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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package spark.streaming.dstream
import spark.{RDD, Partitioner}
import spark.rdd.CoGroupedRDD
import spark.streaming.{Time, DStream, Duration}
private[streaming]
class CoGroupedDStream[K : ClassManifest](
parents: Seq[DStream[(K, _)]],
partitioner: Partitioner
) extends DStream[(K, Seq[Seq[_]])](parents.head.ssc) {
if (parents.length == 0) {
throw new IllegalArgumentException("Empty array of parents")
}
if (parents.map(_.ssc).distinct.size > 1) {
throw new IllegalArgumentException("Array of parents have different StreamingContexts")
}
if (parents.map(_.slideDuration).distinct.size > 1) {
throw new IllegalArgumentException("Array of parents have different slide times")
}
override def dependencies = parents.toList
override def slideDuration: Duration = parents.head.slideDuration
override def compute(validTime: Time): Option[RDD[(K, Seq[Seq[_]])]] = {
val part = partitioner
val rdds = parents.flatMap(_.getOrCompute(validTime))
if (rdds.size > 0) {
val q = new CoGroupedRDD[K](rdds, part)
Some(q)
} else {
None
}
}
}
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