Dataflow Worker code, there are optimized versions of ReduceFnRunner, that
> can take advantage of having elements for a single key sorted by timestamp.
>
> We can use Spark's `{{repartitionAndSortWithinPartitions}}` in order to meet
> this constraint.
>
> For non-merging windows, we can put window itself into the key resulting in
> smaller groupings.
>
> This approach was already tested in ~100TB input scale on Spark 2.3.x.
> (custom Spark runner).
>
> I'll submit a patch once the Dataflow Worker code donation is complete.
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