satishkotha commented on a change in pull request #2263:
URL: https://github.com/apache/hudi/pull/2263#discussion_r532824235



##########
File path: 
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/table/action/cluster/SparkRunClusteringCommitActionExecutor.java
##########
@@ -0,0 +1,153 @@
+/*
+ * 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.table.action.cluster;
+
+import org.apache.avro.Schema;
+import org.apache.avro.generic.IndexedRecord;
+import org.apache.hadoop.fs.Path;
+import org.apache.hudi.avro.HoodieAvroUtils;
+import org.apache.hudi.avro.model.HoodieClusteringGroup;
+import org.apache.hudi.avro.model.HoodieClusteringPlan;
+import org.apache.hudi.client.SparkTaskContextSupplier;
+import org.apache.hudi.client.WriteStatus;
+import org.apache.hudi.client.common.HoodieEngineContext;
+import org.apache.hudi.client.common.HoodieSparkEngineContext;
+import org.apache.hudi.common.model.HoodieCommitMetadata;
+import org.apache.hudi.common.model.HoodieKey;
+import org.apache.hudi.common.model.HoodieRecord;
+import org.apache.hudi.common.model.HoodieRecordPayload;
+import org.apache.hudi.common.model.WriteOperationType;
+import org.apache.hudi.common.table.log.HoodieFileSliceReader;
+import org.apache.hudi.common.table.log.HoodieMergedLogRecordScanner;
+import org.apache.hudi.common.table.timeline.HoodieInstant;
+import org.apache.hudi.common.table.timeline.HoodieTimeline;
+import org.apache.hudi.common.util.ClusteringUtils;
+import org.apache.hudi.common.util.CommitUtils;
+import org.apache.hudi.common.util.Option;
+import org.apache.hudi.common.util.ReflectionUtils;
+import org.apache.hudi.common.util.collection.Pair;
+import org.apache.hudi.config.HoodieWriteConfig;
+import org.apache.hudi.exception.HoodieClusteringException;
+import org.apache.hudi.io.IOUtils;
+import org.apache.hudi.io.storage.HoodieFileReader;
+import org.apache.hudi.io.storage.HoodieFileReaderFactory;
+import org.apache.hudi.table.HoodieTable;
+import org.apache.hudi.table.action.HoodieWriteMetadata;
+import org.apache.hudi.table.action.cluster.strategy.RunClusteringStrategy;
+import org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor;
+import org.apache.log4j.LogManager;
+import org.apache.log4j.Logger;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+
+import java.io.IOException;
+import java.util.Collection;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+public class SparkRunClusteringCommitActionExecutor<T extends 
HoodieRecordPayload<T>>
+    extends BaseSparkCommitActionExecutor<T> {
+
+  private static final Logger LOG = 
LogManager.getLogger(SparkRunClusteringCommitActionExecutor.class);
+  private final HoodieClusteringPlan clusteringPlan;
+
+  public SparkRunClusteringCommitActionExecutor(HoodieEngineContext context,
+                                                HoodieWriteConfig config, 
HoodieTable table,
+                                                String instantTime) {
+    super(context, config, table, instantTime, WriteOperationType.CLUSTER);
+    this.clusteringPlan = 
ClusteringUtils.getClusteringPlan(table.getMetaClient(), 
HoodieTimeline.getReplaceCommitRequestedInstant(instantTime))
+      .map(Pair::getRight).orElseThrow(() -> new 
HoodieClusteringException("Unable to read clustering plan for instant: " + 
instantTime));
+  }
+
+  @Override
+  public HoodieWriteMetadata<JavaRDD<WriteStatus>> execute() {
+    HoodieInstant instant = 
HoodieTimeline.getReplaceCommitRequestedInstant(instantTime);
+    // Mark instant as clustering inflight
+    table.getActiveTimeline().transitionReplaceRequestedToInflight(instant, 
Option.empty());
+    table.getMetaClient().reloadActiveTimeline();
+
+    JavaSparkContext engineContext = 
HoodieSparkEngineContext.getSparkContext(context);
+    // read rdd from input groups in plan
+    JavaRDD<WriteStatus> writeStatuses = 
clusteringPlan.getInputGroups().stream()
+        .map(inputGroup -> runClusteringForGroup(inputGroup, 
clusteringPlan.getStrategy().getStrategyParams()))
+        .reduce((rdd1, rdd2) -> engineContext.union(rdd1, 
rdd2)).orElse(engineContext.emptyRDD());
+    if (writeStatuses.isEmpty()) {
+      throw new HoodieClusteringException("Clustering plan produced 0 
WriteStatus for " + instantTime + " #groups: " + 
clusteringPlan.getInputGroups().size());
+    }
+    // merge all write status
+    HoodieWriteMetadata<JavaRDD<WriteStatus>> writeMetadata = 
buildWriteMetadata(writeStatuses);
+    updateIndexAndCommitIfNeeded(writeStatuses, writeMetadata);
+    if (!writeMetadata.getCommitMetadata().isPresent()) {
+      HoodieCommitMetadata commitMetadata = 
CommitUtils.buildMetadata(writeStatuses.map(WriteStatus::getStat).collect(), 
writeMetadata.getPartitionToReplaceFileIds(),
+          extraMetadata, operationType, getSchemaToStoreInCommit(), 
getCommitActionType());
+      writeMetadata.setCommitMetadata(Option.of(commitMetadata));
+    }
+    return writeMetadata;
+  }
+
+  private JavaRDD<WriteStatus> runClusteringForGroup(HoodieClusteringGroup 
clusteringGroup, Map<String, String> strategyParams) {

Review comment:
       Is there an alternative to union? Should we collect individual RDD and 
merge lists? I did testing only with 2 groups so far. So, I didn't see big 
performance degradation. I'll look into testing large number of groups as 
separate task. (We may also don't want number of groups to be really high as we 
want clustering to be atomic operation. Doing large amount of data/groups can 
increase chance of failures and slow down entire process.)




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