WweiL commented on code in PR #45971: URL: https://github.com/apache/spark/pull/45971#discussion_r1562849582
########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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) + unsafeRow.copy() Review Comment: Just curious why need a copy here? ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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. Review Comment: Thanks for the useful comment! ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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{_ => Review Comment: nit: `(1 to numRows).map { _ =>` spaces between `map`, `{` and `_` ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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 Review Comment: nit: space between `(` and `_` ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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) + unsafeRow.copy() + } + } + + private def getRandomSchemas(numSchemaTypes: Int, rand: Random, + maxNumFields: Int): Seq[StructType] = { + (1 to numSchemaTypes).map { _ => + val numField = rand.nextInt(maxNumFields) + RandomDataGenerator.randomSchema(rand, Math.max(1, numField), 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) { + val seed = Random.nextInt() + val random = new Random(seed) + logInfo(s"Get random inputs with seed $seed") + + val schemas = getRandomSchemas(numSchemaTypes, random, maxNumFields) + + 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( + 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) + Review Comment: nit remove this empty line ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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) + unsafeRow.copy() + } + } + + private def getRandomSchemas(numSchemaTypes: Int, rand: Random, + maxNumFields: Int): Seq[StructType] = { Review Comment: indent ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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) + unsafeRow.copy() + } + } + + private def getRandomSchemas(numSchemaTypes: Int, rand: Random, + maxNumFields: Int): Seq[StructType] = { + (1 to numSchemaTypes).map { _ => + val numField = rand.nextInt(maxNumFields) + RandomDataGenerator.randomSchema(rand, Math.max(1, numField), variableTypes) Review Comment: Should this be `Math.min`? ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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) + unsafeRow.copy() + } + } + + private def getRandomSchemas(numSchemaTypes: Int, rand: Random, + maxNumFields: Int): Seq[StructType] = { + (1 to numSchemaTypes).map { _ => + val numField = rand.nextInt(maxNumFields) + RandomDataGenerator.randomSchema(rand, Math.max(1, numField), variableTypes) + } + } + + private def getPartitionId(rows: Seq[UnsafeRow], hash: HashPartitioning): Seq[Int] = { + val partIdExpr = hash.partitionIdExpression + rows.map{row => Review Comment: nit: `ros.map { row =>` ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + 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.json) + writer.newLine() + } + writer.close() + } + + private def readSchemas(): Seq[StructType] = { + val source = Source.fromFile(new File(goldenFileURI.getPath, schemaFileName)) + try{ + source.getLines().map{json => DataType.fromJson(json).asInstanceOf[StructType]} + .toArray // Avoid Stream lazy materialization + .toSeq + } finally + source.close() + } + + private def saveRowsAndPartIds(rows: Seq[UnsafeRow], partIds: Seq[Int], os: DataOutputStream) = { + os.writeInt(rows.length) + rows.foreach { row => + val rowBytes = row.getBytes() + os.writeInt(rowBytes.size) + os.write(rowBytes)} + 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(RandomDataGenerator.MAX_STR_LEN))) + .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) + unsafeRow.copy() + } + } + + private def getRandomSchemas(numSchemaTypes: Int, rand: Random, + maxNumFields: Int): Seq[StructType] = { + (1 to numSchemaTypes).map { _ => + val numField = rand.nextInt(maxNumFields) + RandomDataGenerator.randomSchema(rand, Math.max(1, numField), 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) { + val seed = Random.nextInt() + val random = new Random(seed) + logInfo(s"Get random inputs with seed $seed") + + val schemas = getRandomSchemas(numSchemaTypes, random, maxNumFields) + + 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( + 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) Review Comment: So in the binary golden file, the format is like (no new line, added for better understanding): ``` <num rows> row1 part1 row2 part2 ... <num rows> row1 part1 row2 part2 ... ``` If so, could you add a comment here explaining this layout? Thanks in advance! ########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryHashPartitionVerifySuite.scala: ########## @@ -0,0 +1,199 @@ +/* + * 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.streaming.StreamTest +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 + val goldenFileURI = this.getClass.getResource("/structured-streaming/partition-tests/").toURI + + val schemaFileName = "randomSchemas" // files for storing random input schemas + val rowAndPartIdFilename = + "rowsAndPartIds" // files for storing random input rows and resulting partition ids + 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) + val numSchemaTypes = 5 + val maxNumFields = 10 + + // Configs for shuffle + val numRows = 10000 + val numShufflePartitions = 100 + private def saveSchemas(schemas: Seq[StructType]) = { + val writer = new BufferedWriter(new FileWriter(new File(goldenFileURI.getPath, schemaFileName))) + schemas.foreach{ schema => Review Comment: nit: `foreach { schema =>` -- This is an automated message from the Apache Git Service. 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