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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