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/*
* 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.collection.mutable.ArrayBuffer
import scala.reflect.{classTag, ClassTag}
import org.apache.spark.{Logging, Partition, SparkContext, SparkException, TaskContext}
import org.apache.spark.partial.{PartialResult, BoundedDouble}
import org.apache.spark.rdd.RDD
import org.apache.spark.util.NextIterator
import kafka.api.{FetchRequestBuilder, FetchResponse}
import kafka.common.{ErrorMapping, TopicAndPartition}
import kafka.consumer.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) specified in host1:port1,host2:port2 form.
* @param offsetRanges offset ranges that define the Kafka data belonging to this RDD
* @param messageHandler function for translating each message into the desired type
*/
private[kafka]
class KafkaRDD[
K: ClassTag,
V: ClassTag,
U <: Decoder[_]: ClassTag,
T <: Decoder[_]: ClassTag,
R: ClassTag] private[spark] (
sc: SparkContext,
kafkaParams: Map[String, String],
val offsetRanges: Array[OffsetRange],
leaders: Map[TopicAndPartition, (String, Int)],
messageHandler: MessageAndMetadata[K, V] => R
) extends RDD[R](sc, Nil) with Logging with HasOffsetRanges {
override def getPartitions: Array[Partition] = {
offsetRanges.zipWithIndex.map { case (o, i) =>
val (host, port) = leaders(TopicAndPartition(o.topic, o.partition))
new KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset, o.untilOffset, host, port)
}.toArray
}
override def count(): Long = offsetRanges.map(_.count).sum
override def countApprox(
timeout: Long,
confidence: Double = 0.95
): PartialResult[BoundedDouble] = {
val c = count
new PartialResult(new BoundedDouble(c, 1.0, c, c), true)
}
override def isEmpty(): Boolean = count == 0L
override def take(num: Int): Array[R] = {
val nonEmptyPartitions = this.partitions
.map(_.asInstanceOf[KafkaRDDPartition])
.filter(_.count > 0)
if (num < 1 || nonEmptyPartitions.size < 1) {
return new Array[R](0)
}
// Determine in advance how many messages need to be taken from each partition
val parts = nonEmptyPartitions.foldLeft(Map[Int, Int]()) { (result, part) =>
val remain = num - result.values.sum
if (remain > 0) {
val taken = Math.min(remain, part.count)
result + (part.index -> taken.toInt)
} else {
result
}
}
val buf = new ArrayBuffer[R]
val res = context.runJob(
this,
(tc: TaskContext, it: Iterator[R]) => it.take(parts(tc.partitionId)).toArray,
parts.keys.toArray)
res.foreach(buf ++= _)
buf.toArray
}
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.info(s"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)
}
}
private def fetchBatch: Iterator[MessageAndOffset] = {
val req = new FetchRequestBuilder()
.addFetch(part.topic, part.partition, requestOffset, kc.config.fetchMessageMaxBytes)
.build()
val resp = consumer.fetch(req)
handleFetchErr(resp)
// kafka may return a batch that starts before the requested offset
resp.messageSet(part.topic, part.partition)
.iterator
.dropWhile(_.offset < requestOffset)
}
override def close(): Unit = {
if (consumer != null) {
consumer.close()
}
}
override def getNext(): R = {
if (iter == null || !iter.hasNext) {
iter = fetchBatch
}
if (!iter.hasNext) {
assert(requestOffset == part.untilOffset, errRanOutBeforeEnd(part))
finished = true
null.asInstanceOf[R]
} else {
val item = iter.next()
if (item.offset >= part.untilOffset) {
assert(item.offset == part.untilOffset, errOvershotEnd(item.offset, part))
finished = true
null.asInstanceOf[R]
} else {
requestOffset = item.nextOffset
messageHandler(new MessageAndMetadata(
part.topic, part.partition, item.message, item.offset, keyDecoder, valueDecoder))
}
}
}
}
}
private[kafka]
object KafkaRDD {
import KafkaCluster.LeaderOffset
/**
* @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
*/
def apply[
K: ClassTag,
V: ClassTag,
U <: Decoder[_]: ClassTag,
T <: Decoder[_]: ClassTag,
R: ClassTag](
sc: SparkContext,
kafkaParams: Map[String, String],
fromOffsets: Map[TopicAndPartition, Long],
untilOffsets: Map[TopicAndPartition, LeaderOffset],
messageHandler: MessageAndMetadata[K, V] => R
): KafkaRDD[K, V, U, T, R] = {
val leaders = untilOffsets.map { case (tp, lo) =>
tp -> (lo.host, lo.port)
}.toMap
val offsetRanges = fromOffsets.map { case (tp, fo) =>
val uo = untilOffsets(tp)
OffsetRange(tp.topic, tp.partition, fo, uo.offset)
}.toArray
new KafkaRDD[K, V, U, T, R](sc, kafkaParams, offsetRanges, leaders, messageHandler)
}
}
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