Github user koeninger commented on a diff in the pull request: https://github.com/apache/spark/pull/11863#discussion_r68138575 --- Diff: external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala --- @@ -0,0 +1,401 @@ +/* + * 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.annotation.Experimental +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. + * @param preferredHosts map from TopicPartition to preferred host for processing that partition. + * In most cases, use [[DirectKafkaInputDStream.preferConsistent]] + * Use [[DirectKafkaInputDStream.preferBrokers]] if your executors are on same nodes as brokers. + * @param executorKafkaParams 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. + * @param driverConsumer zero-argument function for you to construct a Kafka Consumer, + * and subscribe topics or assign partitions. + * This consumer will be used on the driver to query for offsets only, not messages. + * See <a href="http://kafka.apache.org/documentation.html#newconsumerapi">Consumer doc</a> + * @tparam K type of Kafka message key + * @tparam V type of Kafka message value + */ +@Experimental +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 0.10 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 + c.pause(newPartitions.asJava) + + c.seekToEnd(currentOffsets.keySet.asJava) + 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, true) + + // 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() + Some(rdd) + } + + override def start(): Unit = { + val c = consumer + c.poll(0) + if (currentOffsets.isEmpty) { + currentOffsets = c.assignment().asScala.map { tp => + tp -> c.position(tp) + }.toMap + } + + // don't actually want to consume any messages, so pause all partitions + c.pause(currentOffsets.keySet.asJava) + } + + override def stop(): Unit = this.synchronized { + if (kc != null) { + kc.close() + } + } + + protected val commitQueue = new ConcurrentLinkedQueue[OffsetRange] + protected val commitCallback = new AtomicReference[OffsetCommitCallback] + + /** + * Queue up offset ranges for commit to Kafka at a future time. Threadsafe. + * @param offsetRanges The maximum untilOffset for a given partition will be used at commit. + */ + def commitAsync(offsetRanges: Array[OffsetRange]): Unit = { + commitAsync(offsetRanges, null) + } + + /** + * Queue up offset ranges for commit to Kafka at a future time. Threadsafe. + * @param offsetRanges The maximum untilOffset for a given partition will be used at commit. + * @param callback Only the most recently provided callback will be used at commit. + */ + def commitAsync(offsetRanges: Array[OffsetRange], callback: OffsetCommitCallback): Unit = { + commitCallback.set(callback) + commitQueue.addAll(ju.Arrays.asList(offsetRanges: _*)) + } + + protected def commitAll(): Unit = { + val m = new ju.HashMap[TopicPartition, OffsetAndMetadata]() + val it = commitQueue.iterator() + while (it.hasNext) { + val osr = it.next + val tp = osr.topicPartition + val x = m.get(tp) + val offset = if (null == x) { osr.untilOffset } else { Math.max(x.offset, osr.untilOffset) } + m.put(tp, new OffsetAndMetadata(offset)) + } + if (!m.isEmpty) { + consumer.commitAsync(m, commitCallback.get) + } + } + + private[streaming] + class DirectKafkaInputDStreamCheckpointData extends DStreamCheckpointData(this) { + def batchForTime: mutable.HashMap[Time, Array[(String, Int, Long, Long)]] = { + data.asInstanceOf[mutable.HashMap[Time, Array[OffsetRange.OffsetRangeTuple]]] + } + + override def update(time: Time) { + batchForTime.clear() + generatedRDDs.foreach { kv => + val a = kv._2.asInstanceOf[KafkaRDD[K, V]].offsetRanges.map(_.toTuple).toArray + batchForTime += kv._1 -> a + } + } + + override def cleanup(time: Time) { } + + override def restore() { + batchForTime.toSeq.sortBy(_._1)(Time.ordering).foreach { case (t, b) => + logInfo(s"Restoring KafkaRDD for time $t ${b.mkString("[", ", ", "]")}") + generatedRDDs += t -> new KafkaRDD[K, V]( + context.sparkContext, + executorKafkaParams, + b.map(OffsetRange(_)), + getPreferredHosts, + // during restore, it's possible same partition will be consumed from multiple + // threads, so dont use cache + false + ) + } + } + } + + /** + * A RateController to retrieve the rate from RateEstimator. + */ + private[streaming] class DirectKafkaRateController(id: Int, estimator: RateEstimator) + extends RateController(id, estimator) { + override def publish(rate: Long): Unit = () + } +} + +/** + * Companion object that provides methods to create instances of [[DirectKafkaInputDStream]] + */ +@Experimental +object DirectKafkaInputDStream extends Logging { + import org.apache.spark.streaming.api.java.{ JavaInputDStream, JavaStreamingContext } + import org.apache.spark.api.java.function.{ Function0 => JFunction0 } + + /** Prefer to run on kafka brokers, if they are on same hosts as executors */ + val preferBrokers: ju.Map[TopicPartition, String] = null + /** Prefer a consistent executor per TopicPartition, evenly from all executors */ + val preferConsistent: ju.Map[TopicPartition, String] = ju.Collections.emptyMap() + + /** + * Scala constructor + * @param preferredHosts map from TopicPartition to preferred host for processing that partition. + * In most cases, use [[DirectKafkaInputDStream.preferConsistent]] + * Use [[DirectKafkaInputDStream.preferBrokers]] if your executors are on same nodes as brokers. + * @param executorKafkaParams 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. + * @param driverConsumer zero-argument function for you to construct a Kafka Consumer, + * and subscribe topics or assign partitions. + * This consumer will be used on the driver to query for offsets only, not messages. + * See <a href="http://kafka.apache.org/documentation.html#newconsumerapi">Consumer doc</a> + * @tparam K type of Kafka message key + * @tparam V type of Kafka message value + */ + def apply[K: ClassTag, V: ClassTag]( + ssc: StreamingContext, + preferredHosts: ju.Map[TopicPartition, String], + executorKafkaParams: ju.Map[String, Object], + driverConsumer: () => Consumer[K, V] --- End diff -- You need to be able to reconstruct a properly setup consumer after checkpoint recovery. There are lots of different ways to set up a KafkaConsumer, and they require post-object-instantiation calls to e.g. subscribe or assign, with fixed or dynamic topics, etc. Trying to do multiple overloads would get into an explosion problem pretty quickly, especially when you consider some of the quirks in the underlying API that need to be worked around for special cases.
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