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使用Java编写并运行Spark应用程序

xioaxu790 2014-12-24 20:03:58 发表于 实操演练 [显示全部楼层] 回帖奖励 阅读模式 关闭右栏 0 49649
问题导读
1、统计分析程序需要实现哪几个功能点？
2、要分析某网站的访问日志信息思路怎样？
3、怎样通过Web控制台来查看当前执行应用程序的状态信息？






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. .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. 

  3. .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"

  4. 

  5. .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"

  6. 

  7. .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"

  8. 

  9. .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"

  10. 

  11. .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"
复制代码


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：
  1. package org.shirdrn.spark.job;

  2. 

  3. import java.io.File;

  4. import java.io.IOException;

  5. import java.util.Arrays;

  6. import java.util.Collections;

  7. import java.util.Comparator;

  8. import java.util.List;

  9. import java.util.regex.Pattern;

  10. 

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

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

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

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

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

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

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

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

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

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

  21. 

  22. import scala.Serializable;

  23. import scala.Tuple2;

  24. 

  25. public class IPAddressStats implements Serializable {

  26. 

  27.      private static final long serialVersionUID = 8533489548835413763L;

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

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

  30.      private transient LookupService lookupService;

  31.      private transient final String geoIPFile;

  32. 

  33.      public IPAddressStats(String geoIPFile) {

  34.           this.geoIPFile = geoIPFile;

  35.           try {

  36.                // lookupService: get country code from a IP address

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

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

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

  40.           } catch (IOException e) {

  41.                throw new RuntimeException(e);

  42.           }

  43.      }

  44. 

  45.      @SuppressWarnings("serial")

  46.      public void stat(String[] args) {

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

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

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

  50. 

  51.           // splits and extracts ip address filed

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

  53.                @Override

  54.                public Iterable<String> call(String s) {

  55.                     // 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"

  56.                     // ip address

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

  58.                }

  59.           });

  60. 

  61.           // map

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

  63.                @Override

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

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

  66.                }

  67.           });

  68. 

  69.           // reduce

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

  71.                @Override

  72.                public Integer call(Integer i1, Integer i2) {

  73.                     return i1 + i2;

  74.                }

  75.           });

  76. 

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

  78.           // sort statistics result by value

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

  80.                @Override

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

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

  83.                          return 1;

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

  85.                          return -1;

  86.                     }

  87.                     return 0;

  88.                }

  89.           });

  90.           writeTo(args, output);

  91.      }

  92. 

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

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

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

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

  97.           }

  98.      }

  99. 

  100. 

  101.      public static void main(String[] args) {

  102.           // ./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

  103.           if (args.length < 3) {

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

  105.                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");

  106.                System.exit(1);

  107.           }

  108.           String geoIPFile = args[2];

  109.           IPAddressStats stats = new IPAddressStats(geoIPFile);

  110.           stats.stat(args);

  111.           System.exit(0);

  112.      }

  113. }
复制代码



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：
  1. <dependencies>

  2. 

  3.           <dependency>

  4. 

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

  6. 

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

  8. 

  9.                <version>0.9.0-incubating</version>

  10. 

  11.           </dependency>

  12. 

  13.           <dependency>

  14. 

  15.                <groupId>log4j</groupId>

  16. 

  17.                <artifactId>log4j</artifactId>

  18. 

  19.                <version>1.2.16</version>

  20. 

  21.           </dependency>

  22. 

  23.           <dependency>

  24. 

  25.                <groupId>dnsjava</groupId>

  26. 

  27.                <artifactId>dnsjava</artifactId>

  28. 

  29.                <version>2.1.1</version>

  30. 

  31.           </dependency>

  32. 

  33.           <dependency>

  34. 

  35.                <groupId>commons-net</groupId>

  36. 

  37.                <artifactId>commons-net</artifactId>

  38. 

  39.                <version>3.1</version>

  40. 

  41.           </dependency>

  42. 

  43.           <dependency>

  44. 

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

  46. 

  47.                <artifactId>hadoop-client</artifactId>

  48. 

  49.                <version>1.2.1</version>

  50. 

  51.           </dependency>

  52. 

  53.      </dependencies>
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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：
  1. /03/10 22:34:06 INFO scheduler.DAGScheduler: Failed to run collect at IPAddressStats.java:76

  2. 

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

  4. 

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

  6. 

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

  8. 

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

  10. 

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

  12. 

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

  14. 

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

  16. 

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

  18. 

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

  20. 

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

  22. 

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

  24. 

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

  26. 

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

  28. 

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

  30. 

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

  32. 

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

  34. 

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

  36. 

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

  38. 

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

  40. 

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

  42. 

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

  44. 

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

  46. 

  47.      at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
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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：
  1. <plugin>

  2.      <artifactId>maven-assembly-plugin</artifactId>

  3.      <configuration>

  4.           <archive>

  5.                <manifest>

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

  7.                </manifest>

  8.           </archive>

  9.           <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>
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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：
  1. cygwin=false

  2. case "`uname`" in

  3.     CYGWIN*) cygwin=true;;

  4. esac

  5. SCALA_VERSION=2.10

  6. # Figure out where the Scala framework is installed

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

  8. 

  9. # Export this as SPARK_HOME

  10. export SPARK_HOME="$FWDIR"

  11. 

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

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

  14.   . $FWDIR/conf/spark-env.sh

  15. fi

  16. 

  17. if [ -z "$1" ]; then

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

  19.   exit 1

  20. fi

  21. 

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

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

  24. EXAMPLES_DIR="$FWDIR"/java-examples

  25. SPARK_EXAMPLES_JAR=""

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

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

  28. fi

  29. if [[ -z $SPARK_EXAMPLES_JAR ]]; then

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

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

  32.   exit 1

  33. fi

  34. 

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

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

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

  38. CLASSPATH="$SPARK_EXAMPLES_JAR:$CLASSPATH"

  39. 

  40. if $cygwin; then

  41.     CLASSPATH=`cygpath -wp $CLASSPATH`

  42.     export SPARK_EXAMPLES_JAR=`cygpath -w $SPARK_EXAMPLES_JAR`

  43. fi

  44. 

  45. # Find java binary

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

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

  48. else

  49.   if [ `command -v java` ]; then

  50.     RUNNER="java"

  51.   else

  52.     echo "JAVA_HOME is not set" >&2

  53.     exit 1

  54.   fi

  55. fi

  56. 

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

  58. JAVA_OPTS="$SPARK_JAVA_OPTS"

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

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

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

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

  63. fi

  64. export JAVA_OPTS

  65. 

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

  67.   echo -n "Spark Command: "

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

  69.   echo "========================================"

  70.   echo

  71. fi
复制代码


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. 

  3. ./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
复制代码


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：
  1. /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

  2. 

  3. SLF4J: Class path contains multiple SLF4J bindings.

  4. 

  5. 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]

  6. 

  7. 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]

  8. 

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

  10. 

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

  12. 

  13. /03/10 22:17:25 INFO slf4j.Slf4jLogger: Slf4jLogger started

  14. 

  15. /03/10 22:17:25 INFO Remoting: Starting remoting

  16. 

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

  18. 

  19. /03/10 22:17:25 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@m1:57379]

  20. 

  21. /03/10 22:17:25 INFO spark.SparkEnv: Registering BlockManagerMaster

  22. 

  23. /03/10 22:17:25 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-local-20140310221725-c1cb

  24. 

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

  26. 

  27. /03/10 22:17:25 INFO network.ConnectionManager: Bound socket to port 45189 with id = ConnectionManagerId(m1,45189)

  28. 

  29. /03/10 22:17:25 INFO storage.BlockManagerMaster: Trying to register BlockManager

  30. 

  31. /03/10 22:17:25 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager m1:45189 with 143.8 MB RAM

  32. 

  33. /03/10 22:17:25 INFO storage.BlockManagerMaster: Registered BlockManager

  34. 

  35. /03/10 22:17:25 INFO spark.HttpServer: Starting HTTP Server

  36. 

  37. /03/10 22:17:25 INFO server.Server: jetty-7.x.y-SNAPSHOT

  38. 

  39. /03/10 22:17:25 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:49186

  40. 

  41. /03/10 22:17:25 INFO broadcast.HttpBroadcast: Broadcast server started at http://10.95.3.56:49186

  42. 

  43. /03/10 22:17:25 INFO spark.SparkEnv: Registering MapOutputTracker

  44. 

  45. /03/10 22:17:25 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-56c3e30d-a01b-4752-83d1-af1609ab2370

  46. 

  47. /03/10 22:17:25 INFO spark.HttpServer: Starting HTTP Server

  48. 

  49. /03/10 22:17:25 INFO server.Server: jetty-7.x.y-SNAPSHOT

  50. 

  51. /03/10 22:17:25 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:52073

  52. 

  53. /03/10 22:17:26 INFO server.Server: jetty-7.x.y-SNAPSHOT

  54. 

  55. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage/rdd,null}

  56. 

  57. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage,null}

  58. 

  59. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/stage,null}

  60. 

  61. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/pool,null}

  62. 

  63. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages,null}

  64. 

  65. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/environment,null}

  66. 

  67. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/executors,null}

  68. 

  69. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/metrics/json,null}

  70. 

  71. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/static,null}

  72. 

  73. /03/10 22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/,null}

  74. 

  75. /03/10 22:17:26 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040

  76. 

  77. /03/10 22:17:26 INFO ui.SparkUI: Started Spark Web UI at http://m1:4040

  78. 

  79. /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

  80. 

  81. /03/10 22:17:26 INFO client.AppClient$ClientActor: Connecting to master spark://m1:7077...

  82. 

  83. /03/10 22:17:26 INFO storage.MemoryStore: ensureFreeSpace(60341) called with curMem=0, maxMem=150837657

  84. 

  85. /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)

  86. 

  87. /03/10 22:17:26 INFO cluster.SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20140310221726-0000

  88. 

  89. /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

  90. 

  91. /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

  92. 

  93. /03/10 22:17:27 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

  94. 

  95. /03/10 22:17:27 WARN snappy.LoadSnappy: Snappy native library not loaded

  96. 

  97. /03/10 22:17:27 INFO client.AppClient$ClientActor: Executor updated: app-20140310221726-0000/0 is now RUNNING

  98. 

  99. /03/10 22:17:27 INFO mapred.FileInputFormat: Total input paths to process : 1

  100. 

  101. /03/10 22:17:27 INFO spark.SparkContext: Starting job: collect at IPAddressStats.java:77

  102. 

  103. /03/10 22:17:27 INFO scheduler.DAGScheduler: Registering RDD 4 (reduceByKey at IPAddressStats.java:70)

  104. 

  105. /03/10 22:17:27 INFO scheduler.DAGScheduler: Got job 0 (collect at IPAddressStats.java:77) with 1 output partitions (allowLocal=false)

  106. 

  107. /03/10 22:17:27 INFO scheduler.DAGScheduler: Final stage: Stage 0 (collect at IPAddressStats.java:77)

  108. 

  109. /03/10 22:17:27 INFO scheduler.DAGScheduler: Parents of final stage: List(Stage 1)

  110. 

  111. /03/10 22:17:27 INFO scheduler.DAGScheduler: Missing parents: List(Stage 1)

  112. 

  113. /03/10 22:17:27 INFO scheduler.DAGScheduler: Submitting Stage 1 (MapPartitionsRDD[4] at reduceByKey at IPAddressStats.java:70), which has no missing parents

  114. 

  115. /03/10 22:17:27 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 1 (MapPartitionsRDD[4] at reduceByKey at IPAddressStats.java:70)

  116. 

  117. /03/10 22:17:27 INFO scheduler.TaskSchedulerImpl: Adding task set 1.0 with 1 tasks

  118. 

  119. /03/10 22:17:28 INFO cluster.SparkDeploySchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@s1:59233/user/Executor#-671170811] with ID 0

  120. 

  121. /03/10 22:17:28 INFO scheduler.TaskSetManager: Starting task 1.0:0 as TID 0 on executor 0: s1 (PROCESS_LOCAL)

  122. 

  123. /03/10 22:17:28 INFO scheduler.TaskSetManager: Serialized task 1.0:0 as 2396 bytes in 5 ms

  124. 

  125. /03/10 22:17:29 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager s1:47282 with 297.0 MB RAM

  126. 

  127. /03/10 22:17:32 INFO scheduler.TaskSetManager: Finished TID 0 in 3376 ms on s1 (progress: 0/1)

  128. 

  129. /03/10 22:17:32 INFO scheduler.DAGScheduler: Completed ShuffleMapTask(1, 0)

  130. 

  131. /03/10 22:17:32 INFO scheduler.DAGScheduler: Stage 1 (reduceByKey at IPAddressStats.java:70) finished in 4.420 s

  132. 

  133. /03/10 22:17:32 INFO scheduler.DAGScheduler: looking for newly runnable stages

  134. 

  135. /03/10 22:17:32 INFO scheduler.DAGScheduler: running: Set()

  136. 

  137. /03/10 22:17:32 INFO scheduler.DAGScheduler: waiting: Set(Stage 0)

  138. 

  139. /03/10 22:17:32 INFO scheduler.DAGScheduler: failed: Set()

  140. 

  141. /03/10 22:17:32 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 1.0 from pool

  142. 

  143. /03/10 22:17:32 INFO scheduler.DAGScheduler: Missing parents for Stage 0: List()

  144. 

  145. /03/10 22:17:32 INFO scheduler.DAGScheduler: Submitting Stage 0 (MapPartitionsRDD[6] at reduceByKey at IPAddressStats.java:70), which is now runnable

  146. 

  147. /03/10 22:17:32 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from Stage 0 (MapPartitionsRDD[6] at reduceByKey at IPAddressStats.java:70)

  148. 

  149. /03/10 22:17:32 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 1 tasks

  150. 

  151. /03/10 22:17:32 INFO scheduler.TaskSetManager: Starting task 0.0:0 as TID 1 on executor 0: s1 (PROCESS_LOCAL)

  152. 

  153. /03/10 22:17:32 INFO scheduler.TaskSetManager: Serialized task 0.0:0 as 2255 bytes in 1 ms

  154. 

  155. /03/10 22:17:32 INFO spark.MapOutputTrackerMasterActor: Asked to send map output locations for shuffle 0 to spark@s1:33534

  156. 

  157. /03/10 22:17:32 INFO spark.MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 120 bytes

  158. 

  159. /03/10 22:17:32 INFO scheduler.TaskSetManager: Finished TID 1 in 282 ms on s1 (progress: 0/1)

  160. 

  161. /03/10 22:17:32 INFO scheduler.DAGScheduler: Completed ResultTask(0, 0)

  162. 

  163. /03/10 22:17:32 INFO scheduler.DAGScheduler: Stage 0 (collect at IPAddressStats.java:77) finished in 0.314 s

  164. 

  165. /03/10 22:17:32 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 0.0 from pool

  166. 

  167. /03/10 22:17:32 INFO spark.SparkContext: Job finished: collect at IPAddressStats.java:77, took 4.870958309 s

  168. 

  169. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 58.246.49.218     312

  170. 

  171. /03/10 22:17:32 INFO job.IPAddressStats: [KR] 1.234.83.77     300

  172. 

  173. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.43.11.16     212

  174. 

  175. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 110.85.72.254     207

  176. 

  177. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 27.150.229.134     185

  178. 

  179. /03/10 22:17:32 INFO job.IPAddressStats: [HK] 180.178.52.181     181

  180. 

  181. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.37.210.212     180

  182. 

  183. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 222.77.226.83     176

  184. 

  185. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.43.11.205     169

  186. 

  187. /03/10 22:17:32 INFO job.IPAddressStats: [CN] 120.43.9.19     165

  188. ...
复制代码



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。


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