HeartSaVioR commented on code in PR #45971: URL: https://github.com/apache/spark/pull/45971#discussion_r1565032444
########## 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: Probably the better frame for this is, we ensure the same has function to be used across Spark versions. That's technically the same when the query upgrades the Spark version during the restart, but emphasizing "compatibility across Spark versions" is more direct to what we verify here. ########## 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]) = { Review Comment: nit: empty line between val and def ########## 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) + logInfo(s"Get random inputs with seed $seed") + + val schemas = getRandomSchemas(random) + + val os = new DataOutputStream( + codec.compressedOutputStream(new FileOutputStream(rowAndPartIdFile)) + ) + + saveSchemas(schemas) + + schemas.foreach { schema => + // Streaming stateful ops rely on this distribution to partition the data. + // Spark should make sure this class's partition dependency remain unchanged. + val hash = StatefulOpClusteredDistribution( + Seq(BoundReference(0, schema, nullable = true)), + numShufflePartitions + ).createPartitioning(numShufflePartitions) + + assert(sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala Review Comment: Looks like broken code? ########## 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: traditional approach for using random seed (except the case you create random instance too quickly - multiple in several milliseconds) is System.currentTimeMillis() as seed, which Random with no param will just do it. ########## 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) + logInfo(s"Get random inputs with seed $seed") + + val schemas = getRandomSchemas(random) + + val os = new DataOutputStream( + codec.compressedOutputStream(new FileOutputStream(rowAndPartIdFile)) + ) + + saveSchemas(schemas) + + schemas.foreach { schema => + // Streaming stateful ops rely on this distribution to partition the data. + // Spark should make sure this class's partition dependency remain unchanged. + val hash = StatefulOpClusteredDistribution( + Seq(BoundReference(0, schema, nullable = true)), + numShufflePartitions + ).createPartitioning(numShufflePartitions) + + assert(sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala + hash.isInstanceOf[HashPartitioning], + "StatefulOpClusteredDistribution should " + + "rely on HashPartitioning to ensure partitions remain the same for streaming " + + "stateful operators." + ) + val rows = getRandomRows(schema, numRows, random) + val partitions = getPartitionId(rows, hash.asInstanceOf[HashPartitioning]) + saveRowsAndPartIds(rows, partitions, os) + } + os.close() + } else { + val schemas = readSchemas() + val is = new DataInputStream( + codec.compressedInputStream(new FileInputStream(rowAndPartIdFile)) + ) + schemas.foreach { schema => + val hash = StatefulOpClusteredDistribution( + Seq(BoundReference(0, schema, nullable = true)), + numShufflePartitions + ).createPartitioning(numShufflePartitions) + + assert( + hash.isInstanceOf[HashPartitioning], + "StatefulOpClusteredDistribution should " + + "rely on HashPartitioning to ensure partitions remain the same for streaming " + + "stateful operators." + ) + + val (rows, expectedPartitions) = readRowsAndPartIds(is) + val partitions = getPartitionId(rows, hash.asInstanceOf[HashPartitioning]) + assert( + partitions === expectedPartitions, Review Comment: Please consider what will be logged when this assertion fails, and whether the information is sufficient to investigate the issue. If there are only a few rows being shown as mismatched partitions, what would be the better format of assertion error message to understand the issue? ########## 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) Review Comment: While I don't think we have to add all types (size restriction), let's add importantly used types. StructType with two TimestampTypes consists a type of time window, so `TimestampType` would be a good one to test. `LongType` is also a good one. ########## 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 Review Comment: Do I understand correctly that RandomDataGenerator.forType receives the parameters for max string length and max array size? We don't need to let others to fix the code of RandomDataGenerator. Let's just do that by ourselves via providing the parameters. ########## 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 = Review Comment: Where do we actually use this? We actually do not do actual shuffle, and we seem to pass the number of shuffle partition as parameter for all necessary places. -- 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. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
