yihua commented on a change in pull request #4106: URL: https://github.com/apache/hudi/pull/4106#discussion_r765217559
########## 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 Hilbert Curve I'm confused by the Spark stage naming using `ZOrderingUtil`. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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