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:

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:

  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:

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。

参考链接

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