Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/17043#discussion_r102843676
--- Diff:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaWriter.scala
---
@@ -0,0 +1,264 @@
+/*
+ * 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.kafka010
+
+import java.{util => ju}
+import java.util.concurrent.TimeUnit
+
+import scala.collection.mutable
+import scala.collection.mutable.ListBuffer
+
+import org.apache.kafka.clients.producer.{Callback, KafkaProducer,
ProducerRecord, RecordMetadata}
+import org.apache.kafka.common.serialization.ByteArraySerializer
+
+import org.apache.spark.{SparkException, TaskContext}
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.{QueryExecution, SQLExecution}
+import org.apache.spark.sql.types.{BinaryType, StringType}
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.Utils
+
+object KafkaWriter extends Logging {
+ val TOPIC_ATTRIBUTE_NAME: String = "topic"
+ val KEY_ATTRIBUTE_NAME: String = "key"
+ val VALUE_ATTRIBUTE_NAME: String = "value"
+ val MAX_OUTSTANDING_WRITES: String = "maxOutstandingWrites"
+ val DEFAULT_MAX_OUTSTANDING_WRITES: Int = 1000
+ val WAIT_FOR_CONFIRMED_WRITE_MS = "waitForConfirmedWriteMs"
+ val DEFAULT_WAIT_FOR_CONFIRMED_WRITE_MS = 1000
+
+ private case class TaskCommitMessage(
+ sparkStageId: Int,
+ sparkPartitionId: Int,
+ writeCommitted: Boolean) extends Serializable
+
+ def write(sparkSession: SparkSession,
+ queryExecution: QueryExecution,
+ kafkaParameters: ju.Map[String, Object],
+ defaultTopic: Option[String] = None): Unit = {
+
+ val schema = queryExecution.logical.output
+ schema.find(p => p.name == TOPIC_ATTRIBUTE_NAME).getOrElse(
+ if (defaultTopic == None) {
+ throw new IllegalArgumentException(s"Default topic required when
no " +
+ s"'$TOPIC_ATTRIBUTE_NAME' attribute is present")
+ } else {
+ Literal(defaultTopic.get, StringType)
+ }
+ ).dataType match {
+ case StringType => // good
+ case _ =>
+ throw new IllegalArgumentException(s"Topic type must be a String")
+ }
+ schema.find(p => p.name == KEY_ATTRIBUTE_NAME).getOrElse(
+ Literal(null, StringType)
+ ).dataType match {
+ case StringType | BinaryType => // good
+ case _ =>
+ throw new IllegalArgumentException(s"$KEY_ATTRIBUTE_NAME attribute
type " +
+ s"must be a String or BinaryType")
+ }
+ val valueField = schema.find(p => p.name ==
VALUE_ATTRIBUTE_NAME).getOrElse(
+ throw new IllegalArgumentException(s"Required attribute
'$VALUE_ATTRIBUTE_NAME' not found")
+ ).dataType match {
+ case StringType | BinaryType => // good
+ case _ =>
+ throw new IllegalArgumentException(s"$VALUE_ATTRIBUTE_NAME
attribute type " +
+ s"must be a String or BinaryType")
+ }
+ SQLExecution.withNewExecutionId(sparkSession, queryExecution) {
+ try {
+ val ret = sparkSession.sparkContext.runJob(queryExecution.toRdd,
+ (taskContext: TaskContext, iter: Iterator[InternalRow]) => {
+ executeTask(
+ iterator = iter,
+ producerConfiguration = kafkaParameters,
+ sparkStageId = taskContext.stageId(),
+ sparkPartitionId = taskContext.partitionId(),
+ sparkAttemptNumber = taskContext.attemptNumber(),
+ inputSchema = schema,
+ defaultTopic = defaultTopic)
+ })
+
+ // logInfo(s"Job ${job.getJobID} committed.")
+ } catch {
+ case cause: Throwable =>
+ // logError(s"Aborting job ${job.getJobID}.", cause)
+ throw new SparkException("Job aborted.", cause)
+ }
+ }
+ }
+
+ /** Writes data out in a single Spark task. */
+ private def executeTask(
+ iterator: Iterator[InternalRow],
+ producerConfiguration: ju.Map[String, Object],
+ sparkStageId: Int,
+ sparkPartitionId: Int,
+ sparkAttemptNumber: Int,
+ inputSchema: Seq[Attribute],
+ defaultTopic: Option[String]): TaskCommitMessage = {
+ val writeTask = new KafkaWriteTask(
+ producerConfiguration, inputSchema, defaultTopic)
+ try {
+ Utils.tryWithSafeFinallyAndFailureCallbacks(block = {
+ // Execute the task to write rows out and commit the task.
+ writeTask.execute(iterator)
+ writeTask.releaseResources()
+ })(catchBlock = {
+ // If there is an error, release resource and then abort the task
+ try {
+ writeTask.releaseResources()
+ } finally {
+ logError(s"Stage $sparkStageId, task $sparkPartitionId aborted.")
+ }
+ })
+ } catch {
+ case t: Throwable =>
+ throw new SparkException("Task failed while writing rows", t)
+ }
+
+ if (writeTask.failedWrites.size == 0) {
+ TaskCommitMessage(sparkStageId, sparkPartitionId, writeCommitted =
true)
+ } else {
+ TaskCommitMessage(sparkStageId, sparkPartitionId, writeCommitted =
false)
+ }
+ }
+
+ /**
+ * Used to reference an outstanding write. Kafka will call onCompletion
when
+ * the write has been confirmed, after which we remove it from the
outstanding
+ * write set.
+ * @param uuid used to differentiate writes
+ * @param outstandingWriteSet to remove itself from after write is
confirmed
+ */
+ private case class KafkaCallback(uuid: ju.UUID)(
+ val outstandingWriteSet: mutable.HashSet[KafkaCallback]) extends
Callback {
+ override def onCompletion(recordMetadata: RecordMetadata, e:
Exception): Unit = {
+ outstandingWriteSet.synchronized {
+ outstandingWriteSet.remove(this)
+ outstandingWriteSet.notifyAll()
+ }
+ }
+ }
+
+ /**
+ * A simple trait for writing out data in a single Spark task, without
any concerns about how
+ * to commit or abort tasks. Exceptions thrown by the implementation of
this trait will
+ * automatically trigger task aborts.
+ */
+ private class KafkaWriteTask(
+ producerConfiguration: ju.Map[String, Object],
+ inputSchema: Seq[Attribute],
+ defaultTopic: Option[String]) {
+ val waitForConfirmedWriteMs =
+ if (producerConfiguration.containsKey(WAIT_FOR_CONFIRMED_WRITE_MS)) {
+
producerConfiguration.get(WAIT_FOR_CONFIRMED_WRITE_MS).asInstanceOf[Int]
+ } else {
+ DEFAULT_WAIT_FOR_CONFIRMED_WRITE_MS
+ }
+ val maxOutstandingWrites =
+ if (producerConfiguration.containsKey(MAX_OUTSTANDING_WRITES)) {
+ producerConfiguration.get(MAX_OUTSTANDING_WRITES).asInstanceOf[Int]
+ } else {
+ DEFAULT_MAX_OUTSTANDING_WRITES
+ }
+ val outstandingWriteSet = new mutable.HashSet[KafkaCallback]
+ var failedWrites = ListBuffer.empty[Throwable]
+ val topicExpression = inputSchema.find(p => p.name ==
TOPIC_ATTRIBUTE_NAME).getOrElse(
+ if (defaultTopic == None) {
+ throw new IllegalStateException(s"Default topic required when no "
+
+ s"'$TOPIC_ATTRIBUTE_NAME' attribute is present")
+ } else {
+ Literal(null, StringType)
+ }
+ ).map{c =>
+ if (defaultTopic == None) {
+ c // return null if we can't fall back on a default value
+ } else {
+ // fall back on a default value in case we evaluate c to null
+ If(IsNull(c), Literal(UTF8String.fromString(defaultTopic.get),
StringType), c)
+ }
+ }
+ val keyExpression = inputSchema.find(p => p.name ==
KEY_ATTRIBUTE_NAME).getOrElse(
+ Literal(null, BinaryType)
+ )
+ keyExpression.dataType match {
+ case StringType | BinaryType => // good
+ case t =>
+ throw new IllegalStateException(s"$KEY_ATTRIBUTE_NAME attribute
unsupported type $t")
+ }
+ val valueExpression = inputSchema.find(p => p.name ==
VALUE_ATTRIBUTE_NAME).getOrElse(
+ throw new IllegalStateException(s"Required attribute
'$VALUE_ATTRIBUTE_NAME' not found")
+ )
+ valueExpression.dataType match {
+ case StringType | BinaryType => // good
+ case t =>
+ throw new IllegalStateException(s"$VALUE_ATTRIBUTE_NAME attribute
unsupported type $t")
+ }
+ val projection = UnsafeProjection.create(topicExpression ++
+ Seq(Cast(keyExpression, BinaryType), Cast(valueExpression,
BinaryType)), inputSchema)
+
+ // Create a Kafka Producer
+ producerConfiguration.put("key.serializer",
classOf[ByteArraySerializer].getName)
+ producerConfiguration.put("value.serializer",
classOf[ByteArraySerializer].getName)
+ val producer = new KafkaProducer[Array[Byte],
Array[Byte]](producerConfiguration)
+
+ /**
+ * Writes key value data out to topics.
+ */
+ def execute(iterator: Iterator[InternalRow]): Unit = {
+ while (iterator.hasNext) {
+ val currentRow = iterator.next()
+ val projectedRow = projection(currentRow)
+ val topic = projectedRow.get(0, StringType).toString
+ val key = projectedRow.get(1, BinaryType).asInstanceOf[Array[Byte]]
+ val value = projectedRow.get(2,
BinaryType).asInstanceOf[Array[Byte]]
+ val record = new ProducerRecord[Array[Byte], Array[Byte]](topic,
key, value)
+ val callback = outstandingWriteSet.synchronized {
+ if (outstandingWriteSet.size >= maxOutstandingWrites) {
+ outstandingWriteSet.wait(waitForConfirmedWriteMs)
+ }
+ if (outstandingWriteSet.size >= maxOutstandingWrites) {
+ throw new SparkException(s"Outstanding Kafka writes not
draining")
+ }
+ // okay we have head room now, create the callback and add to set
+ val callback = new
KafkaCallback(ju.UUID.randomUUID())(outstandingWriteSet)
+ outstandingWriteSet.add(callback)
+ callback
+ }
+ producer.send(record, callback)
+ }
+ producer.flush()
+ }
+
+ def releaseResources(): Unit = {
--- End diff --
rename to close.
---
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