alexeykudinkin commented on code in PR #5328:
URL: https://github.com/apache/hudi/pull/5328#discussion_r930466594
##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RDDPartitionSortPartitioner.java:
##########
@@ -20,46 +20,62 @@
import org.apache.hudi.common.model.HoodieRecord;
import org.apache.hudi.common.model.HoodieRecordPayload;
-import org.apache.hudi.table.BulkInsertPartitioner;
-
+import org.apache.hudi.common.table.HoodieTableConfig;
+import org.apache.hudi.common.util.collection.Pair;
+import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
-
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-
+import org.apache.spark.sql.HoodieJavaRDDUtils;
import scala.Tuple2;
+import java.util.Comparator;
+
/**
* A built-in partitioner that does local sorting for each RDD partition
- * after coalesce for bulk insert operation, corresponding to the
- * {@code BulkInsertSortMode.PARTITION_SORT} mode.
+ * after coalescing it to specified number of partitions.
+ * Corresponds to the {@link BulkInsertSortMode#PARTITION_SORT} mode.
*
* @param <T> HoodieRecordPayload type
*/
public class RDDPartitionSortPartitioner<T extends HoodieRecordPayload>
- implements BulkInsertPartitioner<JavaRDD<HoodieRecord<T>>> {
+ extends RepartitioningBulkInsertPartitionerBase<JavaRDD<HoodieRecord<T>>> {
+ public RDDPartitionSortPartitioner(HoodieTableConfig tableConfig) {
+ super(tableConfig);
+ }
+
+ @SuppressWarnings("unchecked")
@Override
public JavaRDD<HoodieRecord<T>> repartitionRecords(JavaRDD<HoodieRecord<T>>
records,
int
outputSparkPartitions) {
- return records.coalesce(outputSparkPartitions)
- .mapToPair(record ->
- new Tuple2<>(
- new StringBuilder()
- .append(record.getPartitionPath())
- .append("+")
- .append(record.getRecordKey())
- .toString(), record))
- .mapPartitions(partition -> {
- // Sort locally in partition
- List<Tuple2<String, HoodieRecord<T>>> recordList = new ArrayList<>();
- for (; partition.hasNext(); ) {
- recordList.add(partition.next());
- }
- Collections.sort(recordList, (o1, o2) -> o1._1.compareTo(o2._1));
- return recordList.stream().map(e -> e._2).iterator();
- });
+
+ // NOTE: Datasets being ingested into partitioned tables are additionally
re-partitioned to better
+ // align dataset's logical partitioning with expected table's
physical partitioning to
+ // provide for appropriate file-sizing and better control of the
number of files created.
+ //
+ // Please check out {@code GlobalSortPartitioner} java-doc for more
details
+ if (isPartitionedTable) {
+ PartitionPathRDDPartitioner partitioner =
+ new PartitionPathRDDPartitioner((pair) -> ((Pair<String, String>)
pair).getKey(), outputSparkPartitions);
+
+ // Both partition-path and record-key are extracted, since
+ // - Partition-path will be used for re-partitioning (as called out
above)
+ // - Record-key will be used for sorting the records w/in individual
partitions
+ return records.mapToPair(record -> new
Tuple2<>(Pair.of(record.getPartitionPath(), record.getRecordKey()), record))
+ // NOTE: We're sorting by (partition-path, record-key) pair to make
sure that in case
+ // when there are less Spark partitions (requested) than there
are physical partitions
+ // (in which case multiple physical partitions, will be
handled w/in single Spark
+ // partition) records w/in a single Spark partition are still
ordered first by
+ // partition-path, then record's key
+ .repartitionAndSortWithinPartitions(partitioner,
Comparator.naturalOrder())
+ .values();
+ } else {
+ JavaPairRDD<String, HoodieRecord<T>> kvPairsRDD =
+ records.coalesce(outputSparkPartitions).mapToPair(record -> new
Tuple2<>(record.getRecordKey(), record));
+
+ // NOTE: [[JavaRDD]] doesn't expose an API to do the sorting w/o
(re-)shuffling, as such
+ // we're relying on our own sequence to achieve that
+ return HoodieJavaRDDUtils.sortWithinPartitions(kvPairsRDD,
Comparator.naturalOrder()).values();
Review Comment:
We can't as it could spill
##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RDDPartitionSortPartitioner.java:
##########
@@ -20,46 +20,62 @@
import org.apache.hudi.common.model.HoodieRecord;
import org.apache.hudi.common.model.HoodieRecordPayload;
-import org.apache.hudi.table.BulkInsertPartitioner;
-
+import org.apache.hudi.common.table.HoodieTableConfig;
+import org.apache.hudi.common.util.collection.Pair;
+import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
-
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-
+import org.apache.spark.sql.HoodieJavaRDDUtils;
import scala.Tuple2;
+import java.util.Comparator;
+
/**
* A built-in partitioner that does local sorting for each RDD partition
- * after coalesce for bulk insert operation, corresponding to the
- * {@code BulkInsertSortMode.PARTITION_SORT} mode.
+ * after coalescing it to specified number of partitions.
+ * Corresponds to the {@link BulkInsertSortMode#PARTITION_SORT} mode.
*
* @param <T> HoodieRecordPayload type
*/
public class RDDPartitionSortPartitioner<T extends HoodieRecordPayload>
- implements BulkInsertPartitioner<JavaRDD<HoodieRecord<T>>> {
+ extends RepartitioningBulkInsertPartitionerBase<JavaRDD<HoodieRecord<T>>> {
+ public RDDPartitionSortPartitioner(HoodieTableConfig tableConfig) {
+ super(tableConfig);
+ }
+
+ @SuppressWarnings("unchecked")
@Override
public JavaRDD<HoodieRecord<T>> repartitionRecords(JavaRDD<HoodieRecord<T>>
records,
int
outputSparkPartitions) {
- return records.coalesce(outputSparkPartitions)
- .mapToPair(record ->
- new Tuple2<>(
- new StringBuilder()
- .append(record.getPartitionPath())
- .append("+")
- .append(record.getRecordKey())
- .toString(), record))
- .mapPartitions(partition -> {
- // Sort locally in partition
- List<Tuple2<String, HoodieRecord<T>>> recordList = new ArrayList<>();
- for (; partition.hasNext(); ) {
- recordList.add(partition.next());
- }
- Collections.sort(recordList, (o1, o2) -> o1._1.compareTo(o2._1));
- return recordList.stream().map(e -> e._2).iterator();
- });
+
+ // NOTE: Datasets being ingested into partitioned tables are additionally
re-partitioned to better
+ // align dataset's logical partitioning with expected table's
physical partitioning to
+ // provide for appropriate file-sizing and better control of the
number of files created.
+ //
+ // Please check out {@code GlobalSortPartitioner} java-doc for more
details
+ if (isPartitionedTable) {
+ PartitionPathRDDPartitioner partitioner =
+ new PartitionPathRDDPartitioner((pair) -> ((Pair<String, String>)
pair).getKey(), outputSparkPartitions);
+
+ // Both partition-path and record-key are extracted, since
+ // - Partition-path will be used for re-partitioning (as called out
above)
+ // - Record-key will be used for sorting the records w/in individual
partitions
+ return records.mapToPair(record -> new
Tuple2<>(Pair.of(record.getPartitionPath(), record.getRecordKey()), record))
+ // NOTE: We're sorting by (partition-path, record-key) pair to make
sure that in case
+ // when there are less Spark partitions (requested) than there
are physical partitions
+ // (in which case multiple physical partitions, will be
handled w/in single Spark
+ // partition) records w/in a single Spark partition are still
ordered first by
+ // partition-path, then record's key
+ .repartitionAndSortWithinPartitions(partitioner,
Comparator.naturalOrder())
Review Comment:
Fair call out. What's particularly troubling from your perspective?
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