[GitHub] [kyuubi] cxzl25 commented on a diff in pull request #4662: [ARROW] Arrow serialization should not introduce extra shuffle for outermost limit

["cxzl25 (via GitHub)" <gi...@apache.org>]

cxzl25 commented on code in PR #4662:
URL: https://github.com/apache/kyuubi/pull/4662#discussion_r1160540646


##########
externals/kyuubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/execution/arrow/KyuubiArrowConverters.scala:
##########
@@ -0,0 +1,309 @@
+/*
+ * 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.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.nio.channels.Channels
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.arrow.vector._
+import org.apache.arrow.vector.ipc.{ArrowStreamWriter, ReadChannel, 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.SparkSession
+import org.apache.spark.sql.catalyst.{InternalRow, SQLConfHelper}
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.sql.execution.CollectLimitExec
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+object KyuubiArrowConverters extends SQLConfHelper with Logging {
+
+  type Batch = (Array[Byte], Long)
+
+  private val rootAllocator = ArrowUtils.rootAllocator.newChildAllocator(
+    s"to${this.getClass.getSimpleName}",
+    0,
+    Long.MaxValue)
+
+  /**
+   * this method is to slice the input Arrow record batch byte array `bytes`, starting from `start`
+   * and taking `length` number of elements.
+   */
+  def slice(
+      schema: StructType,
+      timeZoneId: String,
+      bytes: Array[Byte],
+      start: Int,
+      length: Int): Array[Byte] = {
+    val in = new ByteArrayInputStream(bytes)
+    val out = new ByteArrayOutputStream(bytes.length)
+
+    try {
+      val recordBatch = MessageSerializer.deserializeRecordBatch(
+        new ReadChannel(Channels.newChannel(in)),
+        rootAllocator)
+      val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId)
+
+      val root = VectorSchemaRoot.create(arrowSchema, rootAllocator)
+      val vectorLoader = new VectorLoader(root)
+      vectorLoader.load(recordBatch)
+      recordBatch.close()
+
+      val unloader = new VectorUnloader(root.slice(start, length))
+      val writeChannel = new WriteChannel(Channels.newChannel(out))
+      val batch = unloader.getRecordBatch()
+      MessageSerializer.serialize(writeChannel, batch)
+      batch.close()
+      out.toByteArray()
+    } finally {
+      in.close()
+      out.close()
+    }
+  }
+
+  /**
+   * Forked from `org.apache.spark.sql.execution.SparkPlan#executeTake()`, the algorithm can be
+   * summarized in the following steps:
+   * 1. If the limit specified in the CollectLimitExec object is 0, the function returns an empty
+   *    array of batches.
+   * 2. Otherwise, execute the child query plan of the CollectLimitExec object to obtain an RDD of
+   *    data to collect.
+   * 3. Use an iterative approach to collect data in batches until the specified limit is reached.
+   *    In each iteration, it selects a subset of the partitions of the RDD to scan and tries to
+   *    collect data from them.
+   * 4. For each partition subset, we use the runJob method of the Spark context to execute a
+   *    closure that scans the partition data and converts it to Arrow batches.
+   * 5. Check if the collected data reaches the specified limit. If not, it selects another subset
+   *    of partitions to scan and repeats the process until the limit is reached or all partitions
+   *    have been scanned.
+   * 6. Return an array of all the collected Arrow batches.
+   *
+   * Note that:
+   * 1. The returned Arrow batches row count >= limit, if the input df has more than the `limit`
+   *    row count
+   * 2. We don't implement the `takeFromEnd` logical
+   *
+   * @return
+   */
+  def takeAsArrowBatches(
+      collectLimitExec: CollectLimitExec,
+      maxRecordsPerBatch: Long,
+      maxEstimatedBatchSize: Long,
+      timeZoneId: String): Array[Batch] = {
+    val n = collectLimitExec.limit
+    val schema = collectLimitExec.schema
+    if (n == 0) {
+      return new Array[Batch](0)

Review Comment:
   nit: remove `return`



##########
externals/kyuubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/execution/arrow/KyuubiArrowConverters.scala:
##########
@@ -0,0 +1,309 @@
+/*
+ * 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.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.nio.channels.Channels
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.arrow.vector._
+import org.apache.arrow.vector.ipc.{ArrowStreamWriter, ReadChannel, 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.SparkSession
+import org.apache.spark.sql.catalyst.{InternalRow, SQLConfHelper}
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.sql.execution.CollectLimitExec
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+object KyuubiArrowConverters extends SQLConfHelper with Logging {
+
+  type Batch = (Array[Byte], Long)
+
+  private val rootAllocator = ArrowUtils.rootAllocator.newChildAllocator(
+    s"to${this.getClass.getSimpleName}",
+    0,
+    Long.MaxValue)
+
+  /**
+   * this method is to slice the input Arrow record batch byte array `bytes`, starting from `start`
+   * and taking `length` number of elements.
+   */
+  def slice(
+      schema: StructType,
+      timeZoneId: String,
+      bytes: Array[Byte],
+      start: Int,
+      length: Int): Array[Byte] = {
+    val in = new ByteArrayInputStream(bytes)
+    val out = new ByteArrayOutputStream(bytes.length)
+
+    try {
+      val recordBatch = MessageSerializer.deserializeRecordBatch(
+        new ReadChannel(Channels.newChannel(in)),
+        rootAllocator)
+      val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId)
+
+      val root = VectorSchemaRoot.create(arrowSchema, rootAllocator)
+      val vectorLoader = new VectorLoader(root)
+      vectorLoader.load(recordBatch)
+      recordBatch.close()
+
+      val unloader = new VectorUnloader(root.slice(start, length))
+      val writeChannel = new WriteChannel(Channels.newChannel(out))
+      val batch = unloader.getRecordBatch()
+      MessageSerializer.serialize(writeChannel, batch)
+      batch.close()
+      out.toByteArray()
+    } finally {
+      in.close()
+      out.close()
+    }
+  }
+
+  /**
+   * Forked from `org.apache.spark.sql.execution.SparkPlan#executeTake()`, the algorithm can be
+   * summarized in the following steps:
+   * 1. If the limit specified in the CollectLimitExec object is 0, the function returns an empty
+   *    array of batches.
+   * 2. Otherwise, execute the child query plan of the CollectLimitExec object to obtain an RDD of
+   *    data to collect.
+   * 3. Use an iterative approach to collect data in batches until the specified limit is reached.
+   *    In each iteration, it selects a subset of the partitions of the RDD to scan and tries to
+   *    collect data from them.
+   * 4. For each partition subset, we use the runJob method of the Spark context to execute a
+   *    closure that scans the partition data and converts it to Arrow batches.
+   * 5. Check if the collected data reaches the specified limit. If not, it selects another subset
+   *    of partitions to scan and repeats the process until the limit is reached or all partitions
+   *    have been scanned.
+   * 6. Return an array of all the collected Arrow batches.
+   *
+   * Note that:
+   * 1. The returned Arrow batches row count >= limit, if the input df has more than the `limit`
+   *    row count
+   * 2. We don't implement the `takeFromEnd` logical
+   *
+   * @return
+   */
+  def takeAsArrowBatches(
+      collectLimitExec: CollectLimitExec,
+      maxRecordsPerBatch: Long,
+      maxEstimatedBatchSize: Long,
+      timeZoneId: String): Array[Batch] = {
+    val n = collectLimitExec.limit
+    val schema = collectLimitExec.schema
+    if (n == 0) {
+      return new Array[Batch](0)
+    } else {
+      val limitScaleUpFactor = Math.max(conf.limitScaleUpFactor, 2)
+      // TODO: refactor and reuse the code from RDD's take()
+      val childRDD = collectLimitExec.child.execute()
+      val buf = new ArrayBuffer[Batch]
+      var bufferedRowSize = 0L
+      val totalParts = childRDD.partitions.length
+      var partsScanned = 0
+      while (bufferedRowSize < n && partsScanned < totalParts) {
+        // The number of partitions to try in this iteration. It is ok for this number to be
+        // greater than totalParts because we actually cap it at totalParts in runJob.
+        var numPartsToTry = limitInitialNumPartitions
+        if (partsScanned > 0) {
+          // If we didn't find any rows after the previous iteration, multiply by
+          // limitScaleUpFactor and retry. Otherwise, interpolate the number of partitions we need
+          // to try, but overestimate it by 50%. We also cap the estimation in the end.
+          if (buf.isEmpty) {
+            numPartsToTry = partsScanned * limitScaleUpFactor
+          } else {
+            val left = n - bufferedRowSize
+            // As left > 0, numPartsToTry is always >= 1
+            numPartsToTry = Math.ceil(1.5 * left * partsScanned / bufferedRowSize).toInt
+            numPartsToTry = Math.min(numPartsToTry, partsScanned * limitScaleUpFactor)
+          }
+        }
+
+        val partsToScan =
+          partsScanned.until(math.min(partsScanned + numPartsToTry, totalParts).toInt)

Review Comment:
   nit: remove `toInt`
   



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