详解Java编写并运行spark应用程序的方法

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：

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"
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"
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"
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"
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"
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：

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<string> lines = ctx.textfile(args[1], 1);
   // splits and extracts ip address filed
   javardd<string> words = lines.flatmap(new flatmapfunction<string, string>() {
    @override
    public iterable<string> 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<string, integer> ones = words.map(new pairfunction<string, string, integer>() {
    @override
    public tuple2<string, integer> call(string s) {
     return new tuple2<string, integer>(s, 1);
    }
   });
   // reduce
   javapairrdd<string, integer> counts = ones.reducebykey(new function2<integer, integer, integer>() {
    @override
    public integer call(integer i1, integer i2) {
     return i1 + i2;
    }
   });
   list<tuple2<string, integer>> output = counts.collect();
   // sort statistics result by value
   collections.sort(output, new comparator<tuple2<string, integer>>() {
    @override
    public int compare(tuple2<string, integer> t1, tuple2<string, integer> 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<tuple2<string, integer>> 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 <master> <infile> <geoipfile>");
    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：

<dependencies>
   <dependency>
    <groupid>org.apache.spark</groupid>
    <artifactid>spark-core_2.10</artifactid>
    <version>0.9.0-incubating</version>
   </dependency>
   <dependency>
    <groupid>log4j</groupid>
    <artifactid>log4j</artifactid>
    <version>1.2.16</version>
   </dependency>
   <dependency>
    <groupid>dnsjava</groupid>
    <artifactid>dnsjava</artifactid>
    <version>2.1.1</version>
   </dependency>
   <dependency>
    <groupid>commons-net</groupid>
    <artifactid>commons-net</artifactid>
    <version>3.1</version>
   </dependency>
   <dependency>
    <groupid>org.apache.hadoop</groupid>
    <artifactid>hadoop-client</artifactid>
    <version>1.2.1</version>
   </dependency>
  </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：

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：

<plugin>
  <artifactid>maven-assembly-plugin</artifactid>
  <configuration>
   <archive>
    <manifest>
     <mainclass>org.shirdrn.spark.job.useragentstats</mainclass>
    </manifest>
   </archive>
   <descriptorrefs>
    <descriptorref>jar-with-dependencies</descriptorref>
   </descriptorrefs>
   <excludes>
    <exclude>*.properties</exclude>
    <exclude>*.xml</exclude>
   </excludes>
  </configuration>
  <executions>
   <execution>
    <id>make-assembly</id>
    <phase>package</phase>
    <goals>
     <goal>single</goal>
    </goals>
   </execution>
  </executions>
</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：

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 "$1" ]; then
 echo "usage: run-example <example-class> [<args>]" >&2
 exit 1
fi
# figure out the jar file that our examples were packaged into. this includes a bit of a hack
# to avoid the -sources and -doc packages that are built by publish-local.
examples_dir="$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。

总结

以上就是本文关于详解java编写并运行spark应用程序的方法的全部内容，希望对大家有所帮助。有什么问题可以随时留言，小编会及时回复大家。