[SPARK-6986] [SQL] Use Serializer2 in more cases.
With0a2b15ce43
, the serialization stream and deserialization stream has enough information to determine it is handling a key-value pari, a key, or a value. It is safe to use `SparkSqlSerializer2` in more cases. Author: Yin Huai <yhuai@databricks.com> Closes #5849 from yhuai/serializer2MoreCases and squashes the following commits: 53a5eaa [Yin Huai] Josh's comments. 487f540 [Yin Huai] Use BufferedOutputStream. 8385f95 [Yin Huai] Always create a new row at the deserialization side to work with sort merge join. c7e2129 [Yin Huai] Update tests. 4513d13 [Yin Huai] Use Serializer2 in more places. (cherry picked from commit3af423c92f
) Signed-off-by: Yin Huai <yhuai@databricks.com>
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@ -84,18 +84,8 @@ case class Exchange(
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def serializer(
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keySchema: Array[DataType],
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valueSchema: Array[DataType],
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hasKeyOrdering: Boolean,
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numPartitions: Int): Serializer = {
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// In ExternalSorter's spillToMergeableFile function, key-value pairs are written out
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// through write(key) and then write(value) instead of write((key, value)). Because
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// SparkSqlSerializer2 assumes that objects passed in are Product2, we cannot safely use
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// it when spillToMergeableFile in ExternalSorter will be used.
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// So, we will not use SparkSqlSerializer2 when
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// - Sort-based shuffle is enabled and the number of reducers (numPartitions) is greater
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// then the bypassMergeThreshold; or
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// - newOrdering is defined.
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val cannotUseSqlSerializer2 =
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(sortBasedShuffleOn && numPartitions > bypassMergeThreshold) || newOrdering.nonEmpty
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// It is true when there is no field that needs to be write out.
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// For now, we will not use SparkSqlSerializer2 when noField is true.
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val noField =
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@ -104,14 +94,13 @@ case class Exchange(
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val useSqlSerializer2 =
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child.sqlContext.conf.useSqlSerializer2 && // SparkSqlSerializer2 is enabled.
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!cannotUseSqlSerializer2 && // Safe to use Serializer2.
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SparkSqlSerializer2.support(keySchema) && // The schema of key is supported.
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SparkSqlSerializer2.support(valueSchema) && // The schema of value is supported.
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!noField
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val serializer = if (useSqlSerializer2) {
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logInfo("Using SparkSqlSerializer2.")
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new SparkSqlSerializer2(keySchema, valueSchema)
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new SparkSqlSerializer2(keySchema, valueSchema, hasKeyOrdering)
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} else {
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logInfo("Using SparkSqlSerializer.")
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new SparkSqlSerializer(sparkConf)
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@ -154,7 +143,8 @@ case class Exchange(
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}
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val keySchema = expressions.map(_.dataType).toArray
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val valueSchema = child.output.map(_.dataType).toArray
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shuffled.setSerializer(serializer(keySchema, valueSchema, numPartitions))
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shuffled.setSerializer(
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serializer(keySchema, valueSchema, newOrdering.nonEmpty, numPartitions))
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shuffled.map(_._2)
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@ -179,7 +169,8 @@ case class Exchange(
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new ShuffledRDD[Row, Null, Null](rdd, part)
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}
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val keySchema = child.output.map(_.dataType).toArray
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shuffled.setSerializer(serializer(keySchema, null, numPartitions))
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shuffled.setSerializer(
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serializer(keySchema, null, newOrdering.nonEmpty, numPartitions))
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shuffled.map(_._1)
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@ -199,7 +190,7 @@ case class Exchange(
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val partitioner = new HashPartitioner(1)
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val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner)
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val valueSchema = child.output.map(_.dataType).toArray
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shuffled.setSerializer(serializer(null, valueSchema, 1))
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shuffled.setSerializer(serializer(null, valueSchema, false, 1))
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shuffled.map(_._2)
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case _ => sys.error(s"Exchange not implemented for $newPartitioning")
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@ -27,7 +27,7 @@ import scala.reflect.ClassTag
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import org.apache.spark.serializer._
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import org.apache.spark.Logging
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import org.apache.spark.sql.Row
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import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
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import org.apache.spark.sql.catalyst.expressions.{SpecificMutableRow, MutableRow, GenericMutableRow}
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import org.apache.spark.sql.types._
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/**
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@ -49,9 +49,9 @@ private[sql] class Serializer2SerializationStream(
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out: OutputStream)
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extends SerializationStream with Logging {
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val rowOut = new DataOutputStream(out)
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val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut)
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val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut)
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private val rowOut = new DataOutputStream(new BufferedOutputStream(out))
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private val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut)
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private val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut)
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override def writeObject[T: ClassTag](t: T): SerializationStream = {
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val kv = t.asInstanceOf[Product2[Row, Row]]
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@ -86,31 +86,44 @@ private[sql] class Serializer2SerializationStream(
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private[sql] class Serializer2DeserializationStream(
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keySchema: Array[DataType],
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valueSchema: Array[DataType],
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hasKeyOrdering: Boolean,
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in: InputStream)
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extends DeserializationStream with Logging {
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val rowIn = new DataInputStream(new BufferedInputStream(in))
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private val rowIn = new DataInputStream(new BufferedInputStream(in))
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val key = if (keySchema != null) new SpecificMutableRow(keySchema) else null
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val value = if (valueSchema != null) new SpecificMutableRow(valueSchema) else null
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val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key)
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val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value)
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private def rowGenerator(schema: Array[DataType]): () => (MutableRow) = {
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if (schema == null) {
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() => null
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} else {
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if (hasKeyOrdering) {
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// We have key ordering specified in a ShuffledRDD, it is not safe to reuse a mutable row.
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() => new GenericMutableRow(schema.length)
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} else {
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// It is safe to reuse the mutable row.
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val mutableRow = new SpecificMutableRow(schema)
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() => mutableRow
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}
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}
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}
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// Functions used to return rows for key and value.
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private val getKey = rowGenerator(keySchema)
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private val getValue = rowGenerator(valueSchema)
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// Functions used to read a serialized row from the InputStream and deserialize it.
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private val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn)
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private val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn)
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override def readObject[T: ClassTag](): T = {
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readKeyFunc()
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readValueFunc()
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(key, value).asInstanceOf[T]
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(readKeyFunc(getKey()), readValueFunc(getValue())).asInstanceOf[T]
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}
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override def readKey[T: ClassTag](): T = {
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readKeyFunc()
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key.asInstanceOf[T]
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readKeyFunc(getKey()).asInstanceOf[T]
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}
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override def readValue[T: ClassTag](): T = {
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readValueFunc()
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value.asInstanceOf[T]
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readValueFunc(getValue()).asInstanceOf[T]
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}
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override def close(): Unit = {
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@ -118,9 +131,10 @@ private[sql] class Serializer2DeserializationStream(
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}
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}
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private[sql] class ShuffleSerializerInstance(
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private[sql] class SparkSqlSerializer2Instance(
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keySchema: Array[DataType],
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valueSchema: Array[DataType])
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valueSchema: Array[DataType],
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hasKeyOrdering: Boolean)
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extends SerializerInstance {
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def serialize[T: ClassTag](t: T): ByteBuffer =
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@ -137,7 +151,7 @@ private[sql] class ShuffleSerializerInstance(
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}
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def deserializeStream(s: InputStream): DeserializationStream = {
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new Serializer2DeserializationStream(keySchema, valueSchema, s)
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new Serializer2DeserializationStream(keySchema, valueSchema, hasKeyOrdering, s)
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}
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}
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@ -148,12 +162,16 @@ private[sql] class ShuffleSerializerInstance(
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* The schema of keys is represented by `keySchema` and that of values is represented by
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* `valueSchema`.
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*/
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private[sql] class SparkSqlSerializer2(keySchema: Array[DataType], valueSchema: Array[DataType])
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private[sql] class SparkSqlSerializer2(
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keySchema: Array[DataType],
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valueSchema: Array[DataType],
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hasKeyOrdering: Boolean)
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extends Serializer
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with Logging
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with Serializable{
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def newInstance(): SerializerInstance = new ShuffleSerializerInstance(keySchema, valueSchema)
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def newInstance(): SerializerInstance =
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new SparkSqlSerializer2Instance(keySchema, valueSchema, hasKeyOrdering)
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override def supportsRelocationOfSerializedObjects: Boolean = {
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// SparkSqlSerializer2 is stateless and writes no stream headers
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*/
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def createDeserializationFunction(
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schema: Array[DataType],
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in: DataInputStream,
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mutableRow: SpecificMutableRow): () => Unit = {
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() => {
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// If the schema is null, the returned function does nothing when it get called.
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if (schema != null) {
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in: DataInputStream): (MutableRow) => Row = {
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if (schema == null) {
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(mutableRow: MutableRow) => null
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} else {
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(mutableRow: MutableRow) => {
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var i = 0
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while (i < schema.length) {
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schema(i) match {
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@ -440,6 +458,8 @@ private[sql] object SparkSqlSerializer2 {
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}
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i += 1
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}
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mutableRow
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}
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}
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}
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@ -148,6 +148,15 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll
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table("shuffle").collect())
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}
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test("key schema is null") {
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val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",")
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val df = sql(s"SELECT $aggregations FROM shuffle")
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checkSerializer(df.queryExecution.executedPlan, serializerClass)
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checkAnswer(
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df,
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Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000))
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}
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test("value schema is null") {
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val df = sql(s"SELECT col0 FROM shuffle ORDER BY col0")
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checkSerializer(df.queryExecution.executedPlan, serializerClass)
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@ -167,29 +176,20 @@ class SparkSqlSerializer2SortShuffleSuite extends SparkSqlSerializer2Suite {
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override def beforeAll(): Unit = {
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super.beforeAll()
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// Sort merge will not be triggered.
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sql("set spark.sql.shuffle.partitions = 200")
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}
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test("key schema is null") {
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val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",")
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val df = sql(s"SELECT $aggregations FROM shuffle")
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checkSerializer(df.queryExecution.executedPlan, serializerClass)
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checkAnswer(
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df,
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Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000))
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val bypassMergeThreshold =
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sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
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sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold-1}")
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}
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}
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/** For now, we will use SparkSqlSerializer for sort based shuffle with sort merge. */
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class SparkSqlSerializer2SortMergeShuffleSuite extends SparkSqlSerializer2Suite {
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// We are expecting SparkSqlSerializer.
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override val serializerClass: Class[Serializer] =
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classOf[SparkSqlSerializer].asInstanceOf[Class[Serializer]]
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override def beforeAll(): Unit = {
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super.beforeAll()
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// To trigger the sort merge.
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sql("set spark.sql.shuffle.partitions = 201")
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val bypassMergeThreshold =
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sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
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sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold + 1}")
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}
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}
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