使用Java编写并运行Spark应用程序

我们首先提出这样一个简单的需求：
现在要分析某网站的访问日志信息，统计来自不同IP的用户访问的次数，从而通过Geo信息来获得来访用户所在国家地区分布状况。这里我拿我网站的日志记录行示例，如下所示：

1	121.205.198.92 - - [21/Feb/2014:00:00:07 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

2	121.205.198.92 - - [21/Feb/2014:00:00:11 +0800] "POST /wp-comments-post.php HTTP/1.1" 302 26 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:23.0) Gecko/20100101 Firefox/23.0"

3	121.205.198.92 - - [21/Feb/2014:00:00:12 +0800] "GET /archives/417.html/ HTTP/1.1" 301 26 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

4	121.205.198.92 - - [21/Feb/2014:00:00:12 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

5	121.205.241.229 - - [21/Feb/2014:00:00:13 +0800] "GET /archives/526.html HTTP/1.1" 200 12080 "http://shiyanjun.cn/archives/526.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

6	121.205.241.229 - - [21/Feb/2014:00:00:15 +0800] "POST /wp-comments-post.php HTTP/1.1" 302 26 "http://shiyanjun.cn/archives/526.html/" "Mozilla/5.0 (Windows NT 5.1; rv:23.0) Gecko/20100101 Firefox/23.0"

Java实现Spark应用程序（Application）

我们实现的统计分析程序，有如下几个功能点：

• 从HDFS读取日志数据文件
• 将每行的第一个字段（IP地址）抽取出来
• 统计每个IP地址出现的次数
• 根据每个IP地址出现的次数进行一个降序排序
• 根据IP地址，调用GeoIP库获取IP所属国家
• 打印输出结果，每行的格式：[国家代码] IP地址 频率

下面，看我们使用Java实现的统计分析应用程序代码，如下所示：

001	package org.shirdrn.spark.job;

002

003	import java.io.File;

004	import java.io.IOException;

005	import java.util.Arrays;

006	import java.util.Collections;

007	import java.util.Comparator;

008	import java.util.List;

009	import java.util.regex.Pattern;

010

011	import org.apache.commons.logging.Log;

012	import org.apache.commons.logging.LogFactory;

013	import org.apache.spark.api.java.JavaPairRDD;

014	import org.apache.spark.api.java.JavaRDD;

015	import org.apache.spark.api.java.JavaSparkContext;

016	import org.apache.spark.api.java.function.FlatMapFunction;

017	import org.apache.spark.api.java.function.Function2;

018	import org.apache.spark.api.java.function.PairFunction;

019	import org.shirdrn.spark.job.maxmind.Country;

020	import org.shirdrn.spark.job.maxmind.LookupService;

021

022	import scala.Serializable;

023	import scala.Tuple2;

024

025	public class IPAddressStats implements Serializable {

026

027	private static final long serialVersionUID = 8533489548835413763L;

028	private static final Log LOG = LogFactory.getLog(IPAddressStats.class);

029	private static final Pattern SPACE = Pattern.compile(" ");

030	private transient LookupService lookupService;

031	private transient final String geoIPFile;

032

033	public IPAddressStats(String geoIPFile) {

034	this.geoIPFile = geoIPFile;

035

try {

036	// lookupService: get country code from a IP address

037	File file = new File(this.geoIPFile);

038	LOG.info("GeoIP file: " + file.getAbsolutePath());

039	lookupService = new AdvancedLookupService(file, LookupService.GEOIP_MEMORY_CACHE);

040	} catch (IOException e) {

041	throw new RuntimeException(e);

042

}

043

}

044

045	@SuppressWarnings("serial")

046	public void stat(String[] args) {

047	JavaSparkContext ctx = new JavaSparkContext(args[0], "IPAddressStats",

048	System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(IPAddressStats.class));

049	JavaRDD<String> lines = ctx.textFile(args[1], 1);

050

051	// splits and extracts ip address filed

052	JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {

053

@Override

054	public Iterable<String> call(String s) {

055	// 121.205.198.92 - - [21/Feb/2014:00:00:07 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

056	// ip address

057	return Arrays.asList(SPACE.split(s)[0]);

058

}

059

});

060

061

// map

062	JavaPairRDD<String, Integer> ones = words.map(new PairFunction<String, String, Integer>() {

063

@Override

064	public Tuple2<String, Integer> call(String s) {

065	return new Tuple2<String, Integer>(s, 1);

066

}

067

});

068

069

// reduce

070	JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {

071

@Override

072	public Integer call(Integer i1, Integer i2) {

073	return i1 + i2;

074

}

075

});

076

077	List<Tuple2<String, Integer>> output = counts.collect();

078

079	// sort statistics result by value

080	Collections.sort(output, new Comparator<Tuple2<String, Integer>>() {

081

@Override

082	public int compare(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) {

083	if(t1._2 < t2._2) {

084

return 1;

085	} else if(t1._2 > t2._2) {

086	return -1;

087

}

088

return 0;

089

}

090

});

091

092	writeTo(args, output);

093

094

}

095

096	private void writeTo(String[] args, List<Tuple2<String, Integer>> output) {

097	for (Tuple2<?, ?> tuple : output) {

098	Country country = lookupService.getCountry((String) tuple._1);

099	LOG.info("[" + country.getCode() + "] " + tuple._1 + " " + tuple._2);

100

}

101

}

102

103	public static void main(String[] args) {

104	// ./bin/run-my-java-example org.shirdrn.spark.job.IPAddressStatsspark://m1:7077 hdfs://m1:9000/user/shirdrn/wwwlog20140222.log/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat

105	if (args.length < 3) {

106	System.err.println("Usage: IPAddressStats <master> <inFile> <GeoIPFile>");

107	System.err.println("    Example: org.shirdrn.spark.job.IPAddressStatsspark://m1:7077 hdfs://m1:9000/user/shirdrn/wwwlog20140222.log/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat");

108	System.exit(1);

109

}

110

111	String geoIPFile = args[2];

112	IPAddressStats stats = new IPAddressStats(geoIPFile);

113	stats.stat(args);

114

115	System.exit(0);

116

117

}

118

119

}

具体实现逻辑，可以参考代码中的注释。我们使用Maven管理构建Java程序，首先看一下我的pom配置中所依赖的软件包，如下所示：

01	<dependencies>

02	<dependency>

03	<groupId>org.apache.spark</groupId>

04	<artifactId>spark-core_2.10</artifactId>

05	<version>0.9.0-incubating</version>

06	</dependency>

07	<dependency>

08	<groupId>log4j</groupId>

09	<artifactId>log4j</artifactId>

10	<version>1.2.16</version>

11	</dependency>

12	<dependency>

13	<groupId>dnsjava</groupId>

14	<artifactId>dnsjava</artifactId>

15	<version>2.1.1</version>

16	</dependency>

17	<dependency>

18	<groupId>commons-net</groupId>

19	<artifactId>commons-net</artifactId>

20	<version>3.1</version>

21	</dependency>

22	<dependency>

23	<groupId>org.apache.hadoop</groupId>

24	<artifactId>hadoop-client</artifactId>

25	<version>1.2.1</version>

26	</dependency>

27	</dependencies>

需要说明的是，当我们将程序在Spark集群上运行时，它要求我们的编写的Job能够进行序列化，如果某些字段不需要序列化或者无法序列化，可以直接使用 transient修饰即可，如上面的属性lookupService没有实现序列化接口，使用transient使其不执行序列化，否则的话，可能会出 现类似如下的错误：

01	14/03/10 22:34:06 INFO scheduler.DAGScheduler: Failed to run collect at IPAddressStats.java:76

02	Exception in thread "main" org.apache.spark.SparkException: Job aborted: Task not serializable: java.io.NotSerializableException: org.shirdrn.spark.job.IPAddressStats

03	at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)

04	at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)

05	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)

06	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)

07	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026)

08	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:794)

09	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:737)

10	at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:741)

11	at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:740)

12	at scala.collection.immutable.List.foreach(List.scala:318)

13	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:740)

14	at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:569)

15	at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)

16	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)

17	at akka.actor.ActorCell.invoke(ActorCell.scala:456)

18	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)

19	at akka.dispatch.Mailbox.run(Mailbox.scala:219)

20	at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)

21	at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)

22	at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)

23	at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)

24	at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

在Spark集群上运行Java程序

这里，我使用了Maven管理构建Java程序，实现上述代码以后，使用Maven的maven-assembly-plugin插件，配置内容如下所示：

01

<plugin>

02	<artifactId>maven-assembly-plugin</artifactId>

03	<configuration>

04

<archive>

05	<manifest>

06	<mainClass>org.shirdrn.spark.job.UserAgentStats</mainClass>

07	</manifest>

08	</archive>

09	<descriptorRefs>

10	<descriptorRef>jar-with-dependencies</descriptorRef>

11	</descriptorRefs>

12	<excludes>

13	<exclude>*.properties</exclude>

14	<exclude>*.xml</exclude>

15	</excludes>

16	</configuration>

17	<executions>

18	<execution>

19	<id>make-assembly</id>

20	<phase>package</phase>

21

<goals>

22	<goal>single</goal>

23

</goals>

24	</execution>

25	</executions>

26

</plugin>

将相关依赖库文件都打进程序包里面，最后拷贝JAR文件到Linux系统下（不一定非要在Spark集群的Master节点上），保证该节点上Spark 的环境变量配置正确即可看。Spark软件发行包解压缩后，可以看到脚本bin/run-example，我们可以直接修改该脚本，将对应的路径指向我们 实现的Java程序包（修改变量EXAMPLES_DIR以及我们的JAR文件存放位置相关的内容），使用该脚本就可以运行，脚本内容如下所示：

01	cygwin=false

02	case "`uname`" in

03	CYGWIN*) cygwin=true;;

04

esac

05

06	SCALA_VERSION=2.10

07

08	# Figure out where the Scala framework is installed

09	FWDIR="$(cd `dirname $0`/..; pwd)"

10

11	# Export this as SPARK_HOME

12	export SPARK_HOME="$FWDIR"

13

14	# Load environment variables from conf/spark-env.sh, if it exists

15	if [ -e "$FWDIR/conf/spark-env.sh" ] ; then

16	. $FWDIR/conf/spark-env.sh

17

fi

18

19	if [ -z "$1" ]; then

20	echo "Usage: run-example <example-class> [<args>]" >&2

21

exit 1

22

fi

23

24	# Figure out the JAR file that our examples were packaged into. This includes a bit of a hack

25	# to avoid the -sources and -doc packages that are built by publish-local.

26	EXAMPLES_DIR="$FWDIR"/java-examples

27	SPARK_EXAMPLES_JAR=""

28	if [ -e "$EXAMPLES_DIR"/*.jar ]; then

29	export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/*.jar`

30

fi

31	if [[ -z $SPARK_EXAMPLES_JAR ]]; then

32	echo "Failed to find Spark examples assembly in $FWDIR/examples/target" >&2

33	echo "You need to build Spark with sbt/sbt assembly before running this program" >&2

34

exit 1

35

fi

36

37

38	# Since the examples JAR ideally shouldn't include spark-core (that dependency should be

39	# "provided"), also add our standard Spark classpath, built using compute-classpath.sh.

40	CLASSPATH=`$FWDIR/bin/compute-classpath.sh`

41	CLASSPATH="$SPARK_EXAMPLES_JAR:$CLASSPATH"

42

43	if $cygwin; then

44	CLASSPATH=`cygpath -wp $CLASSPATH`

45	export SPARK_EXAMPLES_JAR=`cygpath -w $SPARK_EXAMPLES_JAR`

46

fi

47

48	# Find java binary

49	if [ -n "${JAVA_HOME}" ]; then

50	RUNNER="${JAVA_HOME}/bin/java"

51

else

52	if [ `command -v java` ]; then

53	RUNNER="java"

54

else

55	echo "JAVA_HOME is not set" >&2

56

exit 1

57

fi

58

fi

59

60	# Set JAVA_OPTS to be able to load native libraries and to set heap size

61	JAVA_OPTS="$SPARK_JAVA_OPTS"

62	JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH"

63	# Load extra JAVA_OPTS from conf/java-opts, if it exists

64	if [ -e "$FWDIR/conf/java-opts" ] ; then

65	JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`"

66

fi

67	export JAVA_OPTS

68

69	if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then

70	echo -n "Spark Command: "

71	echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"

72	echo "========================================"

73

echo

74

fi

75

76	exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"

在Spark上运行我们开发的Java程序，执行如下命令：

1	cd /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1

2	./bin/run-my-java-example org.shirdrn.spark.job.IPAddressStats spark://m1:7077hdfs://m1:9000/user/shirdrn/wwwlog20140222.log /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat

我实现的程序类org.shirdrn.spark.job.IPAddressStats运行需要3个参数：

1. Spark集群主节点URL：例如我的是spark://m1:7077
2. 输入文件路径：业务相关的，我这里是从HDFS上读取文件hdfs://m1:9000/user/shirdrn/wwwlog20140222.log
3. GeoIP库文件：业务相关的，用来计算IP地址所属国家的外部文件

如果程序没有错误，能够正常运行，控制台输出程序运行日志，示例如下所示：

01	14/03/10 22:17:24 INFO job.IPAddressStats: GeoIP file: /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat

02	SLF4J: Class path contains multiple SLF4J bindings.

03	SLF4J: Found binding in [jar:file:/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/spark-0.0.1-SNAPSHOT-jar-with-dependencies.jar!/org/slf4j/impl/StaticLoggerBinder.class]

04	SLF4J: Found binding in [jar:file:/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/assembly/target/scala-2.10/spark-assembly_2.10-0.9.0-incubating-hadoop1.0.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]

05	SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

06	SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

07	14/03/10 22:17:25 INFO slf4j.Slf4jLogger: Slf4jLogger started

08	14/03/10 22:17:25 INFO Remoting: Starting remoting

09	14/03/10 22:17:25 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://spark@m1:57379]

10	14/03/10 22:17:25 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@m1:57379]

11	14/03/10 22:17:25 INFO spark.SparkEnv: Registering BlockManagerMaster

12	14/03/10 22:17:25 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-local-20140310221725-c1cb

13	14/03/10 22:17:25 INFO storage.MemoryStore: MemoryStore started with capacity 143.8 MB.

14	14/03/10 22:17:25 INFO network.ConnectionManager: Bound socket to port 45189 with id = ConnectionManagerId(m1,45189)

15	14/03/10 22:17:25 INFO storage.BlockManagerMaster: Trying to register BlockManager

16	14/03/10 22:17:25 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager m1:45189 with 143.8 MB RAM

17	14/03/10 22:17:25 INFO storage.BlockManagerMaster: Registered BlockManager

18	14/03/10 22:17:25 INFO spark.HttpServer: Starting HTTP Server

19	14/03/10 22:17:25 INFO server.Server: jetty-7.x.y-SNAPSHOT

20	14/03/10 22:17:25 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:49186

21	14/03/10 22:17:25 INFO broadcast.HttpBroadcast: Broadcast server started athttp://10.95.3.56:49186

22	14/03/10 22:17:25 INFO spark.SparkEnv: Registering MapOutputTracker

23	14/03/10 22:17:25 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-56c3e30d-a01b-4752-83d1-af1609ab2370

24	14/03/10 22:17:25 INFO spark.HttpServer: Starting HTTP Server

25	14/03/10 22:17:25 INFO server.Server: jetty-7.x.y-SNAPSHOT

26	14/03/10 22:17:25 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:52073

27	14/03/10 22:17:26 INFO server.Server: jetty-7.x.y-SNAPSHOT

28	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage/rdd,null}

29	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage,null}

30	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/stage,null}

31	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/pool,null}

32	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages,null}

33	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/environment,null}

34	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/executors,null}

35	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/metrics/json,null}

36	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/static,null}

37	14/03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/,null}

38	14/03/10 22:17:26 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040

39	14/03/10 22:17:26 INFO ui.SparkUI: Started Spark Web UI at http://m1:4040

40	14/03/10 22:17:26 INFO spark.SparkContext: Added JAR /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/spark-0.0.1-SNAPSHOT-jar-with-dependencies.jar at http://10.95.3.56:52073/jars/spark-0.0.1-SNAPSHOT-jar-with-dependencies.jar with timestamp 1394515046396

41	14/03/10 22:17:26 INFO client.AppClient$ClientActor: Connecting to masterspark://m1:7077...

42	14/03/10 22:17:26 INFO storage.MemoryStore: ensureFreeSpace(60341) called with curMem=0, maxMem=150837657

43	14/03/10 22:17:26 INFO storage.MemoryStore: Block broadcast_0 stored as values to memory (estimated size 58.9 KB, free 143.8 MB)

44	14/03/10 22:17:26 INFO cluster.SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20140310221726-0000

45	14/03/10 22:17:27 INFO client.AppClient$ClientActor: Executor added: app-20140310221726-0000/0 on worker-20140310221648-s1-52544 (s1:52544) with 1 cores

46	14/03/10 22:17:27 INFO cluster.SparkDeploySchedulerBackend: Granted executor ID app-20140310221726-0000/0 on hostPort s1:52544 with 1 cores, 512.0 MB RAM

47	14/03/10 22:17:27 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

48	14/03/10 22:17:27 WARN snappy.LoadSnappy: Snappy native library not loaded

49	14/03/10 22:17:27 INFO client.AppClient$ClientActor: Executor updated: app-20140310221726-0000/0 is now RUNNING

50	14/03/10 22:17:27 INFO mapred.FileInputFormat: Total input paths to process : 1

51	14/03/10 22:17:27 INFO spark.SparkContext: Starting job: collect at IPAddressStats.java:77

52	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Registering RDD 4 (reduceByKey at IPAddressStats.java:70)

53	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Got job 0 (collect at IPAddressStats.java:77) with 1 output partitions (allowLocal=false)

54	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Final stage: Stage 0 (collect at IPAddressStats.java:77)

55	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Parents of final stage: List(Stage 1)

56	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Missing parents: List(Stage 1)

57	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Submitting Stage 1 (MapPartitionsRDD[4] at reduceByKey at IPAddressStats.java:70), which has no missing parents

58	14/03/10 22:17:27 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 1 (MapPartitionsRDD[4] at reduceByKey at IPAddressStats.java:70)

59	14/03/10 22:17:27 INFO scheduler.TaskSchedulerImpl: Adding task set 1.0 with 1 tasks

60	14/03/10 22:17:28 INFO cluster.SparkDeploySchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@s1:59233/user/Executor#-671170811] with ID 0

61	14/03/10 22:17:28 INFO scheduler.TaskSetManager: Starting task 1.0:0 as TID 0 on executor 0: s1 (PROCESS_LOCAL)

62	14/03/10 22:17:28 INFO scheduler.TaskSetManager: Serialized task 1.0:0 as 2396 bytes in 5 ms

63	14/03/10 22:17:29 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager s1:47282 with 297.0 MB RAM

64	14/03/10 22:17:32 INFO scheduler.TaskSetManager: Finished TID 0 in 3376 ms on s1 (progress: 0/1)

65	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Completed ShuffleMapTask(1, 0)

66	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Stage 1 (reduceByKey at IPAddressStats.java:70) finished in 4.420 s

67	14/03/10 22:17:32 INFO scheduler.DAGScheduler: looking for newly runnable stages

68	14/03/10 22:17:32 INFO scheduler.DAGScheduler: running: Set()

69	14/03/10 22:17:32 INFO scheduler.DAGScheduler: waiting: Set(Stage 0)

70	14/03/10 22:17:32 INFO scheduler.DAGScheduler: failed: Set()

71	14/03/10 22:17:32 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 1.0 from pool

72	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Missing parents for Stage 0: List()

73	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Submitting Stage 0 (MapPartitionsRDD[6] at reduceByKey at IPAddressStats.java:70), which is now runnable

74	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 0 (MapPartitionsRDD[6] at reduceByKey at IPAddressStats.java:70)

75	14/03/10 22:17:32 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 1 tasks

76	14/03/10 22:17:32 INFO scheduler.TaskSetManager: Starting task 0.0:0 as TID 1 on executor 0: s1 (PROCESS_LOCAL)

77	14/03/10 22:17:32 INFO scheduler.TaskSetManager: Serialized task 0.0:0 as 2255 bytes in 1 ms

78	14/03/10 22:17:32 INFO spark.MapOutputTrackerMasterActor: Asked to send map output locations for shuffle 0 to spark@s1:33534

79	14/03/10 22:17:32 INFO spark.MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 120 bytes

80	14/03/10 22:17:32 INFO scheduler.TaskSetManager: Finished TID 1 in 282 ms on s1 (progress: 0/1)

81	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Completed ResultTask(0, 0)

82	14/03/10 22:17:32 INFO scheduler.DAGScheduler: Stage 0 (collect at IPAddressStats.java:77) finished in 0.314 s

83	14/03/10 22:17:32 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 0.0 from pool

84	14/03/10 22:17:32 INFO spark.SparkContext: Job finished: collect at IPAddressStats.java:77, took 4.870958309 s

85	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 58.246.49.218     312

86	14/03/10 22:17:32 INFO job.IPAddressStats: [KR] 1.234.83.77     300

87	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.43.11.16     212

88	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 110.85.72.254     207

89	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 27.150.229.134     185

90	14/03/10 22:17:32 INFO job.IPAddressStats: [HK] 180.178.52.181     181

91	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.37.210.212     180

92	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 222.77.226.83     176

93	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.43.11.205     169

94	14/03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.43.9.19     165

95

...

我们也可以通过Web控制台来查看当前执行应用程序（Application）的状态信息，通过Master节点的8080端口（如：http://m1:8080/）就能看到集群的应用程序（Application）状态信息。
另外，需要说明的时候，如果在Unix环境下使用Eclipse使用Java开发Spark应用程序，也能够直接通过Eclipse连接Spark集群，并提交开发的应用程序，然后交给集群去处理。

参考链接

• https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples
• http://spark.apache.org/docs/latest/java-programming-guide.html