fanyue-xia commented on code in PR #45971: URL: https://github.com/apache/spark/pull/45971#discussion_r1565133720
########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,219 @@ +/* + * 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.spark.sql.streaming + +import java.io.{BufferedWriter, DataInputStream, DataOutputStream, File, FileInputStream, FileOutputStream, FileWriter} + +import scala.io.Source +import scala.util.Random + +import com.google.common.io.ByteStreams + +import org.apache.spark.SparkConf +import org.apache.spark.io.CompressionCodec +import org.apache.spark.sql.{RandomDataGenerator, Row} +import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.expressions.{BoundReference, GenericInternalRow, UnsafeProjection, UnsafeRow} +import org.apache.spark.sql.catalyst.plans.physical._ +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{BinaryType, DataType, DoubleType, FloatType, IntegerType, StringType, StructType} + +class StreamingQueryHashPartitionVerifySuite extends StreamTest { + override protected def sparkConf: SparkConf = + super.sparkConf + .set(SQLConf.SHUFFLE_PARTITIONS.key, numShufflePartitions.toString) + + // Configs for golden file + private val goldenFileURI = + this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + private val schemaFileName = "randomSchemas" // files for storing random input schemas + private val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + private val codec = CompressionCodec.createCodec( + sparkConf) // Used for compressing output to rowAndPartId file + + // Configs for random schema generation + private val variableTypes = Seq(IntegerType, DoubleType, FloatType, BinaryType, StringType) + private val numSchemaTypes = 1 + private val maxNumFields = 20 + + // Configs for shuffle + private val numRows = 10000 + private val numShufflePartitions = 100 + private def saveSchemas(schemas: Seq[StructType]) = { + val writer = new BufferedWriter(new FileWriter(new File(goldenFileURI.getPath, schemaFileName))) + schemas.foreach { schema => + writer.write(schema.toDDL) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try { + source + .getLines() + .map { ddl => + StructType.fromDDL(ddl) + } + .toArray // Avoid Stream lazy materialization + .toSeq + } finally source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + // Save the total number of rows + os.writeInt(rows.length) + // Save all rows + rows.foreach { row => + // Save the row's total number of bytes + val rowBytes = row.getBytes() + // Save the row's actual bytes + os.writeInt(rowBytes.size) + os.write(rowBytes) + } + // Save all partIds, which should be in the same order as rows + partIds.foreach { id => + os.writeInt(id) + } + } + + private def readRowsAndPartIds(is: DataInputStream): (Seq[UnsafeRow], Seq[Int]) = { + val numRows = is.readInt() + val rows = (1 to numRows).map { _ => + val rowSize = is.readInt() + val rowBuffer = new Array[Byte](rowSize) + ByteStreams.readFully(is, rowBuffer, 0, rowSize) + val row = new UnsafeRow(1) + row.pointTo(rowBuffer, rowSize) + row + } + val partIds = (1 to numRows).map(_ => is.readInt()).toArray.toSeq + (rows, partIds) + } + + private def getRandomRows(schema: StructType, numRows: Int, rand: Random): Seq[UnsafeRow] = { + val generator = RandomDataGenerator + .forType( + schema, + rand = new Random(rand.nextInt()) + ) + .get + + // Create the converters needed to convert from external row to internal + // row and to UnsafeRows. Projection itself costs a lot on initialization + // (codegen and compile), so initialize it once. + val internalConverter = CatalystTypeConverters.createToCatalystConverter(schema) + val unsafeConverter = UnsafeProjection.create(Array(schema).asInstanceOf[Array[DataType]]) + + (1 to numRows).map { _ => + val row = generator().asInstanceOf[Row] + + val internalRow = new GenericInternalRow(1) + internalRow.update(0, internalConverter(row).asInstanceOf[GenericInternalRow]) + val unsafeRow = unsafeConverter.apply(internalRow) + + // UnsafeProjection returns the same UnsafeRow instance intentionally, so + // unless doing deep copy, the hash partitions below will evaluate on the + // same row thus return same value. + unsafeRow.copy() + } + } + + private def getRandomSchemas(rand: Random): Seq[StructType] = { + (1 to numSchemaTypes).map { _ => + RandomDataGenerator.randomNestedSchema(rand, maxNumFields, variableTypes) + } + } + + private def getPartitionId(rows: Seq[UnsafeRow], hash: HashPartitioning): Seq[Int] = { + val partIdExpr = hash.partitionIdExpression + rows.map { row => + partIdExpr.eval(row).asInstanceOf[Int] + } + } + + test("SPARK-47788: Ensure the same hash function is used across batches.") { + val rowAndPartIdFile = new File(goldenFileURI.getPath, rowAndPartIdFilename) + + if (regenerateGoldenFiles) { + // To limit the golden file size under 10Mb, please set the final val MAX_STR_LEN: Int = 100 + // and final val MAX_ARR_SIZE: Int = 4 in org.apache.spark.sql.RandomDataGenerator + + val seed = Random.nextInt() + val random = new Random(seed) Review Comment: Thanks for the suggestion. ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,219 @@ +/* + * 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.spark.sql.streaming + +import java.io.{BufferedWriter, DataInputStream, DataOutputStream, File, FileInputStream, FileOutputStream, FileWriter} + +import scala.io.Source +import scala.util.Random + +import com.google.common.io.ByteStreams + +import org.apache.spark.SparkConf +import org.apache.spark.io.CompressionCodec +import org.apache.spark.sql.{RandomDataGenerator, Row} +import org.apache.spark.sql.catalyst.CatalystTypeConverters +import org.apache.spark.sql.catalyst.expressions.{BoundReference, GenericInternalRow, UnsafeProjection, UnsafeRow} +import org.apache.spark.sql.catalyst.plans.physical._ +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{BinaryType, DataType, DoubleType, FloatType, IntegerType, StringType, StructType} + +class StreamingQueryHashPartitionVerifySuite extends StreamTest { + override protected def sparkConf: SparkConf = + super.sparkConf + .set(SQLConf.SHUFFLE_PARTITIONS.key, numShufflePartitions.toString) + + // Configs for golden file + private val goldenFileURI = + this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + private val schemaFileName = "randomSchemas" // files for storing random input schemas + private val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + private val codec = CompressionCodec.createCodec( + sparkConf) // Used for compressing output to rowAndPartId file + + // Configs for random schema generation + private val variableTypes = Seq(IntegerType, DoubleType, FloatType, BinaryType, StringType) + private val numSchemaTypes = 1 + private val maxNumFields = 20 + + // Configs for shuffle + private val numRows = 10000 + private val numShufflePartitions = 100 + private def saveSchemas(schemas: Seq[StructType]) = { + val writer = new BufferedWriter(new FileWriter(new File(goldenFileURI.getPath, schemaFileName))) + schemas.foreach { schema => + writer.write(schema.toDDL) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try { + source + .getLines() + .map { ddl => + StructType.fromDDL(ddl) + } + .toArray // Avoid Stream lazy materialization + .toSeq + } finally source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + // Save the total number of rows + os.writeInt(rows.length) + // Save all rows + rows.foreach { row => + // Save the row's total number of bytes + val rowBytes = row.getBytes() + // Save the row's actual bytes + os.writeInt(rowBytes.size) + os.write(rowBytes) + } + // Save all partIds, which should be in the same order as rows + partIds.foreach { id => + os.writeInt(id) + } + } + + private def readRowsAndPartIds(is: DataInputStream): (Seq[UnsafeRow], Seq[Int]) = { + val numRows = is.readInt() + val rows = (1 to numRows).map { _ => + val rowSize = is.readInt() + val rowBuffer = new Array[Byte](rowSize) + ByteStreams.readFully(is, rowBuffer, 0, rowSize) + val row = new UnsafeRow(1) + row.pointTo(rowBuffer, rowSize) + row + } + val partIds = (1 to numRows).map(_ => is.readInt()).toArray.toSeq + (rows, partIds) + } + + private def getRandomRows(schema: StructType, numRows: Int, rand: Random): Seq[UnsafeRow] = { + val generator = RandomDataGenerator + .forType( + schema, + rand = new Random(rand.nextInt()) + ) + .get + + // Create the converters needed to convert from external row to internal + // row and to UnsafeRows. Projection itself costs a lot on initialization + // (codegen and compile), so initialize it once. + val internalConverter = CatalystTypeConverters.createToCatalystConverter(schema) + val unsafeConverter = UnsafeProjection.create(Array(schema).asInstanceOf[Array[DataType]]) + + (1 to numRows).map { _ => + val row = generator().asInstanceOf[Row] + + val internalRow = new GenericInternalRow(1) + internalRow.update(0, internalConverter(row).asInstanceOf[GenericInternalRow]) + val unsafeRow = unsafeConverter.apply(internalRow) + + // UnsafeProjection returns the same UnsafeRow instance intentionally, so + // unless doing deep copy, the hash partitions below will evaluate on the + // same row thus return same value. + unsafeRow.copy() + } + } + + private def getRandomSchemas(rand: Random): Seq[StructType] = { + (1 to numSchemaTypes).map { _ => + RandomDataGenerator.randomNestedSchema(rand, maxNumFields, variableTypes) + } + } + + private def getPartitionId(rows: Seq[UnsafeRow], hash: HashPartitioning): Seq[Int] = { + val partIdExpr = hash.partitionIdExpression + rows.map { row => + partIdExpr.eval(row).asInstanceOf[Int] + } + } + + test("SPARK-47788: Ensure the same hash function is used across batches.") { Review Comment: Updated. -- This is an automated message from the Apache Git Service. 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