the-other-tim-brown commented on code in PR #13976:
URL: https://github.com/apache/hudi/pull/13976#discussion_r2391585279
##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/client/StreamingMetadataWriteHandler.java:
##########
@@ -87,12 +97,24 @@ public void commitToMetadataTable(HoodieTable table,
private HoodieData<WriteStatus>
streamWriteToMetadataTable(HoodieData<WriteStatus> dataTableWriteStatuses,
HoodieTableMetadataWriter metadataWriter,
HoodieTable table,
- String
instantTime) {
- HoodieData<WriteStatus> allWriteStatus = dataTableWriteStatuses;
+ String
instantTime,
+ boolean
enforceCoalesceWithRepartition,
+ int
coalesceDividentForDataTableWrites) {
HoodieData<WriteStatus> mdtWriteStatuses =
metadataWriter.streamWriteToMetadataPartitions(dataTableWriteStatuses,
instantTime);
- allWriteStatus = allWriteStatus.union(mdtWriteStatuses);
- allWriteStatus.persist("MEMORY_AND_DISK_SER", table.getContext(),
HoodieData.HoodieDataCacheKey.of(table.getMetaClient().getBasePath().toString(),
instantTime));
- return allWriteStatus;
+ mdtWriteStatuses.persist("MEMORY_AND_DISK_SER", table.getContext(),
HoodieData.HoodieDataCacheKey.of(table.getMetaClient().getBasePath().toString(),
instantTime));
+ HoodieData<WriteStatus> coalescedDataWriteStatuses;
+ int coalesceParallelism = Math.max(1,
dataTableWriteStatuses.getNumPartitions() / coalesceDividentForDataTableWrites);
+ if (enforceCoalesceWithRepartition) {
+ // with bulk insert and NONE sort mode, simple coalesce on datatable
write statuses also impact record key generation stages.
+ // and hence we are adding a partitioner to cut the chain so that
coalesce(1) here does not impact record key generation stages.
+ coalescedDataWriteStatuses =
HoodieJavaRDD.of(HoodieJavaRDD.getJavaRDD(dataTableWriteStatuses)
+ .mapToPair((PairFunction<WriteStatus, Boolean, WriteStatus>)
writeStatus -> new Tuple2(true, writeStatus))
Review Comment:
The method is `public int getPartition(Object key)` which implies that the
key is used. The key here is `true` due to `new Tuple2(true, writeStatus)`. If
you set a breakpoint in the `CoalescingPartitioner` you will see this.
##########
hudi-client/hudi-spark-client/src/test/java/org/apache/hudi/client/TestCoalescingPartitioner.java:
##########
@@ -0,0 +1,111 @@
+/*
+ * 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.hudi.client;
+
+import org.apache.hudi.common.data.HoodieData;
+import org.apache.hudi.data.HoodieJavaRDD;
+import org.apache.hudi.testutils.HoodieClientTestBase;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.Arguments;
+import org.junit.jupiter.params.provider.MethodSource;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+import java.util.stream.Stream;
+
+import scala.Tuple2;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+public class TestCoalescingPartitioner extends HoodieClientTestBase {
+
+ @Test
+ public void simpleCoalescingPartitionerTest() {
+ int numPartitions = 100;
+ HoodieData<Integer> rddData = HoodieJavaRDD.of(jsc.parallelize(
+ IntStream.rangeClosed(0, 100).boxed().collect(Collectors.toList()),
numPartitions));
+
+ // 100 keys spread across 10 partitions.
+ CoalescingPartitioner coalescingPartitioner = new
CoalescingPartitioner(10);
+ assertEquals(10, coalescingPartitioner.numPartitions());
+ rddData.collectAsList().forEach(entry -> {
+ assertEquals(entry.hashCode() % 10,
coalescingPartitioner.getPartition(entry));
+ });
+
+ // 1 partition
+ CoalescingPartitioner coalescingPartitioner1 = new
CoalescingPartitioner(1);
+ assertEquals(1, coalescingPartitioner1.numPartitions());
+ rddData.collectAsList().forEach(entry -> {
+ assertEquals(0, coalescingPartitioner1.getPartition(entry));
+ });
+
+ // empty rdd
+ rddData = HoodieJavaRDD.of(jsc.emptyRDD());
+ CoalescingPartitioner coalescingPartitioner2 = new
CoalescingPartitioner(1);
+ assertEquals(1, coalescingPartitioner2.numPartitions());
+ rddData.collectAsList().forEach(entry -> {
+ // since there is only one partition, any getPartition will return just
the same partition index
+ assertEquals(0, coalescingPartitioner2.getPartition(entry));
+ });
+ }
+
+ private static Stream<Arguments> coalesceTestArgs() {
+ return Arrays.stream(new Object[][] {
+ {100, 1},
+ {1, 1},
+ {1000, 10},
+ {100, 7},
+ {10, 2}
+ }).map(Arguments::of);
+ }
+
+ @ParameterizedTest
+ @MethodSource("coalesceTestArgs")
+ public void testCoalescingPartitionerWithRDD(int inputNumPartitions, int
targetPartitions) {
+ List<Integer> inputData = IntStream.rangeClosed(0,
inputNumPartitions).boxed().collect(Collectors.toList());
+ JavaRDD<Integer> data = jsc.parallelize(inputData, inputNumPartitions);
+ JavaRDD<Integer> coalescedData = data.mapToPair(new
PairFunc()).partitionBy(new CoalescingPartitioner(targetPartitions)).map(new
MapFunc());
+
+ assertEquals(targetPartitions, coalescedData.getNumPartitions());
Review Comment:
We should check for a relatively even spread amongst the partitions. I wrote
some local code to validate that the partitioner is unfortunately writing all
data to the same spark partition:
```
List<Pair<Integer, Integer>> results =
coalescedData.mapPartitionsWithIndex((i, rows) -> {
int count = 0;
while (rows.hasNext()) {
rows.next();
count++;
}
return Collections.singletonList(Pair.of(i, count)).iterator();
}, true).collect();
```
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