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From tdas <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4964] [Streaming] Exactly-once semantic...
Date Fri, 30 Jan 2015 03:50:18 GMT
Github user tdas commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3798#discussion_r23822761
  
    --- Diff: external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
---
    @@ -144,4 +149,182 @@ object KafkaUtils {
         createStream[K, V, U, T](
           jssc.ssc, kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), 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 sc SparkContext object
    +   * @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 batch Each OffsetRange in the batch corresponds to a
    +   *   range of offsets for a given Kafka topic/partition
    +   */
    +  def createRDD[
    +    K: ClassTag,
    +    V: ClassTag,
    +    U <: Decoder[_]: ClassTag,
    +    T <: Decoder[_]: ClassTag,
    +    R: ClassTag] (
    +      sc: SparkContext,
    +      kafkaParams: Map[String, String],
    +      batch: Array[OffsetRange]
    +  ): RDD[(K, V)] with HasOffsetRanges = {
    +    val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
    +    val kc = new KafkaCluster(kafkaParams)
    +    val topics = batch.map(o => TopicAndPartition(o.topic, o.partition)).toSet
    +    val leaderMap = kc.findLeaders(topics).fold(
    +      errs => throw new SparkException(errs.mkString("\n")),
    +      ok => ok
    +    )
    +    val rddParts = batch.zipWithIndex.map { case (o, i) =>
    +        val tp = TopicAndPartition(o.topic, o.partition)
    +        val (host, port) = leaderMap(tp)
    +        new KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset, o.untilOffset, host,
port)
    +    }.toArray
    +    new KafkaRDD[K, V, U, T, (K, V)](sc, kafkaParams, rddParts, messageHandler)
    +  }
    +
    +  /** 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 sc SparkContext object
    +   * @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 batch Each OffsetRange in the batch corresponds to a
    +   *   range of offsets for a given Kafka topic/partition
    +   * @param leaders Kafka leaders for each offset range in batch
    +   * @param messageHandler function for translating each message into the desired type
    +   */
    +  def createRDD[
    +    K: ClassTag,
    +    V: ClassTag,
    +    U <: Decoder[_]: ClassTag,
    +    T <: Decoder[_]: ClassTag,
    +    R: ClassTag] (
    +      sc: SparkContext,
    +      kafkaParams: Map[String, String],
    +      batch: Array[OffsetRange],
    +      leaders: Array[Leader],
    +      messageHandler: MessageAndMetadata[K, V] => R
    +  ): RDD[R] with HasOffsetRanges = {
    +    val leaderMap = leaders.map(l => (l.topic, l.partition) -> (l.host, l.port)).toMap
    +    val rddParts = batch.zipWithIndex.map { case (o, i) =>
    +        val (host, port) = leaderMap((o.topic, o.partition))
    +        new KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset, o.untilOffset, host,
port)
    +    }.toArray
    +
    +    new KafkaRDD[K, V, U, T, R](sc, kafkaParams, rddParts, messageHandler)
    +  }
    +
    +  /**
    +   * Compared to `createStream`, the stream created by this can guarantee that each message
    +   * from Kafka is included in transformations (as opposed to output actions) exactly
once,
    +   * even in most failure situations.
    +   *
    +   * Points to note:
    +   *
    +   * Failure Recovery - You must checkpoint this stream, or save offsets yourself and
provide them
    +   * as the fromOffsets parameter on restart.
    +   * Kafka must have sufficient log retention to obtain messages after failure.
    +   *
    +   * Getting offsets from the stream - see programming guide
    +   *
    +.  * Zookeeper - This does not use Zookeeper to store offsets.  For interop with Kafka
monitors
    +   * that depend on Zookeeper, you must store offsets in ZK yourself.
    +   *
    +   * End-to-end semantics - This does not guarantee that any output operation will push
each record
    +   * exactly once. To ensure end-to-end exactly-once semantics (that is, receiving exactly
once and
    +   * outputting exactly once), you have to either ensure that the output operation is
    +   * idempotent, or transactionally store offsets with the output. See the programming
guide for
    +   * more details.
    +   *
    +   * @param ssc StreamingContext object
    +   * @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 messageHandler function for translating each message into the desired type
    +   * @param fromOffsets per-topic/partition Kafka offsets defining the (inclusive)
    +   *  starting point of the stream
    +   * @param maxRetries maximum number of times in a row to retry getting leaders' offsets
    +   */
    +  def createNewStream[
    --- End diff --
    
    BTW, all these public methods needs to annotated with "@Experimental" (see org.apache.spark.annotation.Experimental
and its uses).


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