org.apache.spark.sql.kafka010.KafkaSourceRDD.scala Maven / Gradle / Ivy
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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.sql.kafka010
import java.{util => ju}
import scala.collection.mutable.ArrayBuffer
import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.kafka.common.TopicPartition
import org.apache.spark.{Partition, SparkContext, TaskContext}
import org.apache.spark.partial.{BoundedDouble, PartialResult}
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.NextIterator
/** Offset range that one partition of the KafkaSourceRDD has to read */
private[kafka010] case class KafkaSourceRDDOffsetRange(
topicPartition: TopicPartition,
fromOffset: Long,
untilOffset: Long,
preferredLoc: Option[String]) {
def topic: String = topicPartition.topic
def partition: Int = topicPartition.partition
def size: Long = untilOffset - fromOffset
}
/** Partition of the KafkaSourceRDD */
private[kafka010] case class KafkaSourceRDDPartition(
index: Int, offsetRange: KafkaSourceRDDOffsetRange) extends Partition
/**
* An RDD that reads data from Kafka based on offset ranges across multiple partitions.
* Additionally, it allows preferred locations to be set for each topic + partition, so that
* the [[KafkaSource]] can ensure the same executor always reads the same topic + partition
* and cached KafkaConsuemrs (see [[CachedKafkaConsumer]] can be used read data efficiently.
*
* @param sc the [[SparkContext]]
* @param executorKafkaParams Kafka configuration for creating KafkaConsumer on the executors
* @param offsetRanges Offset ranges that define the Kafka data belonging to this RDD
*/
private[kafka010] class KafkaSourceRDD(
sc: SparkContext,
executorKafkaParams: ju.Map[String, Object],
offsetRanges: Seq[KafkaSourceRDDOffsetRange],
pollTimeoutMs: Long,
failOnDataLoss: Boolean,
reuseKafkaConsumer: Boolean)
extends RDD[ConsumerRecord[Array[Byte], Array[Byte]]](sc, Nil) {
override def persist(newLevel: StorageLevel): this.type = {
logError("Kafka ConsumerRecord is not serializable. " +
"Use .map to extract fields before calling .persist or .window")
super.persist(newLevel)
}
override def getPartitions: Array[Partition] = {
offsetRanges.zipWithIndex.map { case (o, i) => new KafkaSourceRDDPartition(i, o) }.toArray
}
override def count(): Long = offsetRanges.map(_.size).sum
override def countApprox(timeout: Long, confidence: Double): 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[ConsumerRecord[Array[Byte], Array[Byte]]] = {
val nonEmptyPartitions =
this.partitions.map(_.asInstanceOf[KafkaSourceRDDPartition]).filter(_.offsetRange.size > 0)
if (num < 1 || nonEmptyPartitions.isEmpty) {
return new Array[ConsumerRecord[Array[Byte], Array[Byte]]](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.offsetRange.size)
result + (part.index -> taken.toInt)
} else {
result
}
}
val buf = new ArrayBuffer[ConsumerRecord[Array[Byte], Array[Byte]]]
val res = context.runJob(
this,
(tc: TaskContext, it: Iterator[ConsumerRecord[Array[Byte], Array[Byte]]]) =>
it.take(parts(tc.partitionId)).toArray, parts.keys.toArray
)
res.foreach(buf ++= _)
buf.toArray
}
override def getPreferredLocations(split: Partition): Seq[String] = {
val part = split.asInstanceOf[KafkaSourceRDDPartition]
part.offsetRange.preferredLoc.map(Seq(_)).getOrElse(Seq.empty)
}
override def compute(
thePart: Partition,
context: TaskContext): Iterator[ConsumerRecord[Array[Byte], Array[Byte]]] = {
val sourcePartition = thePart.asInstanceOf[KafkaSourceRDDPartition]
val topic = sourcePartition.offsetRange.topic
val kafkaPartition = sourcePartition.offsetRange.partition
val consumer =
if (!reuseKafkaConsumer) {
// If we can't reuse CachedKafkaConsumers, creating a new CachedKafkaConsumer. As here we
// uses `assign`, we don't need to worry about the "group.id" conflicts.
CachedKafkaConsumer.createUncached(topic, kafkaPartition, executorKafkaParams)
} else {
CachedKafkaConsumer.getOrCreate(topic, kafkaPartition, executorKafkaParams)
}
val range = resolveRange(consumer, sourcePartition.offsetRange)
assert(
range.fromOffset <= range.untilOffset,
s"Beginning offset ${range.fromOffset} is after the ending offset ${range.untilOffset} " +
s"for topic ${range.topic} partition ${range.partition}. " +
"You either provided an invalid fromOffset, or the Kafka topic has been damaged")
if (range.fromOffset == range.untilOffset) {
logInfo(s"Beginning offset ${range.fromOffset} is the same as ending offset " +
s"skipping ${range.topic} ${range.partition}")
Iterator.empty
} else {
val underlying = new NextIterator[ConsumerRecord[Array[Byte], Array[Byte]]]() {
var requestOffset = range.fromOffset
override def getNext(): ConsumerRecord[Array[Byte], Array[Byte]] = {
if (requestOffset >= range.untilOffset) {
// Processed all offsets in this partition.
finished = true
null
} else {
val r = consumer.get(requestOffset, range.untilOffset, pollTimeoutMs, failOnDataLoss)
if (r == null) {
// Losing some data. Skip the rest offsets in this partition.
finished = true
null
} else {
requestOffset = r.offset + 1
r
}
}
}
override protected def close(): Unit = {
if (!reuseKafkaConsumer) {
// Don't forget to close non-reuse KafkaConsumers. You may take down your cluster!
consumer.close()
} else {
// Indicate that we're no longer using this consumer
CachedKafkaConsumer.releaseKafkaConsumer(topic, kafkaPartition, executorKafkaParams)
}
}
}
// Release consumer, either by removing it or indicating we're no longer using it
context.addTaskCompletionListener { _ =>
underlying.closeIfNeeded()
}
underlying
}
}
private def resolveRange(consumer: CachedKafkaConsumer, range: KafkaSourceRDDOffsetRange) = {
if (range.fromOffset < 0 || range.untilOffset < 0) {
// Late bind the offset range
val availableOffsetRange = consumer.getAvailableOffsetRange()
val fromOffset = if (range.fromOffset < 0) {
assert(range.fromOffset == KafkaOffsetRangeLimit.EARLIEST,
s"earliest offset ${range.fromOffset} does not equal ${KafkaOffsetRangeLimit.EARLIEST}")
availableOffsetRange.earliest
} else {
range.fromOffset
}
val untilOffset = if (range.untilOffset < 0) {
assert(range.untilOffset == KafkaOffsetRangeLimit.LATEST,
s"latest offset ${range.untilOffset} does not equal ${KafkaOffsetRangeLimit.LATEST}")
availableOffsetRange.latest
} else {
range.untilOffset
}
KafkaSourceRDDOffsetRange(range.topicPartition,
fromOffset, untilOffset, range.preferredLoc)
} else {
range
}
}
}
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