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SnappyData distributed data store and execution engine
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.streaming
import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming._
import org.apache.spark.util.IntParam
/**
* Receives text from multiple rawNetworkStreams and counts how many '\n' delimited
* lines have the word 'the' in them. This is useful for benchmarking purposes. This
* will only work with spark.streaming.util.RawTextSender running on all worker nodes
* and with Spark using Kryo serialization (set Java property "spark.serializer" to
* "org.apache.spark.serializer.KryoSerializer").
* Usage: RawNetworkGrep
* is the number rawNetworkStreams, which should be same as number
* of work nodes in the cluster
* is "localhost".
* is the port on which RawTextSender is running in the worker nodes.
* is the Spark Streaming batch duration in milliseconds.
*/
object RawNetworkGrep {
def main(args: Array[String]) {
if (args.length != 4) {
System.err.println("Usage: RawNetworkGrep ")
System.exit(1)
}
StreamingExamples.setStreamingLogLevels()
val Array(IntParam(numStreams), host, IntParam(port), IntParam(batchMillis)) = args
val sparkConf = new SparkConf().setAppName("RawNetworkGrep")
// Create the context
val ssc = new StreamingContext(sparkConf, Duration(batchMillis))
val rawStreams = (1 to numStreams).map(_ =>
ssc.rawSocketStream[String](host, port, StorageLevel.MEMORY_ONLY_SER_2)).toArray
val union = ssc.union(rawStreams)
union.filter(_.contains("the")).count().foreachRDD(r =>
println("Grep count: " + r.collect().mkString))
ssc.start()
ssc.awaitTermination()
}
}
// scalastyle:on println
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