spark应用程序如何在Java项目中运行

发布时间：2020-12-08 15:40:21

来源：亿速云

阅读：97

作者：Leah

这篇文章将为大家详细讲解有关spark应用程序如何在Java项目中运行，文章内容质量较高，因此小编分享给大家做个参考，希望大家阅读完这篇文章后对相关知识有一定的了解。

如下所示：

package org.shirdrn.spark.job;

import java.io.File;

import java.io.IOException;

import java.util.Arrays;

import java.util.Collections;

import java.util.Comparator;

import java.util.List;

import java.util.regex.Pattern;

import org.apache.commons.logging.Log;

import org.apache.commons.logging.LogFactory;

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

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

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

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.shirdrn.spark.job.maxmind.Country;

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

import scala.Serializable;

import scala.Tuple2;

public class IPAddressStats implements Serializable {

private static final long serialVersionUID = 8533489548835413763L;

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

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

private transient LookupService lookupService;

private transient final String geoIPFile;

public IPAddressStats(String geoIPFile) {

this.geoIPFile = geoIPFile;

try {

// lookupService: get country code from a IP address

File file = new File(this.geoIPFile);

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

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

} catch (IOException e) {

throw new RuntimeException(e);

}

}

@SuppressWarnings("serial")

public void stat(String[] args) {

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

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

JavaRDD lines = ctx.textFile(args[1], 1);

// splits and extracts ip address filed

JavaRDD words = lines.flatMap(new FlatMapFunction() {

@Override

public Iterable call(String s) {

// 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"

// ip address

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

}

});

// map

JavaPairRDD ones = words.map(new PairFunction() {

@Override

public Tuple2 call(String s) {

return new Tuple2(s, 1);

}

});

// reduce

JavaPairRDD counts = ones.reduceByKey(new Function2() {

@Override

public Integer call(Integer i1, Integer i2) {

return i1 + i2;

}

});

List> output = counts.collect();

// sort statistics result by value

Collections.sort(output, new Comparator>() {

@Override

public int compare(Tuple2 t1, Tuple2 t2) {

if(t1._2 < t2._2) {

return 1;

} else if(t1._2 > t2._2) {

return -1;

}

return 0;

}

});

writeTo(args, output);

}

private void writeTo(String[] args, List> output) {

for (Tuple2 tuple : output) {

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

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

}

}

public static void main(String[] args) {

// ./bin/run-my-java-example org.shirdrn.spark.job.IPAddressStats spark://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

if (args.length < 3) {

System.err.println("Usage: IPAddressStats ");

System.err.println(" Example: org.shirdrn.spark.job.IPAddressStats spark://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");

System.exit(1);

}

String geoIPFile = args[2];

IPAddressStats stats = new IPAddressStats(geoIPFile);

stats.stat(args);

System.exit(0);

}

}

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：

org.apache.spark

spark-core_2.10

0.9.0-incubating

log4j

log4j

1.2.16

dnsjava

dnsjava

2.1.1

commons-net

commons-net

3.1

org.apache.hadoop

hadoop-client

1.2.1

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：

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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：

maven-assembly-plugin

org.shirdrn.spark.job.UserAgentStats

jar-with-dependencies

*.properties

*.xml

make-assembly

package

single

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：

cygwin=false

case "`uname`" in

CYGWIN*) cygwin=true;;

esac

SCALA_VERSION=2.10

# Figure out where the Scala framework is installed

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

# Export this as SPARK_HOME

export SPARK_HOME="$FWDIR"

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

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

. $FWDIR/conf/spark-env.sh

fi

if [ -z "$1" ]; then

echo "Usage: run-example []" >&2

exit 1

fi

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

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

EXAMPLES_DIR="$FWDIR"/java-examples

SPARK_EXAMPLES_JAR=""

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

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

fi

if [[ -z $SPARK_EXAMPLES_JAR ]]; then

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

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

exit 1

fi

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

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

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

CLASSPATH="$SPARK_EXAMPLES_JAR:$CLASSPATH"

if $cygwin; then

CLASSPATH=`cygpath -wp $CLASSPATH`

export SPARK_EXAMPLES_JAR=`cygpath -w $SPARK_EXAMPLES_JAR`

fi

# Find java binary

if [ -n "${JAVA_HOME}" ]; then

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

else

if [ `command -v java` ]; then

RUNNER="java"

else

echo "JAVA_HOME is not set" >&2

exit 1

fi

fi

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

JAVA_OPTS="$SPARK_JAVA_OPTS"

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

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

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

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

fi

export JAVA_OPTS

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

echo -n "Spark Command: "

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

echo "========================================"

echo

fi

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：

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

./bin/run-my-java-example org.shirdrn.spark.job.IPAddressStats spark://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

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

Spark集群主节点URL：例如我的是spark://m1:7077

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

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：

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

SLF4J: Class path contains multiple SLF4J bindings.

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]

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]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

...

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。

关于spark应用程序如何在Java项目中运行就分享到这里了，希望以上内容可以对大家有一定的帮助，可以学到更多知识。如果觉得文章不错，可以把它分享出去让更多的人看到。