Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/11863#discussion_r60417031
--- Diff:
external/kafka-beta/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
---
@@ -0,0 +1,344 @@
+/*
+ * 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 java.util.concurrent.ConcurrentLinkedQueue
+import java.util.concurrent.atomic.AtomicReference
+
+import scala.annotation.tailrec
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+import scala.reflect.ClassTag
+
+import org.apache.kafka.clients.consumer._
+import org.apache.kafka.common.{ PartitionInfo, TopicPartition }
+
+import org.apache.spark.SparkException
+import org.apache.spark.internal.Logging
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{StreamingContext, Time}
+import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.scheduler.{RateController,
StreamInputInfo}
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
+
+/**
+ * A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where
+ * each given Kafka topic/partition corresponds to an RDD partition.
+ * The spark configuration spark.streaming.kafka.maxRatePerPartition 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.
+ * @param kafkaParams Kafka <a
href="http://kafka.apache.org/documentation.html#newconsumerconfigs";>
+ * configuration parameters</a>.
+ * Requires "bootstrap.servers" to be set with Kafka broker(s),
+ * NOT zookeeper servers, specified in host1:port1,host2:port2 form.
+ */
+
+class DirectKafkaInputDStream[K: ClassTag, V: ClassTag] private[spark] (
+ _ssc: StreamingContext,
+ preferredHosts: ju.Map[TopicPartition, String],
+ executorKafkaParams: ju.Map[String, Object],
+ driverConsumer: () => Consumer[K, V]
+ ) extends InputDStream[ConsumerRecord[K, V]](_ssc) with Logging {
+
+ @transient private var kc: Consumer[K, V] = null
+ def consumer(): Consumer[K, V] = this.synchronized {
+ if (null == kc) {
+ kc = driverConsumer()
+ }
+ kc
+ }
+ consumer()
+
+ override def persist(newLevel: StorageLevel): DStream[ConsumerRecord[K,
V]] = {
+ log.error("Kafka ConsumerRecord is not serializable. " +
+ "Use .map to extract fields before calling .persist or .window")
+ super.persist(newLevel)
+ }
+
+ protected def getBrokers = {
+ val c = consumer
+ val result = new ju.HashMap[TopicPartition, String]()
+ val hosts = new ju.HashMap[TopicPartition, String]()
+ val assignments = c.assignment().iterator()
+ while (assignments.hasNext()) {
+ val tp: TopicPartition = assignments.next()
+ if (null == hosts.get(tp)) {
+ val infos = c.partitionsFor(tp.topic).iterator()
+ while (infos.hasNext()) {
+ val i = infos.next()
+ hosts.put(new TopicPartition(i.topic(), i.partition()),
i.leader.host())
+ }
+ }
+ result.put(tp, hosts.get(tp))
+ }
+ result
+ }
+
+ protected def getPreferredHosts: ju.Map[TopicPartition, String] = {
+ if (preferredHosts == DirectKafkaInputDStream.preferBrokers) {
+ getBrokers
+ } else {
+ preferredHosts
+ }
+ }
+
+ // Keep this consistent with how other streams are named (e.g. "Flume
polling stream [2]")
+ private[streaming] override def name: String = s"Kafka beta direct
stream [$id]"
+
+ protected[streaming] override val checkpointData =
+ new DirectKafkaInputDStreamCheckpointData
+
+
+ /**
+ * Asynchronously maintains & sends new rate limits to the receiver
through the receiver tracker.
+ */
+ override protected[streaming] val rateController: Option[RateController]
= {
+ if (RateController.isBackPressureEnabled(ssc.conf)) {
+ Some(new DirectKafkaRateController(id,
+ RateEstimator.create(ssc.conf, context.graph.batchDuration)))
+ } else {
+ None
+ }
+ }
+
+ private val maxRateLimitPerPartition: Int =
context.sparkContext.getConf.getInt(
+ "spark.streaming.kafka.maxRatePerPartition", 0)
+
+ protected[streaming] def maxMessagesPerPartition(
+ offsets: Map[TopicPartition, Long]): Option[Map[TopicPartition, Long]]
= {
+ val estimatedRateLimit = rateController.map(_.getLatestRate().toInt)
+
+ // calculate a per-partition rate limit based on current lag
+ val effectiveRateLimitPerPartition = estimatedRateLimit.filter(_ > 0)
match {
+ case Some(rate) =>
+ val lagPerPartition = offsets.map { case (tp, offset) =>
+ tp -> Math.max(offset - currentOffsets(tp), 0)
+ }
+ val totalLag = lagPerPartition.values.sum
+
+ lagPerPartition.map { case (tp, lag) =>
+ val backpressureRate = Math.round(lag / totalLag.toFloat * rate)
+ tp -> (if (maxRateLimitPerPartition > 0) {
+ Math.min(backpressureRate, maxRateLimitPerPartition)} else
backpressureRate)
+ }
+ case None => offsets.map { case (tp, offset) => tp ->
maxRateLimitPerPartition }
+ }
+
+ if (effectiveRateLimitPerPartition.values.sum > 0) {
+ val secsPerBatch = context.graph.batchDuration.milliseconds.toDouble
/ 1000
+ Some(effectiveRateLimitPerPartition.map {
+ case (tp, limit) => tp -> (secsPerBatch * limit).toLong
+ })
+ } else {
+ None
+ }
+ }
+
+ protected var currentOffsets = Map[TopicPartition, Long]()
+
+ protected def latestOffsets(): Map[TopicPartition, Long] = {
+ val c = consumer
+ c.poll(0)
+ val parts = c.assignment().asScala
+
+ // make sure new partitions are reflected in currentOffsets
+ val newPartitions = parts.diff(currentOffsets.keySet)
+ currentOffsets = currentOffsets ++ newPartitions.map(tp => tp ->
c.position(tp)).toMap
+ newPartitions.foreach(tp => c.pause(tp))
+
+ c.seekToEnd()
+ parts.map(tp => tp -> c.position(tp)).toMap
+ }
+
+ // limits the maximum number of messages per partition
+ protected def clamp(
+ offsets: Map[TopicPartition, Long]): Map[TopicPartition, Long] = {
+
+ maxMessagesPerPartition(offsets).map { mmp =>
+ mmp.map { case (tp, messages) =>
+ val uo = offsets(tp)
+ tp -> Math.min(currentOffsets(tp) + messages, uo)
+ }
+ }.getOrElse(offsets)
+ }
+
+ override def compute(validTime: Time): Option[KafkaRDD[K, V]] = {
+ val untilOffsets = clamp(latestOffsets())
+ val offsetRanges = untilOffsets.map { case (tp, uo) =>
+ val fo = currentOffsets(tp)
+ OffsetRange(tp.topic, tp.partition, fo, uo)
+ }
+ val rdd = new KafkaRDD[K, V](
+ context.sparkContext, executorKafkaParams, offsetRanges.toArray,
getPreferredHosts)
+
+ // Report the record number and metadata of this batch interval to
InputInfoTracker.
+ val description = offsetRanges.filter { offsetRange =>
+ // Don't display empty ranges.
+ offsetRange.fromOffset != offsetRange.untilOffset
+ }.map { offsetRange =>
+ s"topic: ${offsetRange.topic}\tpartition:
${offsetRange.partition}\t" +
+ s"offsets: ${offsetRange.fromOffset} to ${offsetRange.untilOffset}"
+ }.mkString("\n")
+ // Copy offsetRanges to immutable.List to prevent from being modified
by the user
+ val metadata = Map(
+ "offsets" -> offsetRanges.toList,
+ StreamInputInfo.METADATA_KEY_DESCRIPTION -> description)
+ val inputInfo = StreamInputInfo(id, rdd.count, metadata)
+ ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
+
+ currentOffsets = untilOffsets
+ commitAll()
--- End diff --
commitAll is defined lower in this same file, and simply drains the queue
of whatever offsets have been provided by the user via commitAsync.
As the name implies, commitAsync doesn't give you any particular timing
guarantees, but you can't really get transactional commits into Kafka in any
case.
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