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Re-port RecoverableNetworkWordCount to Java example, and touch up doc…
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… / formatting in related examples
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srowen committed Sep 28, 2014
1 parent 0d8cdf0 commit 179b3c2
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Expand Up @@ -25,7 +25,7 @@
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
Expand All @@ -35,8 +35,9 @@

/**
* Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
*
* Usage: JavaNetworkWordCount <hostname> <port>
* <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
* <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
*
* To run this on your local machine, you need to first run a Netcat server
* `$ nc -lk 9999`
Expand All @@ -56,7 +57,7 @@ public static void main(String[] args) {

// Create the context with a 1 second batch size
SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount");
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));

// Create a JavaReceiverInputDStream on target ip:port and count the
// words in input stream of \n delimited text (eg. generated by 'nc')
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@@ -0,0 +1,160 @@
/*
* 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 org.apache.spark.examples.streaming;

import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
import java.util.Arrays;
import java.util.regex.Pattern;

import scala.Tuple2;
import com.google.common.collect.Lists;
import com.google.common.io.Files;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.Time;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.api.java.JavaStreamingContextFactory;

/**
* Counts words in text encoded with UTF8 received from the network every second.
*
* Usage: JavaRecoverableNetworkWordCount <hostname> <port> <checkpoint-directory> <output-file>
* <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
* data. <checkpoint-directory> directory to HDFS-compatible file system which checkpoint data
* <output-file> file to which the word counts will be appended
*
* <checkpoint-directory> and <output-file> must be absolute paths
*
* To run this on your local machine, you need to first run a Netcat server
*
* `$ nc -lk 9999`
*
* and run the example as
*
* `$ ./bin/run-example org.apache.spark.examples.streaming.JavaRecoverableNetworkWordCount \
* localhost 9999 ~/checkpoint/ ~/out`
*
* If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
* a new StreamingContext (will print "Creating new context" to the console). Otherwise, if
* checkpoint data exists in ~/checkpoint/, then it will create StreamingContext from
* the checkpoint data.
*
* To run this example in a local standalone cluster with automatic driver recovery,
*
* `$ bin/spark-class org.apache.spark.deploy.Client -s launch <cluster-url> \
* <path-to-examples-jar> \
* org.apache.spark.examples.streaming.JavaRecoverableNetworkWordCount <cluster-url> \
* localhost 9999 ~/checkpoint ~/out`
*
* <path-to-examples-jar> would typically be
* <spark-dir>/examples/target/scala-XX/spark-examples....jar
*
* Refer to the online documentation for more details.
*/
public final class JavaRecoverableNetworkWordCount {
private static final Pattern SPACE = Pattern.compile(" ");

private static JavaStreamingContext createContext(String ip, int port, String outputPath) {

// If you do not see this printed, that means the StreamingContext has been loaded
// from the new checkpoint
System.out.println("Creating new context");
final File outputFile = new File(outputPath);
if (outputFile.exists()) {
outputFile.delete();
}
SparkConf sparkConf = new SparkConf().setAppName("JavaRecoverableNetworkWordCount");
// Create the context with a 1 second batch size
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));

// Create a socket stream on target ip:port and count the
// words in input stream of \n delimited text (eg. generated by 'nc')
JavaReceiverInputDStream<String> lines = ssc.socketTextStream(ip, port);
JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String x) {
return Lists.newArrayList(SPACE.split(x));
}
});
JavaPairDStream<String, Integer> wordCounts = words.mapToPair(
new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String s) {
return new Tuple2<String, Integer>(s, 1);
}
}).reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});

wordCounts.foreachRDD(new Function2<JavaPairRDD<String, Integer>, Time, Void>() {
@Override
public Void call(JavaPairRDD<String, Integer> rdd, Time time) throws IOException {
String counts = "Counts at time " + time + " " + rdd.collect();
System.out.println(counts);
System.out.println("Appending to " + outputFile.getAbsolutePath());
Files.append(counts + "\n", outputFile, Charset.defaultCharset());
return null;
}
});

return ssc;
}

public static void main(String[] args) {
if (args.length != 4) {
System.err.println("You arguments were " + Arrays.asList(args));
System.err.println(
"Usage: JavaRecoverableNetworkWordCount <hostname> <port> <checkpoint-directory>\n" +
" <output-file>. <hostname> and <port> describe the TCP server that Spark\n" +
" Streaming would connect to receive data. <checkpoint-directory> directory to\n" +
" HDFS-compatible file system which checkpoint data <output-file> file to which\n" +
" the word counts will be appended\n" +
"\n" +
"In local mode, <master> should be 'local[n]' with n > 1\n" +
"Both <checkpoint-directory> and <output-file> must be absolute paths");
System.exit(1);
}

final String ip = args[0];
final int port = Integer.parseInt(args[1]);
String checkpointDirectory = args[2];
final String outputPath = args[3];
JavaStreamingContextFactory factory = new JavaStreamingContextFactory() {
@Override
public JavaStreamingContext create() {
return createContext(ip, port, outputPath);
}
};
JavaStreamingContext ssc = JavaStreamingContext.getOrCreate(checkpointDirectory, factory);
ssc.start();
ssc.awaitTermination();
}
}
Expand Up @@ -31,15 +31,13 @@ import org.apache.spark.util.IntParam
/**
* Counts words in text encoded with UTF8 received from the network every second.
*
* Usage: NetworkWordCount <hostname> <port> <checkpoint-directory> <output-file>
* Usage: RecoverableNetworkWordCount <hostname> <port> <checkpoint-directory> <output-file>
* <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
* data. <checkpoint-directory> directory to HDFS-compatible file system which checkpoint data
* <output-file> file to which the word counts will be appended
*
* In local mode, <master> should be 'local[n]' with n > 1
* <checkpoint-directory> and <output-file> must be absolute paths
*
*
* To run this on your local machine, you need to first run a Netcat server
*
* `$ nc -lk 9999`
Expand All @@ -66,7 +64,6 @@ import org.apache.spark.util.IntParam
*
* Refer to the online documentation for more details.
*/

object RecoverableNetworkWordCount {

def createContext(ip: String, port: Int, outputPath: String) = {
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