danny0405 commented on code in PR #8684: URL: https://github.com/apache/hudi/pull/8684#discussion_r1227895765
########## hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/metadata/SparkHoodieMetadataBulkInsertPartitioner.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.metadata; + +import java.io.Serializable; +import java.util.ArrayList; +import java.util.Comparator; +import java.util.List; + +import org.apache.hudi.common.model.HoodieRecord; +import org.apache.hudi.common.util.ValidationUtils; +import org.apache.hudi.table.BulkInsertPartitioner; +import org.apache.spark.Partitioner; +import org.apache.spark.api.java.JavaRDD; + +import scala.Tuple2; + +/** + * A {@code BulkInsertPartitioner} implementation for Metadata Table to improve performance of initialization of metadata + * table partition when a very large number of records are inserted. + * + * This partitioner requires the records to be already tagged with location. + */ +public class SparkHoodieMetadataBulkInsertPartitioner implements BulkInsertPartitioner<JavaRDD<HoodieRecord>> { + final int numPartitions; + public SparkHoodieMetadataBulkInsertPartitioner(int numPartitions) { + this.numPartitions = numPartitions; + } + + private class FileGroupPartitioner extends Partitioner { + + @Override + public int getPartition(Object key) { + return ((Tuple2<Integer, String>)key)._1; + } + + @Override + public int numPartitions() { + return numPartitions; + } + } + + // FileIDs for the various partitions + private List<String> fileIDPfxs; + + /** + * Partition the records by their location. The number of partitions is determined by the number of MDT fileGroups being udpated rather than the + * specific value of outputSparkPartitions. + */ + @Override + public JavaRDD<HoodieRecord> repartitionRecords(JavaRDD<HoodieRecord> records, int outputSparkPartitions) { + Comparator<Tuple2<Integer, String>> keyComparator = + (Comparator<Tuple2<Integer, String>> & Serializable)(t1, t2) -> t1._2.compareTo(t2._2); + + // Partition the records by their file group + JavaRDD<HoodieRecord> partitionedRDD = records + // key by <file group index, record key>. The file group index is used to partition and the record key is used to sort within the partition. + .keyBy(r -> { + int fileGroupIndex = HoodieTableMetadataUtil.getFileGroupIndexFromFileId(r.getCurrentLocation().getFileId()); + return new Tuple2<>(fileGroupIndex, r.getRecordKey()); + }) + .repartitionAndSortWithinPartitions(new FileGroupPartitioner(), keyComparator) + .map(t -> t._2); + + fileIDPfxs = partitionedRDD.mapPartitions(recordItr -> { + // Due to partitioning, all record in the partition should have same fileID. So we only can get the fileID prefix from the first record. + List<String> fileIds = new ArrayList<>(1); + if (recordItr.hasNext()) { + HoodieRecord record = recordItr.next(); + final String fileID = HoodieTableMetadataUtil.getFileGroupPrefix(record.getCurrentLocation().getFileId()); + fileIds.add(fileID); + } else { + // FileGroupPartitioner returns a fixed number of partition as part of numPartitions(). In the special case that recordsRDD has fewer + // records than fileGroupCount, some of these partitions (corresponding to fileGroups) will not have any data. + // But we still need to return a fileID for use within {@code BulkInsertMapFunction} + fileIds.add(""); + } + return fileIds.iterator(); + }, true).collect(); + ValidationUtils.checkArgument(partitionedRDD.getNumPartitions() == fileIDPfxs.size(), + String.format("Generated fileIDPfxs (%d) are lesser in size than the partitions %d", fileIDPfxs.size(), partitionedRDD.getNumPartitions())); + + return partitionedRDD; + } + + @Override + public boolean arePartitionRecordsSorted() { + return true; + } + + @Override + public String getFileIdPfx(int partitionId) { + return fileIDPfxs.get(partitionId); Review Comment: > automatic estimation of the shard counts for each partition, that can be enhanced This may be a solution if we can make accurate estimation of the file group size. -- This is an automated message from the Apache Git Service. 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