yihua commented on a change in pull request #4106:
URL: https://github.com/apache/hudi/pull/4106#discussion_r765187640
##########
File path:
hudi-client/hudi-client-common/src/main/java/org/apache/hudi/config/HoodieClusteringConfig.java
##########
@@ -544,30 +548,27 @@ public static BuildCurveStrategyType fromValue(String
value) {
}
/**
- * strategy types for optimize layout for hudi data.
+ * Layout optimization strategies such us Z-order, Hilbert space curves, etc
Review comment:
nit: typo `us`
##########
File path:
hudi-client/hudi-client-common/src/main/java/org/apache/hudi/config/HoodieClusteringConfig.java
##########
@@ -544,30 +548,27 @@ public static BuildCurveStrategyType fromValue(String
value) {
}
/**
- * strategy types for optimize layout for hudi data.
+ * Layout optimization strategies such us Z-order, Hilbert space curves, etc
*/
- public enum BuildLayoutOptimizationStrategy {
+ public enum LayoutOptimizationStrategy {
ZORDER("z-order"),
HILBERT("hilbert");
+
+ private static final Map<String, LayoutOptimizationStrategy>
VALUE_ENUM_MAP = createValueToEnumMap();
+
private final String value;
- BuildLayoutOptimizationStrategy(String value) {
+ LayoutOptimizationStrategy(String value) {
this.value = value;
}
- public String toCustomString() {
- return value;
+ public static LayoutOptimizationStrategy fromValue(String value) {
+ return VALUE_ENUM_MAP.get(value);
Review comment:
Do we want to throw a Hoodie exception here if the enum cannot be found?
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/index/columnstats/ColumnStatsIndexHelper.java
##########
@@ -621,4 +495,5 @@ public static String createIndexMergeSql(
String.format("%s.%s", newIndexTable, columns.get(0))
);
}
+
Review comment:
nit: remote empty line?
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/index/columnstats/ColumnStatsIndexHelper.java
##########
@@ -402,52 +297,52 @@ public static void updateZIndexFor(
.filter(f -> !completedCommits.contains(f))
.collect(Collectors.toList());
- Dataset<Row> finalZIndexDf;
-
- // Before writing out new version of the Z-index table we need to merge
it
+ Dataset<Row> finalColStatsIndexDf;
+
+ // Before writing out new version of the col-stats-index table we need
to merge it
// with the most recent one that were successfully persisted previously
if (validIndexTables.isEmpty()) {
- finalZIndexDf = newZIndexDf;
+ finalColStatsIndexDf = newColStatsIndexDf;
} else {
// NOTE: That Parquet schema might deviate from the original table
schema (for ex,
// by upcasting "short" to "integer" types, etc), and hence we
need to re-adjust it
// prior to merging, since merging might fail otherwise due to
schemas incompatibility
- finalZIndexDf =
+ finalColStatsIndexDf =
tryMergeMostRecentIndexTableInto(
sparkSession,
- newZIndexDf,
- // Load current most recent Z-index table
+ newColStatsIndexDf,
+ // Load current most recent col-stats-index table
sparkSession.read().load(
- new Path(zindexFolderPath,
validIndexTables.get(validIndexTables.size() - 1)).toString()
+ new Path(indexFolderPath,
validIndexTables.get(validIndexTables.size() - 1)).toString()
)
);
// Clean up all index tables (after creation of the new index)
tablesToCleanup.addAll(validIndexTables);
}
- // Persist new Z-index table
- finalZIndexDf
- .repartition(1)
- .write()
- .format("parquet")
- .save(newIndexTablePath.toString());
+ // Persist new col-stats-index table
+ finalColStatsIndexDf
+ .repartition(1)
+ .write()
+ .format("parquet")
+ .save(newIndexTablePath.toString());
Review comment:
I see some changes are formatting only. Are they from IDE auto
reformatting?
##########
File path:
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestSpaceCurveLayoutOptimization.scala
##########
@@ -119,106 +140,12 @@ class TestTableLayoutOptimization extends
HoodieClientTestBase {
)
}
- def assertRowsMatch(one: DataFrame, other: DataFrame) = {
+ private def assertRowsMatch(one: DataFrame, other: DataFrame) = {
val rows = one.count()
assert(rows == other.count() && one.intersect(other).count() == rows)
}
- @Test
Review comment:
Are these tests obsolete or they should still be kept?
##########
File path:
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestColumnStatsIndex.scala
##########
@@ -53,100 +43,25 @@ class TestZOrderLayoutOptimization extends
HoodieClientTestBase {
.add("c7", BinaryType)
.add("c8", ByteType)
- val commonOpts = Map(
- "hoodie.insert.shuffle.parallelism" -> "4",
- "hoodie.upsert.shuffle.parallelism" -> "4",
- "hoodie.bulkinsert.shuffle.parallelism" -> "4",
- DataSourceWriteOptions.RECORDKEY_FIELD.key() -> "_row_key",
- DataSourceWriteOptions.PARTITIONPATH_FIELD.key() -> "partition",
- DataSourceWriteOptions.PRECOMBINE_FIELD.key() -> "timestamp",
- HoodieWriteConfig.TBL_NAME.key -> "hoodie_test"
- )
-
@BeforeEach
override def setUp() {
initPath()
initSparkContexts()
- spark = sqlContext.sparkSession
- initTestDataGenerator()
initFileSystem()
+ spark = sqlContext.sparkSession
}
@AfterEach
override def tearDown() = {
- cleanupSparkContexts()
- cleanupTestDataGenerator()
cleanupFileSystem()
- }
-
- @ParameterizedTest
- @ValueSource(strings = Array("COPY_ON_WRITE", "MERGE_ON_READ"))
- def testZOrderingLayoutClustering(tableType: String): Unit = {
- val targetRecordsCount = 10000
- // Bulk Insert Operation
- val records = recordsToStrings(dataGen.generateInserts("001",
targetRecordsCount)).toList
- val writeDf: Dataset[Row] =
spark.read.json(spark.sparkContext.parallelize(records, 2))
-
- writeDf.write.format("org.apache.hudi")
- .options(commonOpts)
- .option("hoodie.compact.inline", "false")
- .option(DataSourceWriteOptions.OPERATION.key(),
DataSourceWriteOptions.BULK_INSERT_OPERATION_OPT_VAL)
- .option(DataSourceWriteOptions.TABLE_TYPE.key(), tableType)
- // option for clustering
- .option("hoodie.parquet.small.file.limit", "0")
- .option("hoodie.clustering.inline", "true")
- .option("hoodie.clustering.inline.max.commits", "1")
- .option("hoodie.clustering.plan.strategy.target.file.max.bytes",
"1073741824")
- .option("hoodie.clustering.plan.strategy.small.file.limit", "629145600")
- .option("hoodie.clustering.plan.strategy.max.bytes.per.group",
Long.MaxValue.toString)
- .option("hoodie.clustering.plan.strategy.target.file.max.bytes",
String.valueOf(64 * 1024 * 1024L))
- .option(HoodieClusteringConfig.LAYOUT_OPTIMIZE_ENABLE.key, "true")
- .option(HoodieClusteringConfig.PLAN_STRATEGY_SORT_COLUMNS.key,
"begin_lat, begin_lon")
- .mode(SaveMode.Overwrite)
- .save(basePath)
-
- val hudiMetaClient = HoodieTableMetaClient.builder
- .setConf(hadoopConf)
- .setBasePath(basePath)
- .setLoadActiveTimelineOnLoad(true)
- .build
-
- val lastCommit =
hudiMetaClient.getActiveTimeline.getAllCommitsTimeline.lastInstant().get()
-
- assertEquals(HoodieTimeline.REPLACE_COMMIT_ACTION, lastCommit.getAction)
- assertEquals(HoodieInstant.State.COMPLETED, lastCommit.getState)
-
- val readDf =
- spark.read
- .format("hudi")
- .load(basePath)
-
- val readDfSkip =
- spark.read
- .option(DataSourceReadOptions.ENABLE_DATA_SKIPPING.key(), "true")
- .format("hudi")
- .load(basePath)
-
- assertEquals(targetRecordsCount, readDf.count())
- assertEquals(targetRecordsCount, readDfSkip.count())
-
- readDf.createOrReplaceTempView("hudi_snapshot_raw")
- readDfSkip.createOrReplaceTempView("hudi_snapshot_skipping")
-
- def select(tableName: String) =
- spark.sql(s"SELECT * FROM $tableName WHERE begin_lat >= 0.49 AND
begin_lat < 0.51 AND begin_lon >= 0.49 AND begin_lon < 0.51")
-
- assertRowsMatch(
- select("hudi_snapshot_raw"),
- select("hudi_snapshot_skipping")
- )
+ cleanupSparkContexts()
}
@Test
@Disabled
Review comment:
Should this be reenabled?
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RDDSpatialCurveOptimizationSortPartitioner.java
##########
@@ -74,23 +81,43 @@ public
RDDSpatialCurveOptimizationSortPartitioner(HoodieSparkEngineContext spark
private JavaRDD<GenericRecord> prepareGenericRecord(JavaRDD<HoodieRecord<T>>
inputRecords, final int numOutputGroups, final Schema schema) {
SerializableSchema serializableSchema = new SerializableSchema(schema);
JavaRDD<GenericRecord> genericRecordJavaRDD = inputRecords.map(f ->
(GenericRecord) f.getData().getInsertValue(serializableSchema.get()).get());
- Dataset<Row> originDF =
AvroConversionUtils.createDataFrame(genericRecordJavaRDD.rdd(),
schema.toString(), sparkEngineContext.getSqlContext().sparkSession());
- Dataset<Row> zDataFrame;
+ Dataset<Row> originDF =
+ AvroConversionUtils.createDataFrame(
+ genericRecordJavaRDD.rdd(),
+ schema.toString(),
+ sparkEngineContext.getSqlContext().sparkSession()
+ );
+
+ Dataset<Row> sortedDF = reorder(originDF, numOutputGroups);
+
+ return HoodieSparkUtils.createRdd(sortedDF, schema.getName(),
+ schema.getNamespace(), false,
org.apache.hudi.common.util.Option.empty()).toJavaRDD();
+ }
+
+ private Dataset<Row> reorder(Dataset<Row> sourceDF, int numOutputGroups) {
+ String orderedColumnsListConfig = config.getClusteringSortColumns();
+
+ if (isNullOrEmpty(orderedColumnsListConfig) || numOutputGroups <= 0) {
+ // No-op
+ return sourceDF;
+ }
+
+ List<String> orderedCols =
+ Arrays.stream(orderedColumnsListConfig.split(","))
+ .map(String::trim)
+ .collect(Collectors.toList());
+
+ HoodieClusteringConfig.LayoutOptimizationStrategy layoutOptStrategy =
+
HoodieClusteringConfig.LayoutOptimizationStrategy.fromValue(config.getLayoutOptimizationStrategy());
switch (config.getLayoutOptimizationCurveBuildMethod()) {
case DIRECT:
- zDataFrame = OrderingIndexHelper
- .createOptimizedDataFrameByMapValue(originDF,
config.getClusteringSortColumns(), numOutputGroups,
config.getLayoutOptimizationStrategy());
- break;
+ return SpaceCurveSortingHelper.orderDataFrameByMappingValues(sourceDF,
layoutOptStrategy, orderedCols, numOutputGroups);
case SAMPLE:
- zDataFrame = OrderingIndexHelper
- .createOptimizeDataFrameBySample(originDF,
config.getClusteringSortColumns(), numOutputGroups,
config.getLayoutOptimizationStrategy());
- break;
+ return
SpaceCurveSortingHelper.orderDataFrameBySamplingValues(sourceDF,
layoutOptStrategy, orderedCols, numOutputGroups);
default:
throw new HoodieException("Not a valid build curve method for
doWriteOperation: ");
Review comment:
nit: this can print the `config.getLayoutOptimizationCurveBuildMethod()`
in the log message.
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/index/columnstats/ColumnStatsIndexHelper.java
##########
@@ -79,13 +71,11 @@
import java.util.stream.Collectors;
import java.util.stream.StreamSupport;
-import scala.collection.JavaConversions;
-
import static org.apache.hudi.util.DataTypeUtils.areCompatible;
-public class ZOrderingIndexHelper {
+public class ColumnStatsIndexHelper {
Review comment:
nit: (not a strong opinion) shall this be simply names as
`ColumnStatsHelper`?
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/sort/SpaceCurveSortingHelper.java
##########
@@ -0,0 +1,260 @@
+/*
+ * 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.sort;
+
+import org.apache.hudi.common.util.CollectionUtils;
+import org.apache.hudi.config.HoodieClusteringConfig;
+import org.apache.hudi.optimize.HilbertCurveUtils;
+import org.apache.hudi.optimize.ZOrderingUtil;
+import org.apache.log4j.LogManager;
+import org.apache.log4j.Logger;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Row$;
+import org.apache.spark.sql.hudi.execution.RangeSampleSort$;
+import org.apache.spark.sql.hudi.execution.ZorderingBinarySort;
+import org.apache.spark.sql.types.BinaryType;
+import org.apache.spark.sql.types.BinaryType$;
+import org.apache.spark.sql.types.BooleanType;
+import org.apache.spark.sql.types.ByteType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DateType;
+import org.apache.spark.sql.types.DecimalType;
+import org.apache.spark.sql.types.DoubleType;
+import org.apache.spark.sql.types.FloatType;
+import org.apache.spark.sql.types.IntegerType;
+import org.apache.spark.sql.types.LongType;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.ShortType;
+import org.apache.spark.sql.types.StringType;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.StructType$;
+import org.apache.spark.sql.types.TimestampType;
+import org.davidmoten.hilbert.HilbertCurve;
+import scala.collection.JavaConversions;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+public class SpaceCurveSortingHelper {
+
+ private static final Logger LOG =
LogManager.getLogger(SpaceCurveSortingHelper.class);
+
+ /**
+ * Orders provided {@link Dataset} by mapping values of the provided list of
columns
+ * {@code orderByCols} onto a specified space curve (Z-curve, Hilbert, etc)
+ *
+ * <p/>
+ * NOTE: Only support base data-types:
long,int,short,double,float,string,timestamp,decimal,date,byte.
+ * This method is more effective than {@link
#orderDataFrameBySamplingValues} leveraging
+ * data sampling instead of direct mapping
+ *
+ * @param df Spark {@link Dataset} holding data to be ordered
+ * @param orderByCols list of columns to be ordered by
+ * @param targetPartitionCount target number of output partitions
+ * @param layoutOptStrategy target layout optimization strategy
+ * @return a {@link Dataset} holding data ordered by mapping tuple of values
from provided columns
+ * onto a specified space-curve
+ */
+ public static Dataset<Row> orderDataFrameByMappingValues(
+ Dataset<Row> df,
+ HoodieClusteringConfig.LayoutOptimizationStrategy layoutOptStrategy,
+ List<String> orderByCols,
+ int targetPartitionCount
+ ) {
+ Map<String, StructField> columnsMap =
+ Arrays.stream(df.schema().fields())
+ .collect(Collectors.toMap(StructField::name, Function.identity()));
+
+ List<String> checkCols =
+ orderByCols.stream()
+ .filter(columnsMap::containsKey)
+ .collect(Collectors.toList());
+
+ if (orderByCols.size() != checkCols.size()) {
+ LOG.error(String.format("Trying to ordering over a column(s) not present
in the schema (%s); skipping", CollectionUtils.diff(orderByCols, checkCols)));
+ return df;
+ }
Review comment:
Does this check work with nested columns?
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/index/columnstats/ColumnStatsIndexHelper.java
##########
@@ -313,66 +207,67 @@ public static String getNumNullsColumnNameFor(String
colName) {
})
.filter(Objects::nonNull);
- StructType indexSchema = composeIndexSchema(zorderedColumnSchemas);
+ StructType indexSchema = composeIndexSchema(orderedColumnSchemas);
return sparkSession.createDataFrame(allMetaDataRDD, indexSchema);
}
/**
* <p/>
- * Updates state of the Z-index by:
+ * Updates state of the column-stats index by:
* <ol>
- * <li>Updating Z-index with statistics for {@code sourceBaseFiles},
collecting corresponding
- * column statistics from Parquet footers</li>
- * <li>Merging newly built Z-index table with the most recent one (if
present and not preempted)</li>
+ * <li>Updating column-stats index with statistics for {@code
sourceBaseFiles},
+ * collecting corresponding column statistics from Parquet footers</li>
+ * <li>Merging newly built column-stats index table with the most recent
one (if present
+ * and not preempted)</li>
* <li>Cleans up any residual index tables, that weren't cleaned up
before</li>
* </ol>
*
* @param sparkSession encompassing Spark session
* @param sourceTableSchema instance of {@link StructType} bearing source
table's writer's schema
* @param sourceBaseFiles list of base-files to be indexed
- * @param zorderedCols target Z-ordered columns
- * @param zindexFolderPath Z-index folder path
+ * @param orderedCols target ordered columns
+ * @param indexFolderPath col-stats index folder path
* @param commitTime current operation commit instant
* @param completedCommits all previously completed commit instants
*/
- public static void updateZIndexFor(
+ public static void updateColumnStatsIndexFor(
Review comment:
nit: if you decide to go with `ColumnStatsHelper`, this can be renames
as `updateColumnStatsFor()`.
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/execution/bulkinsert/RDDSpatialCurveOptimizationSortPartitioner.java
##########
@@ -74,23 +81,43 @@ public
RDDSpatialCurveOptimizationSortPartitioner(HoodieSparkEngineContext spark
private JavaRDD<GenericRecord> prepareGenericRecord(JavaRDD<HoodieRecord<T>>
inputRecords, final int numOutputGroups, final Schema schema) {
SerializableSchema serializableSchema = new SerializableSchema(schema);
JavaRDD<GenericRecord> genericRecordJavaRDD = inputRecords.map(f ->
(GenericRecord) f.getData().getInsertValue(serializableSchema.get()).get());
- Dataset<Row> originDF =
AvroConversionUtils.createDataFrame(genericRecordJavaRDD.rdd(),
schema.toString(), sparkEngineContext.getSqlContext().sparkSession());
- Dataset<Row> zDataFrame;
+ Dataset<Row> originDF =
+ AvroConversionUtils.createDataFrame(
+ genericRecordJavaRDD.rdd(),
+ schema.toString(),
+ sparkEngineContext.getSqlContext().sparkSession()
+ );
+
+ Dataset<Row> sortedDF = reorder(originDF, numOutputGroups);
+
+ return HoodieSparkUtils.createRdd(sortedDF, schema.getName(),
+ schema.getNamespace(), false,
org.apache.hudi.common.util.Option.empty()).toJavaRDD();
+ }
+
+ private Dataset<Row> reorder(Dataset<Row> sourceDF, int numOutputGroups) {
Review comment:
nit: we can pick one naming here, `originDF` vs `sourceDF`
##########
File path:
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestSpaceCurveLayoutOptimization.scala
##########
@@ -52,23 +61,25 @@ class TestTableLayoutOptimization extends
HoodieClientTestBase {
HoodieWriteConfig.TBL_NAME.key -> "hoodie_test"
)
- @BeforeEach override def setUp() {
+ @BeforeEach
+ override def setUp() {
initPath()
initSparkContexts()
spark = sqlContext.sparkSession
initTestDataGenerator()
initFileSystem()
}
- @AfterEach override def tearDown() = {
+ @AfterEach
+ override def tearDown() = {
cleanupSparkContexts()
cleanupTestDataGenerator()
cleanupFileSystem()
}
@ParameterizedTest
- @MethodSource(Array("testLayOutParameter"))
- def testOptimizewithClustering(tableType: String, optimizeMode: String):
Unit = {
+ @MethodSource(Array("testLayoutOptimizationParameters"))
+ def testZOrderingLayoutClustering(tableType: String): Unit = {
Review comment:
nit: rename to `testClusteringWithLayoutOptimization`?
##########
File path:
hudi-common/src/main/java/org/apache/hudi/common/table/HoodieTableMetaClient.java
##########
@@ -79,7 +79,7 @@
public static final String AUXILIARYFOLDER_NAME = METAFOLDER_NAME +
Path.SEPARATOR + ".aux";
public static final String BOOTSTRAP_INDEX_ROOT_FOLDER_PATH =
AUXILIARYFOLDER_NAME + Path.SEPARATOR + ".bootstrap";
public static final String HEARTBEAT_FOLDER_NAME = METAFOLDER_NAME +
Path.SEPARATOR + ".heartbeat";
- public static final String ZINDEX_NAME = ".zindex";
+ public static final String COLUMN_STATISTICS_INDEX_NAME = ".colstatsindex";
Review comment:
nit: similar here. Your call on `colstatsindex` vs `colstats`.
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/sort/SpaceCurveSortingHelper.java
##########
@@ -0,0 +1,260 @@
+/*
+ * 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.sort;
+
+import org.apache.hudi.common.util.CollectionUtils;
+import org.apache.hudi.config.HoodieClusteringConfig;
+import org.apache.hudi.optimize.HilbertCurveUtils;
+import org.apache.hudi.optimize.ZOrderingUtil;
+import org.apache.log4j.LogManager;
+import org.apache.log4j.Logger;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Row$;
+import org.apache.spark.sql.hudi.execution.RangeSampleSort$;
+import org.apache.spark.sql.hudi.execution.ZorderingBinarySort;
+import org.apache.spark.sql.types.BinaryType;
+import org.apache.spark.sql.types.BinaryType$;
+import org.apache.spark.sql.types.BooleanType;
+import org.apache.spark.sql.types.ByteType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DateType;
+import org.apache.spark.sql.types.DecimalType;
+import org.apache.spark.sql.types.DoubleType;
+import org.apache.spark.sql.types.FloatType;
+import org.apache.spark.sql.types.IntegerType;
+import org.apache.spark.sql.types.LongType;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.ShortType;
+import org.apache.spark.sql.types.StringType;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.StructType$;
+import org.apache.spark.sql.types.TimestampType;
+import org.davidmoten.hilbert.HilbertCurve;
+import scala.collection.JavaConversions;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+public class SpaceCurveSortingHelper {
+
+ private static final Logger LOG =
LogManager.getLogger(SpaceCurveSortingHelper.class);
+
+ /**
+ * Orders provided {@link Dataset} by mapping values of the provided list of
columns
+ * {@code orderByCols} onto a specified space curve (Z-curve, Hilbert, etc)
+ *
+ * <p/>
+ * NOTE: Only support base data-types:
long,int,short,double,float,string,timestamp,decimal,date,byte.
+ * This method is more effective than {@link
#orderDataFrameBySamplingValues} leveraging
+ * data sampling instead of direct mapping
+ *
+ * @param df Spark {@link Dataset} holding data to be ordered
+ * @param orderByCols list of columns to be ordered by
+ * @param targetPartitionCount target number of output partitions
+ * @param layoutOptStrategy target layout optimization strategy
+ * @return a {@link Dataset} holding data ordered by mapping tuple of values
from provided columns
+ * onto a specified space-curve
+ */
+ public static Dataset<Row> orderDataFrameByMappingValues(
+ Dataset<Row> df,
+ HoodieClusteringConfig.LayoutOptimizationStrategy layoutOptStrategy,
+ List<String> orderByCols,
+ int targetPartitionCount
+ ) {
+ Map<String, StructField> columnsMap =
+ Arrays.stream(df.schema().fields())
+ .collect(Collectors.toMap(StructField::name, Function.identity()));
+
+ List<String> checkCols =
+ orderByCols.stream()
+ .filter(columnsMap::containsKey)
+ .collect(Collectors.toList());
+
+ if (orderByCols.size() != checkCols.size()) {
+ LOG.error(String.format("Trying to ordering over a column(s) not present
in the schema (%s); skipping", CollectionUtils.diff(orderByCols, checkCols)));
+ return df;
+ }
+
+ // In case when there's just one column to be ordered by, we can skip
space-curve
+ // ordering altogether (since it will match linear ordering anyway)
+ if (orderByCols.size() == 1) {
+ String orderByColName = orderByCols.get(0);
+ LOG.debug(String.format("Single column to order by (%s), skipping
space-curve ordering", orderByColName));
+
+ // TODO validate if we need Spark to re-partition
+ return df.repartitionByRange(targetPartitionCount, new
Column(orderByColName));
+ }
+
+ int fieldNum = df.schema().fields().length;
+
+ Map<Integer, StructField> fieldMap =
+ orderByCols.stream()
+ .collect(
+ Collectors.toMap(e ->
Arrays.asList(df.schema().fields()).indexOf(columnsMap.get(e)),
columnsMap::get));
+
+ JavaRDD<Row> sortedRDD;
+ switch (layoutOptStrategy) {
+ case ZORDER:
+ sortedRDD = createZCurveSortedRDD(df.toJavaRDD(), fieldMap, fieldNum,
targetPartitionCount);
+ break;
+ case HILBERT:
+ sortedRDD = createHilbertSortedRDD(df.toJavaRDD(), fieldMap, fieldNum,
targetPartitionCount);
+ break;
+ default:
+ throw new IllegalArgumentException(String.format("new only support
z-order/hilbert optimize but find: %s", layoutOptStrategy));
+ }
+
+ // Compose new {@code StructType} for ordered RDDs
+ StructType newStructType = composeOrderedRDDStructType(df.schema());
+
+ return df.sparkSession()
+ .createDataFrame(sortedRDD, newStructType)
+ .drop("Index");
+ }
+
+ private static StructType composeOrderedRDDStructType(StructType schema) {
+ return StructType$.MODULE$.apply(
+ CollectionUtils.combine(
+ Arrays.asList(schema.fields()),
+ Arrays.asList(new StructField("Index", BinaryType$.MODULE$, true,
Metadata.empty()))
+ )
+ );
+ }
+
+ private static JavaRDD<Row> createZCurveSortedRDD(JavaRDD<Row> originRDD,
Map<Integer, StructField> fieldMap, int fieldNum, int fileNum) {
+ return originRDD.map(row -> {
+ List<byte[]> zBytesList = fieldMap.entrySet().stream().map(entry -> {
+ int index = entry.getKey();
+ StructField field = entry.getValue();
+ DataType dataType = field.dataType();
+ if (dataType instanceof LongType) {
+ return ZOrderingUtil.longTo8Byte(row.isNullAt(index) ?
Long.MAX_VALUE : row.getLong(index));
+ } else if (dataType instanceof DoubleType) {
+ return ZOrderingUtil.doubleTo8Byte(row.isNullAt(index) ?
Double.MAX_VALUE : row.getDouble(index));
+ } else if (dataType instanceof IntegerType) {
+ return ZOrderingUtil.intTo8Byte(row.isNullAt(index) ?
Integer.MAX_VALUE : row.getInt(index));
+ } else if (dataType instanceof FloatType) {
+ return ZOrderingUtil.doubleTo8Byte(row.isNullAt(index) ?
Float.MAX_VALUE : row.getFloat(index));
+ } else if (dataType instanceof StringType) {
+ return ZOrderingUtil.utf8To8Byte(row.isNullAt(index) ? "" :
row.getString(index));
+ } else if (dataType instanceof DateType) {
+ return ZOrderingUtil.longTo8Byte(row.isNullAt(index) ?
Long.MAX_VALUE : row.getDate(index).getTime());
+ } else if (dataType instanceof TimestampType) {
+ return ZOrderingUtil.longTo8Byte(row.isNullAt(index) ?
Long.MAX_VALUE : row.getTimestamp(index).getTime());
+ } else if (dataType instanceof ByteType) {
+ return ZOrderingUtil.byteTo8Byte(row.isNullAt(index) ?
Byte.MAX_VALUE : row.getByte(index));
+ } else if (dataType instanceof ShortType) {
+ return ZOrderingUtil.intTo8Byte(row.isNullAt(index) ?
Short.MAX_VALUE : row.getShort(index));
+ } else if (dataType instanceof DecimalType) {
+ return ZOrderingUtil.longTo8Byte(row.isNullAt(index) ?
Long.MAX_VALUE : row.getDecimal(index).longValue());
+ } else if (dataType instanceof BooleanType) {
+ boolean value = row.isNullAt(index) ? false : row.getBoolean(index);
+ return ZOrderingUtil.intTo8Byte(value ? 1 : 0);
+ } else if (dataType instanceof BinaryType) {
+ return ZOrderingUtil.paddingTo8Byte(row.isNullAt(index) ? new byte[]
{0} : (byte[]) row.get(index));
+ }
+ return null;
+ }).filter(f -> f != null).collect(Collectors.toList());
+ byte[][] zBytes = new byte[zBytesList.size()][];
+ for (int i = 0; i < zBytesList.size(); i++) {
+ zBytes[i] = zBytesList.get(i);
+ }
+ List<Object> zVaules = new ArrayList<>();
+
zVaules.addAll(scala.collection.JavaConverters.bufferAsJavaListConverter(row.toSeq().toBuffer()).asJava());
+ zVaules.add(ZOrderingUtil.interleaving(zBytes, 8));
+ return Row$.MODULE$.apply(JavaConversions.asScalaBuffer(zVaules));
+ })
+ .sortBy(f -> new ZorderingBinarySort((byte[]) f.get(fieldNum)), true,
fileNum);
+ }
+
+ private static JavaRDD<Row> createHilbertSortedRDD(JavaRDD<Row> originRDD,
Map<Integer, StructField> fieldMap, int fieldNum, int fileNum) {
+ return originRDD.mapPartitions(rows -> {
+ HilbertCurve hilbertCurve =
HilbertCurve.bits(63).dimensions(fieldMap.size());
+ return new Iterator<Row>() {
+
+ @Override
+ public boolean hasNext() {
+ return rows.hasNext();
+ }
+
+ @Override
+ public Row next() {
+ Row row = rows.next();
+ List<Long> longList = fieldMap.entrySet().stream().map(entry -> {
+ int index = entry.getKey();
+ StructField field = entry.getValue();
+ DataType dataType = field.dataType();
+ if (dataType instanceof LongType) {
+ return row.isNullAt(index) ? Long.MAX_VALUE : row.getLong(index);
+ } else if (dataType instanceof DoubleType) {
+ return row.isNullAt(index) ? Long.MAX_VALUE :
Double.doubleToLongBits(row.getDouble(index));
+ } else if (dataType instanceof IntegerType) {
+ return row.isNullAt(index) ? Long.MAX_VALUE :
(long)row.getInt(index);
+ } else if (dataType instanceof FloatType) {
+ return row.isNullAt(index) ? Long.MAX_VALUE :
Double.doubleToLongBits((double) row.getFloat(index));
+ } else if (dataType instanceof StringType) {
+ return row.isNullAt(index) ? Long.MAX_VALUE :
ZOrderingUtil.convertStringToLong(row.getString(index));
Review comment:
`createHilbertSortedRDD` also uses `ZOrderingUtil` APIs. So rename
`ZOrderingUtil` to be general?
During my testing of I'm confused by the Spark stages
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