Github user koeninger commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3798#discussion_r24033509
  
    --- Diff: 
external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala 
---
    @@ -144,4 +150,174 @@ 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 offsetRanges Each OffsetRange in the batch corresponds to a
    +   *   range of offsets for a given Kafka topic/partition
    +   */
    +  @Experimental
    +  def createRDD[
    +    K: ClassTag,
    +    V: ClassTag,
    +    U <: Decoder[_]: ClassTag,
    +    T <: Decoder[_]: ClassTag] (
    +      sc: SparkContext,
    +      kafkaParams: Map[String, String],
    +      offsetRanges: Array[OffsetRange]
    +  ): RDD[(K, V)] with HasOffsetRanges = {
    +    val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, 
mmd.message)
    +    val kc = new KafkaCluster(kafkaParams)
    +    val topics = offsetRanges.map(o => TopicAndPartition(o.topic, 
o.partition)).toSet
    +    val leaders = kc.findLeaders(topics).fold(
    +      errs => throw new SparkException(errs.mkString("\n")),
    +      ok => ok
    +    )
    +    new KafkaRDD[K, V, U, T, (K, V)](sc, kafkaParams, offsetRanges, 
leaders, 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 offsetRanges 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
    +   */
    +  @Experimental
    +  def createRDD[
    +    K: ClassTag,
    +    V: ClassTag,
    +    U <: Decoder[_]: ClassTag,
    +    T <: Decoder[_]: ClassTag,
    +    R: ClassTag] (
    +      sc: SparkContext,
    +      kafkaParams: Map[String, String],
    +      offsetRanges: Array[OffsetRange],
    +      leaders: Array[Leader],
    +      messageHandler: MessageAndMetadata[K, V] => R
    +  ): RDD[R] with HasOffsetRanges = {
    +
    +    val leaderMap = leaders
    +      .map(l => TopicAndPartition(l.topic, l.partition) -> (l.host, 
l.port))
    +      .toMap
    +    new KafkaRDD[K, V, U, T, R](sc, kafkaParams, offsetRanges, leaderMap, 
messageHandler)
    +  }
    +
    +  /**
    +   * This stream can guarantee that each message from Kafka is included in 
transformations
    --- End diff --
    
    The documentation has already been changed several times since your 
previous comments.  The current version of it doesn't make any comparison to 
existing "createStream" calls.


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