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From koeninger <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-12177][Streaming][Kafka] Update KafkaDS...
Date Fri, 06 May 2016 14:24:41 GMT
Github user koeninger commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11863#discussion_r62336181
  
    --- 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()
    +    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
    +    currentOffsets.keySet.foreach(tp => c.pause(tp))
    +  }
    +
    +  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)
    +      }
    +    }
    +  }
    +
    +  /**
    +   * 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 = ()
    +  }
    +}
    +
    +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 */
    +  def apply[K: ClassTag, V: ClassTag](
    +      ssc: StreamingContext,
    +      preferredHosts: ju.Map[TopicPartition, String],
    +      executorKafkaParams: ju.Map[String, Object],
    +      driverConsumer: () => Consumer[K, V]
    +    ): DirectKafkaInputDStream[K, V] = {
    +    val ph = new ju.HashMap[TopicPartition, String](preferredHosts)
    --- End diff --
    
    Good catch, will fix this shortly.


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