Github user helena commented on a diff in the pull request:
https://github.com/apache/spark/pull/2994#discussion_r20059145
--- Diff:
external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaOutputWriter.scala
---
@@ -0,0 +1,126 @@
+/*
+ * 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.streaming.kafka
+
+import java.util.Properties
+
+import scala.reflect.ClassTag
+
+import kafka.producer.{ProducerConfig, KeyedMessage, Producer}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.streaming.dstream.DStream
+
+/**
+ * Import this object in this form:
+ * {{{
+ * import org.apache.spark.streaming.kafka.KafkaWriter._
+ * }}}
+ *
+ * Once imported, the `writeToKafka` can be called on any [[DStream]]
object in this form:
+ * {{{
+ * dstream.writeToKafka(producerConfig, f)
+ * }}}
+ */
+object KafkaWriter {
+ import scala.language.implicitConversions
+ /**
+ * This implicit method allows the user to call dstream.writeToKafka(..)
+ * @param dstream - DStream to write to Kafka
+ * @tparam T - The type of the DStream
+ * @tparam K - The type of the key to serialize to
+ * @tparam V - The type of the value to serialize to
+ * @return
+ */
+ implicit def createKafkaOutputWriter[T: ClassTag, K, V](dstream:
DStream[T]): KafkaWriter[T] = {
+ new KafkaWriter[T](dstream)
+ }
+}
+
+/**
+ *
+ * This class can be used to write data to Kafka from Spark Streaming. To
write data to Kafka
+ * simply `import org.apache.spark.streaming.kafka.KafkaWriter._` in your
application and call
+ * `dstream.writeToKafka(producerConf, func)`
+ *
+ * Here is an example:
+ * {{{
+ * // Adding this line allows the user to call
dstream.writeDStreamToKafka(..)
+ * import org.apache.spark.streaming.kafka.KafkaWriter._
+ *
+ * class ExampleWriter {
+ * val instream = ssc.queueStream(toBe)
+ * val producerConf = new Properties()
+ * producerConf.put("serializer.class",
"kafka.serializer.DefaultEncoder")
+ * producerConf.put("key.serializer.class",
"kafka.serializer.StringEncoder")
+ * producerConf.put("metadata.broker.list", "kafka.example.com:5545")
+ * producerConf.put("request.required.acks", "1")
+ * instream.writeToKafka(producerConf,
+ * (x: String) => new KeyedMessage[String, String]("default", null, x))
+ * ssc.start()
+ * }
+ *
+ * }}}
+ * @param dstream - The [[DStream]] to be written to Kafka
+ *
+ */
+class KafkaWriter[T: ClassTag](@transient dstream: DStream[T]) extends
Serializable with Logging {
+
+ /**
+ * To write data from a DStream to Kafka, call this function after
creating the DStream. Once
+ * the DStream is passed into this function, all data coming from the
DStream is written out to
+ * Kafka. The properties instance takes the configuration required to
connect to the Kafka
+ * brokers in the standard Kafka format. The serializerFunc is a
function that converts each
+ * element of the RDD to a Kafka [[KeyedMessage]]. This closure should
be serializable - so it
+ * should use only instances of Serializables.
+ * @param producerConfig The configuration that can be used to connect
to Kafka
+ * @param serializerFunc The function to convert the data from the
stream into Kafka
+ * [[KeyedMessage]]s.
+ * @tparam K The type of the key
+ * @tparam V The type of the value
+ *
+ */
+ def writeToKafka[K, V](producerConfig: Properties,
+ serializerFunc: T => KeyedMessage[K, V]): Unit = {
+
+ // Broadcast the producer to avoid sending it every time.
+ val broadcastedConfig = dstream.ssc.sc.broadcast(producerConfig)
+
+ def func = (rdd: RDD[T]) => {
+ rdd.foreachPartition(events => {
+ // The ForEachDStream runs the function locally on the driver.
+ // This code can alternatively use sc.runJob, but this approach
seemed cleaner.
+ val producer: Producer[K, V] =
--- End diff --
Why create a new producer each write?
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