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Posted to notifications@kyuubi.apache.org by "ulysses-you (via GitHub)" <gi...@apache.org> on 2023/04/04 09:39:22 UTC
[GitHub] [kyuubi] ulysses-you commented on a diff in pull request #4662: [ARROW] Arrow serialization should not introduce extra shuffle for outermost limit
ulysses-you commented on code in PR #4662:
URL: https://github.com/apache/kyuubi/pull/4662#discussion_r1156978732
##########
externals/kyuubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConvertersHelper.scala:
##########
@@ -0,0 +1,148 @@
+/*
+ * 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.execution.arrow
+
+import java.io.ByteArrayOutputStream
+import java.nio.channels.Channels
+
+import org.apache.arrow.vector._
+import org.apache.arrow.vector.ipc.{ArrowStreamWriter, WriteChannel}
+import org.apache.arrow.vector.ipc.message.{IpcOption, MessageSerializer}
+import org.apache.spark.TaskContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+object ArrowConvertersHelper extends Logging {
+
+ /**
+ * Different from [[org.apache.spark.sql.execution.arrow.ArrowConvertersHelper.toBatchIterator]],
+ * each output arrow batch contains this batch row count.
+ */
+ def toBatchIterator(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ maxRecordsPerBatch: Long,
+ maxEstimatedBatchSize: Long,
+ limit: Long,
+ timeZoneId: String): ArrowBatchIterator = {
+ new ArrowBatchIterator(
+ rowIter,
+ schema,
+ maxRecordsPerBatch,
+ maxEstimatedBatchSize,
+ limit,
+ timeZoneId,
+ TaskContext.get)
+ }
+
+ private[sql] class ArrowBatchIterator(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ maxRecordsPerBatch: Long,
+ maxEstimatedBatchSize: Long,
+ limit: Long,
+ timeZoneId: String,
+ context: TaskContext)
+ extends Iterator[Array[Byte]] {
+
+ protected val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId)
+ private val allocator =
+ ArrowUtils.rootAllocator.newChildAllocator(
+ s"to${this.getClass.getSimpleName}",
+ 0,
+ Long.MaxValue)
+
+ private val root = VectorSchemaRoot.create(arrowSchema, allocator)
+ protected val unloader = new VectorUnloader(root)
+ protected val arrowWriter = ArrowWriter.create(root)
+
+ Option(context).foreach {
+ _.addTaskCompletionListener[Unit] { _ =>
+ root.close()
+ allocator.close()
+ }
+ }
+
+ override def hasNext: Boolean = (rowIter.hasNext && rowCount < limit) || {
+ root.close()
+ allocator.close()
+ false
+ }
+
+ var rowCountInLastBatch: Long = 0
+ var rowCount: Long = 0
+
+ override def next(): Array[Byte] = {
+ val out = new ByteArrayOutputStream()
+ val writeChannel = new WriteChannel(Channels.newChannel(out))
+
+ rowCountInLastBatch = 0
+ var estimatedBatchSize = 0
Review Comment:
should it be long ？
##########
externals/kyuubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConvertersHelper.scala:
##########
@@ -0,0 +1,148 @@
+/*
+ * 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.execution.arrow
+
+import java.io.ByteArrayOutputStream
+import java.nio.channels.Channels
+
+import org.apache.arrow.vector._
+import org.apache.arrow.vector.ipc.{ArrowStreamWriter, WriteChannel}
+import org.apache.arrow.vector.ipc.message.{IpcOption, MessageSerializer}
+import org.apache.spark.TaskContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+object ArrowConvertersHelper extends Logging {
+
+ /**
+ * Different from [[org.apache.spark.sql.execution.arrow.ArrowConvertersHelper.toBatchIterator]],
+ * each output arrow batch contains this batch row count.
+ */
+ def toBatchIterator(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ maxRecordsPerBatch: Long,
+ maxEstimatedBatchSize: Long,
+ limit: Long,
+ timeZoneId: String): ArrowBatchIterator = {
+ new ArrowBatchIterator(
+ rowIter,
+ schema,
+ maxRecordsPerBatch,
+ maxEstimatedBatchSize,
+ limit,
+ timeZoneId,
+ TaskContext.get)
+ }
+
+ private[sql] class ArrowBatchIterator(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ maxRecordsPerBatch: Long,
+ maxEstimatedBatchSize: Long,
+ limit: Long,
+ timeZoneId: String,
+ context: TaskContext)
+ extends Iterator[Array[Byte]] {
+
+ protected val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId)
+ private val allocator =
+ ArrowUtils.rootAllocator.newChildAllocator(
+ s"to${this.getClass.getSimpleName}",
+ 0,
+ Long.MaxValue)
+
+ private val root = VectorSchemaRoot.create(arrowSchema, allocator)
+ protected val unloader = new VectorUnloader(root)
+ protected val arrowWriter = ArrowWriter.create(root)
+
+ Option(context).foreach {
+ _.addTaskCompletionListener[Unit] { _ =>
+ root.close()
+ allocator.close()
+ }
+ }
+
+ override def hasNext: Boolean = (rowIter.hasNext && rowCount < limit) || {
+ root.close()
+ allocator.close()
+ false
+ }
+
+ var rowCountInLastBatch: Long = 0
+ var rowCount: Long = 0
+
+ override def next(): Array[Byte] = {
+ val out = new ByteArrayOutputStream()
+ val writeChannel = new WriteChannel(Channels.newChannel(out))
+
+ rowCountInLastBatch = 0
+ var estimatedBatchSize = 0
+ Utils.tryWithSafeFinally {
+
+ // Always write the first row.
+ while (rowIter.hasNext && (
+ // For maxBatchSize and maxRecordsPerBatch, respect whatever smaller.
+ // If the size in bytes is positive (set properly), always write the first row.
+ rowCountInLastBatch == 0 && maxEstimatedBatchSize > 0 ||
+ // If the size in bytes of rows are 0 or negative, unlimit it.
+ estimatedBatchSize <= 0 ||
+ estimatedBatchSize < maxEstimatedBatchSize ||
+ // If the size of rows are 0 or negative, unlimit it.
+ maxRecordsPerBatch <= 0 ||
+ rowCountInLastBatch < maxRecordsPerBatch ||
+ rowCount < limit)) {
+ val row = rowIter.next()
+ arrowWriter.write(row)
+ estimatedBatchSize += (row match {
+ case ur: UnsafeRow => ur.getSizeInBytes
+ // Trying to estimate the size of the current row, assuming 16 bytes per value.
+ case ir: InternalRow => ir.numFields * 16
Review Comment:
in general, we can infer row size by `schema.defaultSize`
##########
externals/kyuubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/kyuubi/SparkDatasetHelper.scala:
##########
@@ -17,18 +17,95 @@
package org.apache.spark.sql.kyuubi
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.TaskContext
+import org.apache.spark.network.util.{ByteUnit, JavaUtils}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.execution.{CollectLimitExec, SparkPlan, SQLExecution}
+import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec
+import org.apache.spark.sql.execution.arrow.{ArrowCollectUtils, ArrowConverters, KyuubiArrowUtils}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
+import org.apache.kyuubi.engine.spark.KyuubiSparkUtil
import org.apache.kyuubi.engine.spark.schema.RowSet
object SparkDatasetHelper {
+
def toArrowBatchRdd[T](ds: Dataset[T]): RDD[Array[Byte]] = {
ds.toArrowBatchRdd
}
+ /**
+ * Forked from [[Dataset.toArrowBatchRdd(plan: SparkPlan)]].
+ * Convert to an RDD of serialized ArrowRecordBatches.
+ */
+ def toArrowBatchRdd(plan: SparkPlan): RDD[Array[Byte]] = {
+ val schemaCaptured = plan.schema
+ val maxRecordsPerBatch = plan.session.sessionState.conf.arrowMaxRecordsPerBatch
+ val timeZoneId = plan.session.sessionState.conf.sessionLocalTimeZone
+ plan.execute().mapPartitionsInternal { iter =>
+ val context = TaskContext.get()
+ ArrowConverters.toBatchIterator(
+ iter,
+ schemaCaptured,
+ maxRecordsPerBatch,
+ timeZoneId,
+ context)
+ }
+ }
+
+ def doCollectLimit(collectLimit: CollectLimitExec): Array[Array[Byte]] = {
+ val timeZoneId = collectLimit.session.sessionState.conf.sessionLocalTimeZone
+ val maxRecordsPerBatch = collectLimit.session.sessionState.conf.arrowMaxRecordsPerBatch
+
+ val batches = ArrowCollectUtils.takeAsArrowBatches(
+ collectLimit,
+ maxRecordsPerBatch,
+ maxBatchSize,
+ timeZoneId)
+
+ // note that the number of rows in the returned arrow batches may be >= `limit`, preform
+ // the slicing operation of result
+ val result = ArrayBuffer[Array[Byte]]()
+ var i = 0
+ var rest = collectLimit.limit
+ while (i < batches.length && rest > 0) {
+ val (batch, size) = batches(i)
+ if (size <= rest) {
+ result += batch
+ // returned ArrowRecordBatch has less than `limit` row count, safety to do conversion
+ rest -= size.toInt
+ } else { // size > rest
+ result += KyuubiArrowUtils.slice(collectLimit.schema, timeZoneId, batch, 0, rest)
+ rest = 0
+ }
+ i += 1
+ }
+ result.toArray
+ }
+
+ def executeCollect(df: DataFrame): Array[Array[Byte]] = withNewExecutionId(df) {
+ executeArrowBatchCollect(df.queryExecution.executedPlan)
+ }
+
+ def toArrowBatchLocalIterator(df: DataFrame): Iterator[Array[Byte]] = {
+ withNewExecutionId(df) {
+ toArrowBatchRdd(df).toLocalIterator
+ }
+ }
+
+ def executeArrowBatchCollect: SparkPlan => Array[Array[Byte]] = {
+ case adaptiveSparkPlan: AdaptiveSparkPlanExec =>
+ executeArrowBatchCollect(adaptiveSparkPlan.finalPhysicalPlan)
Review Comment:
sall we reflect some related private method to workaround ? it's unacceptable if we break the compatibility.
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