Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/3798#discussion_r22498258
--- Diff:
external/kafka/src/main/scala/org/apache/spark/rdd/kafka/KafkaRDD.scala ---
@@ -0,0 +1,157 @@
+/*
+ * 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.rdd.kafka
+
+import scala.reflect.{classTag, ClassTag}
+
+import org.apache.spark.{Logging, Partition, SparkContext, TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.NextIterator
+
+import java.util.Properties
+import kafka.api.FetchRequestBuilder
+import kafka.common.{ErrorMapping, TopicAndPartition}
+import kafka.consumer.{ConsumerConfig, SimpleConsumer}
+import kafka.message.{MessageAndMetadata, MessageAndOffset}
+import kafka.serializer.Decoder
+import kafka.utils.VerifiableProperties
+
+case class KafkaRDDPartition(
+ override val index: Int,
+ topic: String,
+ partition: Int,
+ fromOffset: Long,
+ untilOffset: Long
+) extends Partition
+
+/** A batch-oriented interface for consuming from Kafka.
+ * Each given Kafka topic/partition corresponds to an RDD partition.
+ * Starting and ending offsets are specified in advance,
+ * so that you can control exactly-once semantics.
+ * For an easy interface to Kafka-managed offsets,
+ * see {@link org.apache.spark.rdd.kafka.KafkaCluster}
+ * @param kafkaParams Kafka <a
href="http://kafka.apache.org/documentation.html#configuration";>
+ * configuration parameters</a>.
+ * Requires "metadata.broker.list" or "bootstrap.servers" to be set
with Kafka broker(s),
+ * NOT zookeeper servers, specified in host1:port1,host2:port2 form.
+ * @param fromOffsets per-topic/partition Kafka offsets defining the
(inclusive)
+ * starting point of the batch
+ * @param untilOffsets per-topic/partition Kafka offsets defining the
(exclusive)
+ * ending point of the batch
+ * @param messageHandler function for translating each message into the
desired type
+ */
+class KafkaRDD[
+ K: ClassTag,
+ V: ClassTag,
+ U <: Decoder[_]: ClassTag,
+ T <: Decoder[_]: ClassTag,
+ R: ClassTag](
+ sc: SparkContext,
+ val kafkaParams: Map[String, String],
+ val fromOffsets: Map[TopicAndPartition, Long],
+ val untilOffsets: Map[TopicAndPartition, Long],
+ messageHandler: MessageAndMetadata[K, V] => R
+ ) extends RDD[R](sc, Nil) with Logging {
+
+ assert(fromOffsets.keys == untilOffsets.keys,
+ "Must provide both from and until offsets for each topic/partition")
+
+ override def getPartitions: Array[Partition] =
fromOffsets.zipWithIndex.map { kvi =>
+ val ((tp, from), index) = kvi
+ new KafkaRDDPartition(index, tp.topic, tp.partition, from,
untilOffsets(tp))
+ }.toArray
+
+ override def compute(thePart: Partition, context: TaskContext) = {
+ val part = thePart.asInstanceOf[KafkaRDDPartition]
+ if (part.fromOffset >= part.untilOffset) {
+ log.warn("Beginning offset is same or after ending offset " +
+ s"skipping ${part.topic} ${part.partition}")
+ Iterator.empty
+ } else {
+ new NextIterator[R] {
+ context.addTaskCompletionListener{ context => closeIfNeeded() }
+
+ val kc = new KafkaCluster(kafkaParams)
+ log.info(s"Computing topic ${part.topic}, partition
${part.partition} " +
+ s"offsets ${part.fromOffset} -> ${part.untilOffset}")
+ val keyDecoder =
classTag[U].runtimeClass.getConstructor(classOf[VerifiableProperties])
+ .newInstance(kc.config.props)
+ .asInstanceOf[Decoder[K]]
+ val valueDecoder =
classTag[T].runtimeClass.getConstructor(classOf[VerifiableProperties])
+ .newInstance(kc.config.props)
+ .asInstanceOf[Decoder[V]]
+ val consumer: SimpleConsumer = kc.connectLeader(part.topic,
part.partition).fold(
+ errs => throw new Exception(
+ s"Couldn't connect to leader for topic ${part.topic}
${part.partition}: " +
+ errs.mkString("\n")),
+ consumer => consumer
+ )
+ var requestOffset = part.fromOffset
+ var iter: Iterator[MessageAndOffset] = null
+
+ override def close() = consumer.close()
+
+ override def getNext: R = {
+ if (iter == null || !iter.hasNext) {
+ val req = new FetchRequestBuilder().
+ addFetch(part.topic, part.partition, requestOffset,
kc.config.fetchMessageMaxBytes).
+ build()
+ val resp = consumer.fetch(req)
+ if (resp.hasError) {
+ val err = resp.errorCode(part.topic, part.partition)
+ if (err == ErrorMapping.LeaderNotAvailableCode ||
+ err == ErrorMapping.NotLeaderForPartitionCode) {
+ log.error(s"Lost leader for topic ${part.topic} partition
${part.partition}, " +
+ s" sleeping for ${kc.config.refreshLeaderBackoffMs}ms")
+ Thread.sleep(kc.config.refreshLeaderBackoffMs)
+ }
+ // Let normal rdd retry sort out reconnect attempts
+ throw ErrorMapping.exceptionFor(err)
+ }
+ iter = resp.messageSet(part.topic, part.partition)
+ .iterator
+ .dropWhile(_.offset < requestOffset)
--- End diff --
Wow! That was not intuitive. Worth mentioning this in the code.
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