[GitHub] [hudi] alexeykudinkin commented on a diff in pull request #5328: [HUDI-3883] Fix Bulk Insert to repartition the dataset based on Partition Path

[GitBox <gi...@apache.org>]

alexeykudinkin commented on code in PR #5328:
URL: https://github.com/apache/hudi/pull/5328#discussion_r928092517


##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/BulkInsertInternalPartitionerFactory.java:
##########
@@ -27,16 +28,18 @@
  */
 public abstract class BulkInsertInternalPartitionerFactory {
 
-  public static BulkInsertPartitioner get(BulkInsertSortMode sortMode) {
-    switch (sortMode) {
+  public static BulkInsertPartitioner get(BulkInsertSortMode bulkInsertMode, HoodieTableConfig tableConfig) {

Review Comment:
   There are modes which have nothing to do with sorting (re-partitioning, for ex), so long-term we should strip the sort part of it



##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RepartitioningBulkInsertPartitionerBase.java:
##########
@@ -0,0 +1,62 @@
+/*
+ * 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.execution.bulkinsert;
+
+import org.apache.hudi.common.function.SerializableFunctionUnchecked;
+import org.apache.hudi.common.table.HoodieTableConfig;
+import org.apache.hudi.table.BulkInsertPartitioner;
+import org.apache.spark.Partitioner;
+
+import java.io.Serializable;
+import java.util.Objects;
+
+/**
+ * Base class for any {@link BulkInsertPartitioner} implementation that does re-partitioning,
+ * to better align "logical" (query-engine's partitioning of the incoming dataset) w/ the table's
+ * "physical" partitioning
+ */
+public abstract class RepartitioningBulkInsertPartitionerBase<I> implements BulkInsertPartitioner<I> {
+
+  protected final boolean isPartitionedTable;
+
+  public RepartitioningBulkInsertPartitionerBase(HoodieTableConfig tableConfig) {

Review Comment:
   Good call. I thought about it initially, but then decided that it's better to abstract this handling w/in the partitioner rather than pushing it onto the caller. LMK what you think.



##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RepartitioningBulkInsertPartitionerBase.java:
##########
@@ -0,0 +1,62 @@
+/*
+ * 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.execution.bulkinsert;
+
+import org.apache.hudi.common.function.SerializableFunctionUnchecked;
+import org.apache.hudi.common.table.HoodieTableConfig;
+import org.apache.hudi.table.BulkInsertPartitioner;
+import org.apache.spark.Partitioner;
+
+import java.io.Serializable;
+import java.util.Objects;
+
+/**
+ * Base class for any {@link BulkInsertPartitioner} implementation that does re-partitioning,
+ * to better align "logical" (query-engine's partitioning of the incoming dataset) w/ the table's
+ * "physical" partitioning
+ */
+public abstract class RepartitioningBulkInsertPartitionerBase<I> implements BulkInsertPartitioner<I> {
+
+  protected final boolean isPartitionedTable;
+
+  public RepartitioningBulkInsertPartitionerBase(HoodieTableConfig tableConfig) {
+    this.isPartitionedTable = tableConfig.getPartitionFields().map(pfs -> pfs.length > 0).orElse(false);
+  }
+
+  protected static class PartitionPathRDDPartitioner extends Partitioner implements Serializable {
+    private final SerializableFunctionUnchecked<Object, String> partitionPathExtractor;
+    private final int numPartitions;
+
+    PartitionPathRDDPartitioner(SerializableFunctionUnchecked<Object, String> partitionPathExtractor, int numPartitions) {
+      this.partitionPathExtractor = partitionPathExtractor;
+      this.numPartitions = numPartitions;
+    }
+
+    @Override
+    public int numPartitions() {
+      return numPartitions;
+    }
+
+    @SuppressWarnings("unchecked")
+    @Override
+    public int getPartition(Object o) {
+      return Math.abs(Objects.hash(partitionPathExtractor.apply(o))) % numPartitions;

Review Comment:
   Not sure i follow your train of thought: that's the whole idea of such partitioners (parittion-sort and partitioner-no-sort) to be able to partition the data to be better aligned with physical partitioning, right? 
   
   In case data is heavily skewed into most recent partition, it shouldn't be handled with this partitioner.



##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RowCustomColumnsSortPartitioner.java:
##########
@@ -19,42 +19,69 @@
 package org.apache.hudi.execution.bulkinsert;
 
 import org.apache.hudi.common.model.HoodieRecord;
+import org.apache.hudi.common.table.HoodieTableConfig;
 import org.apache.hudi.config.HoodieWriteConfig;
-import org.apache.hudi.table.BulkInsertPartitioner;
+import org.apache.spark.sql.Column;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
+import scala.collection.JavaConverters;
 
 import java.util.Arrays;
+import java.util.stream.Collectors;
+
+import static org.apache.hudi.common.util.ValidationUtils.checkState;
+import static org.apache.hudi.execution.bulkinsert.RDDCustomColumnsSortPartitioner.getOrderByColumnNames;
 
 /**
- * A partitioner that does sorting based on specified column values for each spark partitions.
+ * A partitioner that does local sorting for each RDD partition based on the tuple of
+ * values of the columns configured for ordering.
  */
-public class RowCustomColumnsSortPartitioner implements BulkInsertPartitioner<Dataset<Row>> {
+public class RowCustomColumnsSortPartitioner extends RepartitioningBulkInsertPartitionerBase<Dataset<Row>> {
+
+  private final String[] orderByColumnNames;
 
-  private final String[] sortColumnNames;
+  public RowCustomColumnsSortPartitioner(HoodieWriteConfig config, HoodieTableConfig tableConfig) {
+    super(tableConfig);
+    this.orderByColumnNames = getOrderByColumnNames(config);
 
-  public RowCustomColumnsSortPartitioner(HoodieWriteConfig config) {
-    this.sortColumnNames = getSortColumnName(config);
+    checkState(orderByColumnNames.length > 0);
   }
 
-  public RowCustomColumnsSortPartitioner(String[] columnNames) {
-    this.sortColumnNames = columnNames;
+  public RowCustomColumnsSortPartitioner(String[] columnNames, HoodieTableConfig tableConfig) {
+    super(tableConfig);
+    this.orderByColumnNames = columnNames;
+
+    checkState(orderByColumnNames.length > 0);
   }
 
   @Override
-  public Dataset<Row> repartitionRecords(Dataset<Row> records, int outputSparkPartitions) {
-    final String[] sortColumns = this.sortColumnNames;
-    return records.coalesce(outputSparkPartitions)
-        .sortWithinPartitions(HoodieRecord.PARTITION_PATH_METADATA_FIELD, sortColumns);
+  public Dataset<Row> repartitionRecords(Dataset<Row> dataset, int outputSparkPartitions) {
+    Dataset<Row> repartitionedDataset;
+
+    // NOTE: In case of partitioned table even "global" ordering (across all RDD partitions) could
+    //       not change table's partitioning and therefore there's no point in doing global sorting
+    //       across "physical" partitions, and instead we can reduce total amount of data being
+    //       shuffled by doing do "local" sorting:
+    //          - First, re-partitioning dataset such that "logical" partitions are aligned w/
+    //          "physical" ones
+    //          - Sorting locally w/in RDD ("logical") partitions
+    //
+    //       Non-partitioned tables will be globally sorted.
+    if (isPartitionedTable) {
+      repartitionedDataset = dataset.repartition(outputSparkPartitions, new Column(HoodieRecord.PARTITION_PATH_METADATA_FIELD));

Review Comment:
   It will if the dataset wasn't partitioned before, but it brings the benefits of the properly sized files (b/c whole physical partition will be written by a single executor)



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