Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/3798#discussion_r23742746
--- 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 --
Alright, I understand your point of view :) Then lets consider other
alternate names. We could simply name it generic `createNewStream`. Which is
okay, because eventually, if all performance and other issues are flattened
out, we would like to graduate it from "experimental feature" (Spark 1.3 will
mark it experimental) to the default supported implementation of kafka stream.
So "new stream" may be a natural indicator about which one to use.
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