Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/15102#discussion_r80616878
--- Diff:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceRDD.scala
---
@@ -0,0 +1,163 @@
+/*
+ * 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.sql.kafka010
+
+import java.{util => ju}
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.kafka.clients.consumer.{ConsumerRecord,
OffsetOutOfRangeException}
+import org.apache.kafka.common.TopicPartition
+
+import org.apache.spark.{Partition, SparkContext, TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+
+/** 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.
+ *
+ * Note that this is a simplified version of the
org.apache.spark.streaming.kafka010.KafkaRDD.
+ *
+ * @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])
+ 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 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 compute(
+ thePart: Partition,
+ context: TaskContext): Iterator[ConsumerRecord[Array[Byte],
Array[Byte]]] = {
+ val range = thePart.asInstanceOf[KafkaSourceRDDPartition].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 consumer = CachedKafkaConsumer.getOrCreate(
+ range.topic, range.partition, executorKafkaParams)
+ var requestOffset = range.fromOffset
+
+ logDebug(s"Creating iterator for $range")
+
+ new Iterator[ConsumerRecord[Array[Byte], Array[Byte]]]() {
+
+ private var prefetch: ConsumerRecord[Array[Byte], Array[Byte]] = _
+
+ private def fetchNext(): ConsumerRecord[Array[Byte], Array[Byte]]
= {
+ try {
+ val r = consumer.get(requestOffset)
+ requestOffset += 1
+ r
+ } catch {
+ case e: OffsetOutOfRangeException =>
+ logWarning(s"${range.topicPartition} was deleted, some data
may have been missed")
--- End diff --
This is egregiously wrong. That is not the only reason for an offset out
of range exception, and this should not be a warning level log.
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