Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/3798#discussion_r23729113
--- Diff:
external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
---
@@ -144,4 +150,116 @@ 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
+ * @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],
+ messageHandler: MessageAndMetadata[K, V] => R
+ ): RDD[R] = {
+ val parts = batch.zipWithIndex.map { case (o, i) =>
+ new KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset,
o.untilOffset, o.host, o.port)
+ }.toArray
+ new KafkaRDD[K, V, U, T, R](sc, kafkaParams, parts, messageHandler)
+ }
+
+ /**
+ * This DOES NOT guarantee that side-effects of an action will see each
message exactly once.
--- End diff --
It is pretty clear that there is a separation between a stream and a output
oepration. Creating a stream does not create an output operation. This
oepration creates only a stream that ensures that records are received exactly
one in the stream. Whether you want to do the output operation with
exactly-once semantics is a distinct questions. Both are necessary for
achieving end-to-end exactly-once processing.
So what his name suggests is only creating an exactly-once stream. People
are already aware from rest of the documentation in the spark streaming
programming guide that output operations are not exactly-once. And if it is not
clear, I willing to add whatever that is necessary to make it clear.
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