Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/11863#discussion_r60089024
--- Diff:
external/kafka-beta/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
---
@@ -0,0 +1,259 @@
+/*
+ * 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 java.{ util => ju }
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.{classTag, ClassTag}
+
+import org.apache.kafka.clients.consumer.{ ConsumerConfig, ConsumerRecord }
+import org.apache.kafka.common.TopicPartition
+
+import org.apache.spark.{Partition, SparkContext, SparkException,
TaskContext}
+import org.apache.spark.internal.Logging
+import org.apache.spark.partial.{BoundedDouble, PartialResult}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.scheduler.ExecutorCacheTaskLocation
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * 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.htmll#newconsumerconfigs";>
+ * configuration parameters</a>. Requires "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
+ */
+
+class KafkaRDD[
+ K: ClassTag,
+ V: ClassTag] private[spark] (
+ sc: SparkContext,
+ val kafkaParams: ju.Map[String, Object],
+ val offsetRanges: Array[OffsetRange],
+ val preferredHosts: ju.Map[TopicPartition, String]
+) extends RDD[ConsumerRecord[K, V]](sc, Nil) with Logging with
HasOffsetRanges {
+
+ assert("none" ==
+
kafkaParams.get(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG).asInstanceOf[String],
+ ConsumerConfig.AUTO_OFFSET_RESET_CONFIG +
+ " must be set to none for executor kafka params, else messages may
not match offsetRange")
+
+ assert(false ==
+
kafkaParams.get(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG).asInstanceOf[Boolean],
+ ConsumerConfig.AUTO_OFFSET_RESET_CONFIG +
+ " must be set to false for executor kafka params, else offsets may
commit before processing")
+
+ // TODO is it necessary to have separate configs for initial poll time
vs ongoing poll time?
+ private val pollTimeout =
conf.getLong("spark.streaming.kafka.consumer.poll.ms", 256)
+ private val cacheInitialCapacity =
+ conf.getInt("spark.streaming.kafka.consumer.cache.initialCapacity", 16)
+ private val cacheMaxCapacity =
+ conf.getInt("spark.streaming.kafka.consumer.cache.maxCapacity", 64)
+ private val cacheLoadFactor =
+ conf.getDouble("spark.streaming.kafka.consumer.cache.loadFactor",
0.75).toFloat
+
+ override def persist(newLevel: StorageLevel): this.type = {
+ log.error("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 KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset,
o.untilOffset)
+ }.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[ConsumerRecord[K, V]] = {
+ val nonEmptyPartitions = this.partitions
+ .map(_.asInstanceOf[KafkaRDDPartition])
+ .filter(_.count > 0)
+
+ if (num < 1 || nonEmptyPartitions.size < 1) {
--- End diff --
I don't really want to make style changes to code that's identical to the
existing working consumer.
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