Github user dibbhatt commented on a diff in the pull request:
https://github.com/apache/spark/pull/3798#discussion_r23890423
--- Diff:
external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
---
@@ -0,0 +1,220 @@
+/*
+ * 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 scala.reflect.{classTag, ClassTag}
+
+import org.apache.spark.{Logging, Partition, SparkContext, SparkException,
TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.NextIterator
+
+import java.util.Properties
+import kafka.api.{FetchRequestBuilder, FetchResponse}
+import kafka.common.{ErrorMapping, TopicAndPartition}
+import kafka.consumer.{ConsumerConfig, SimpleConsumer}
+import kafka.message.{MessageAndMetadata, MessageAndOffset}
+import kafka.serializer.Decoder
+import kafka.utils.VerifiableProperties
+
+/**
+ * A batch-oriented interface for consuming from Kafka.
+ * Starting and ending offsets are specified in advance,
+ * so that you can control exactly-once semantics.
+ * @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 batch Each KafkaRDDPartition in the batch corresponds to a
+ * range of offsets for a given Kafka topic/partition
+ * @param messageHandler function for translating each message into the
desired type
+ */
+private[spark]
+class KafkaRDD[
+ K: ClassTag,
+ V: ClassTag,
+ U <: Decoder[_]: ClassTag,
+ T <: Decoder[_]: ClassTag,
+ R: ClassTag] private[spark] (
+ sc: SparkContext,
+ kafkaParams: Map[String, String],
+ private[spark] val batch: Array[KafkaRDDPartition],
+ messageHandler: MessageAndMetadata[K, V] => R
+ ) extends RDD[R](sc, Nil) with Logging with HasOffsetRanges {
+
+ def offsetRanges: Array[OffsetRange] =
batch.asInstanceOf[Array[OffsetRange]]
+
+ override def getPartitions: Array[Partition] =
batch.asInstanceOf[Array[Partition]]
+
+ override def getPreferredLocations(thePart: Partition): Seq[String] = {
+ val part = thePart.asInstanceOf[KafkaRDDPartition]
+ // TODO is additional hostname resolution necessary here
+ Seq(part.host)
+ }
+
+ private def errBeginAfterEnd(part: KafkaRDDPartition): String =
+ s"Beginning offset ${part.fromOffset} is after the ending offset
${part.untilOffset} " +
+ s"for topic ${part.topic} partition ${part.partition}. " +
+ "You either provided an invalid fromOffset, or the Kafka topic has
been damaged"
+
+ private def errRanOutBeforeEnd(part: KafkaRDDPartition): String =
+ s"Ran out of messages before reaching ending offset
${part.untilOffset} " +
+ s"for topic ${part.topic} partition ${part.partition} start
${part.fromOffset}." +
+ " This should not happen, and indicates that messages may have been
lost"
+
+ private def errOvershotEnd(itemOffset: Long, part: KafkaRDDPartition):
String =
+ s"Got ${itemOffset} > ending offset ${part.untilOffset} " +
+ s"for topic ${part.topic} partition ${part.partition} start
${part.fromOffset}." +
+ " This should not happen, and indicates a message may have been
skipped"
+
+ override def compute(thePart: Partition, context: TaskContext):
Iterator[R] = {
+ val part = thePart.asInstanceOf[KafkaRDDPartition]
+ assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part))
+ if (part.fromOffset == part.untilOffset) {
+ log.warn("Beginning offset ${part.fromOffset} is the same as ending
offset " +
+ s"skipping ${part.topic} ${part.partition}")
+ Iterator.empty
+ } else {
+ new KafkaRDDIterator(part, context)
+ }
+ }
+
+ private class KafkaRDDIterator(
+ part: KafkaRDDPartition,
+ context: TaskContext) extends NextIterator[R] {
+
+ context.addTaskCompletionListener{ context => closeIfNeeded() }
+
+ log.info(s"Computing topic ${part.topic}, partition ${part.partition}
" +
+ s"offsets ${part.fromOffset} -> ${part.untilOffset}")
+
+ val kc = new KafkaCluster(kafkaParams)
+ 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 = connectLeader
+ var requestOffset = part.fromOffset
+ var iter: Iterator[MessageAndOffset] = null
+
+ // The idea is to use the provided preferred host, except on task
retry atttempts,
+ // to minimize number of kafka metadata requests
+ private def connectLeader: SimpleConsumer = {
+ if (context.attemptNumber > 0) {
+ kc.connectLeader(part.topic, part.partition).fold(
+ errs => throw new SparkException(
+ s"Couldn't connect to leader for topic ${part.topic}
${part.partition}: " +
+ errs.mkString("\n")),
+ consumer => consumer
+ )
+ } else {
+ kc.connect(part.host, part.port)
+ }
+ }
+
+ private def handleFetchErr(resp: FetchResponse) {
+ 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)
+ }
+ }
+
--- End diff --
Hi Cody, I believe in this implementation the range of offset of given only
at the start, for rest of the flow the offset range is calculated
automatically. What I say, if Offset_Out_Of_Range comes in those RDDs where you
calculate the offset, this implementation can not recover from this failure. I
did not say that we always get this error all time , but this error is not very
rare cases also. and if the Receiver stops because of this, that's an issue.
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