Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/3798#discussion_r23967901
--- Diff:
external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DeterministicKafkaInputDStream.scala
---
@@ -0,0 +1,150 @@
+/*
+ * 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.annotation.tailrec
+import scala.collection.mutable
+import scala.reflect.{classTag, ClassTag}
+
+import kafka.common.TopicAndPartition
+import kafka.message.MessageAndMetadata
+import kafka.serializer.Decoder
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
+import org.apache.spark.streaming.{StreamingContext, Time}
+import org.apache.spark.streaming.dstream._
+
+/** A stream of {@link org.apache.spark.rdd.kafka.KafkaRDD} where
+ * each given Kafka topic/partition corresponds to an RDD partition.
+ * The spark configuration spark.streaming.receiver.maxRate gives the
maximum number of messages
+ * per second that each '''partition''' will accept.
+ * Starting offsets are specified in advance,
+ * and this DStream is not responsible for committing offsets,
+ * so that you can control exactly-once semantics.
+ * For an easy interface to Kafka-managed offsets,
+ * see {@link org.apache.spark.rdd.kafka.KafkaCluster}
+ * @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 stream
+ * @param messageHandler function for translating each message into the
desired type
+ * @param maxRetries maximum number of times in a row to retry getting
leaders' offsets
+ */
+private[streaming]
+class DeterministicKafkaInputDStream[
+ K: ClassTag,
+ V: ClassTag,
+ U <: Decoder[_]: ClassTag,
+ T <: Decoder[_]: ClassTag,
+ R: ClassTag](
+ @transient ssc_ : StreamingContext,
+ val kafkaParams: Map[String, String],
+ val fromOffsets: Map[TopicAndPartition, Long],
+ messageHandler: MessageAndMetadata[K, V] => R,
+ maxRetries: Int
+) extends InputDStream[R](ssc_) with Logging {
+
+ protected[streaming] override val checkpointData =
+ new DeterministicKafkaInputDStreamCheckpointData
+
+ protected val kc = new KafkaCluster(kafkaParams)
+
+ protected val maxMessagesPerPartition: Option[Long] = {
+ val ratePerSec =
context.sparkContext.getConf.getInt("spark.streaming.receiver.maxRate", 0)
+ if (ratePerSec > 0) {
+ val secsPerBatch = context.graph.batchDuration.milliseconds.toDouble
/ 1000
+ Some((secsPerBatch * ratePerSec).toLong)
+ } else {
+ None
+ }
+ }
+
+ protected var currentOffsets = fromOffsets
+
+ @tailrec
+ protected final def latestLeaderOffsets(retries: Int):
Map[TopicAndPartition, LeaderOffset] = {
+ val o = kc.getLatestLeaderOffsets(currentOffsets.keySet)
+ // Either.fold would confuse @tailrec, do it manually
+ if (o.isLeft) {
+ val err = o.left.get.toString
+ if (retries <= 0) {
+ throw new Exception(err)
+ } else {
+ log.error(err)
+ Thread.sleep(kc.config.refreshLeaderBackoffMs)
+ latestLeaderOffsets(retries - 1)
+ }
+ } else {
+ o.right.get
+ }
+ }
+
+ protected def clamp(
+ leaderOffsets: Map[TopicAndPartition, LeaderOffset]):
Map[TopicAndPartition, LeaderOffset] = {
+ maxMessagesPerPartition.map { mmp =>
+ leaderOffsets.map { case (tp, lo) =>
+ tp -> lo.copy(offset = Math.min(currentOffsets(tp) + mmp,
lo.offset))
+ }
+ }.getOrElse(leaderOffsets)
+ }
+
+ override def compute(validTime: Time): Option[KafkaRDD[K, V, U, T, R]] =
{
+ val untilOffsets = clamp(latestLeaderOffsets(maxRetries))
+ val rdd = KafkaRDD[K, V, U, T, R](
+ context.sparkContext, kafkaParams, currentOffsets, untilOffsets,
messageHandler)
+
+ currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset)
+ Some(rdd)
+ }
+
+ override def start(): Unit = {
+ }
+
+ def stop(): Unit = {
+ }
+
+ private[streaming]
+ class DeterministicKafkaInputDStreamCheckpointData extends
DStreamCheckpointData(this) {
+ def batchForTime = data.asInstanceOf[mutable.HashMap[
+ Time, Array[(Int, String, Int, Long, Long, String, Int)]]]
--- End diff --
@koeninger Yes, please making this a specific type.
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