使用Java编写并运行Spark应用程序

时间：2023-03-09 15:11:37
使用Java编写并运行Spark应用程序

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

1 121.205.198.92
- - [21/Feb/2014:00:00:07 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"
2 121.205.198.92
- - [21/Feb/2014:00:00:11 +0800] "POST /wp-comments-post.php HTTP/1.1" 302 26 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:23.0) Gecko/20100101 Firefox/23.0"
3 121.205.198.92
- - [21/Feb/2014:00:00:12 +0800] "GET /archives/417.html/ HTTP/1.1" 301 26 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"
4 121.205.198.92
- - [21/Feb/2014:00:00:12 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"
5 121.205.241.229
- - [21/Feb/2014:00:00:13 +0800] "GET /archives/526.html HTTP/1.1" 200 12080 "http://shiyanjun.cn/archives/526.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"
6 121.205.241.229
- - [21/Feb/2014:00:00:15 +0800] "POST /wp-comments-post.php HTTP/1.1" 302 26 "http://shiyanjun.cn/archives/526.html/" "Mozilla/5.0 (Windows NT 5.1; rv:23.0) Gecko/20100101 Firefox/23.0"

Java实现Spark应用程序（Application）

我们实现的统计分析程序，有如下几个功能点：

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

下面，看我们使用Java实现的统计分析应用程序代码，如下所示：

001 package org.shirdrn.spark.job;
002  
003 import java.io.File;
004 import java.io.IOException;
005 import java.util.Arrays;
006 import java.util.Collections;
007 import java.util.Comparator;
008 import java.util.List;
009 import java.util.regex.Pattern;
010  
011 import org.apache.commons.logging.Log;
012 import org.apache.commons.logging.LogFactory;
013 import org.apache.spark.api.java.JavaPairRDD;
014 import org.apache.spark.api.java.JavaRDD;
015 import org.apache.spark.api.java.JavaSparkContext;
016 import org.apache.spark.api.java.function.FlatMapFunction;
017 import org.apache.spark.api.java.function.Function2;
018 import org.apache.spark.api.java.function.PairFunction;
019 import org.shirdrn.spark.job.maxmind.Country;
020 import org.shirdrn.spark.job.maxmind.LookupService;
021  
022 import scala.Serializable;
023 import scala.Tuple2;
024  
025 public class IPAddressStats implements Serializable
{
026  
027      private static final long serialVersionUID
= 8533489548835413763L;
028      private static final Log
LOG = LogFactory.getLog(IPAddressStats.class);
029      private static final Pattern
SPACE = Pattern.compile("
");
030      private transient LookupService
lookupService;
031      private transient final String
geoIPFile;
032      
033      public IPAddressStats(String
geoIPFile) {
034           this.geoIPFile
= geoIPFile;
035           try {
036                //
lookupService: get country code from a IP address
037                File
file = new File(this.geoIPFile);
038                LOG.info("GeoIP
file: " +
file.getAbsolutePath());
039                lookupService
= new AdvancedLookupService(file,
LookupService.GEOIP_MEMORY_CACHE);
040           } catch (IOException
e) {
041                throw new RuntimeException(e);
042           }
043      }
044      
045      @SuppressWarnings("serial")
046      public void stat(String[]
args) {
047           JavaSparkContext
ctx = new JavaSparkContext(args[0], "IPAddressStats",
048                     System.getenv("SPARK_HOME"),
JavaSparkContext.jarOfClass(IPAddressStats.class));
049           JavaRDD<String>
lines = ctx.textFile(args[1], 1);
050  
051           //
splits and extracts ip address filed
052           JavaRDD<String>
words = lines.flatMap(new FlatMapFunction<String,
String>() {
053                @Override
054                public Iterable<String>
call(String s) {
055                     //
121.205.198.92 - - [21/Feb/2014:00:00:07 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101
Firefox/11.0"
056                     //
ip address
057                     return Arrays.asList(SPACE.split(s)[0]);
058                }
059           });
060  
061           //
map
062           JavaPairRDD<String,
Integer> ones = words.map(new PairFunction<String,
String, Integer>() {
063                @Override
064                public Tuple2<String,
Integer> call(String s) {
065                     return new Tuple2<String,
Integer>(s, 1);
066                }
067           });
068  
069           //
reduce
070           JavaPairRDD<String,
Integer> counts = ones.reduceByKey(new Function2<Integer,
Integer, Integer>() {
071                @Override
072                public Integer
call(Integer i1, Integer i2) {
073                     return i1
+ i2;
074                }
075           });
076           
077           List<Tuple2<String,
Integer>> output = counts.collect();
078           
079           //
sort statistics result by value
080           Collections.sort(output, new Comparator<Tuple2<String,
Integer>>() {
081                @Override
082                public int compare(Tuple2<String,
Integer> t1, Tuple2<String, Integer> t2) {
083                     if(t1._2
< t2._2) {
084                          return 1;
085                     } else if(t1._2
> t2._2) {
086                          return -1;
087                     }
088                     return 0;
089                }
090           });
091           
092           writeTo(args,
output);
093           
094      }
095      
096      private void writeTo(String[]
args, List<Tuple2<String, Integer>> output) {
097           for (Tuple2<?,
?> tuple : output) {
098                Country
country = lookupService.getCountry((String) tuple._1);
099                LOG.info("[" +
country.getCode() + "]
" +
tuple._1 + "\t" +
tuple._2);
100           }
101      }
102      
103      public static void main(String[]
args) {
104           //
./bin/run-my-java-example org.shirdrn.spark.job.IPAddressStatsspark://m1:7077 hdfs://m1:9000/user/shirdrn/wwwlog20140222.log/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat
105           if (args.length
< 3)
{
106                System.err.println("Usage:
IPAddressStats <master> <inFile> <GeoIPFile>");
107                System.err.println("   
Example: org.shirdrn.spark.job.IPAddressStatsspark://m1:7077 hdfs://m1:9000/user/shirdrn/wwwlog20140222.log/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat");
108                System.exit(1);
109           }
110           
111           String
geoIPFile = args[2];
112           IPAddressStats
stats = new IPAddressStats(geoIPFile);
113           stats.stat(args);
114           
115           System.exit(0);
116  
117      }
118  
119 }

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

01 <dependencies>
02           <dependency>
03                <groupId>org.apache.spark</groupId>
04                <artifactId>spark-core_2.10</artifactId>
05                <version>0.9.0-incubating</version>
06           </dependency>
07           <dependency>
08                <groupId>log4j</groupId>
09                <artifactId>log4j</artifactId>
10                <version>1.2.16</version>
11           </dependency>
12           <dependency>
13                <groupId>dnsjava</groupId>
14                <artifactId>dnsjava</artifactId>
15                <version>2.1.1</version>
16           </dependency>
17           <dependency>
18                <groupId>commons-net</groupId>
19                <artifactId>commons-net</artifactId>
20                <version>3.1</version>
21           </dependency>
22           <dependency>
23                <groupId>org.apache.hadoop</groupId>
24                <artifactId>hadoop-client</artifactId>
25                <version>1.2.1</version>
26           </dependency>
27      </dependencies>

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

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

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

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

org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
05      at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
06      at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
07      at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026)
08      at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:794)
09      at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:737)
10      at

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

org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:740)
12      at
scala.collection.immutable.List.foreach(List.scala:318)
13      at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:740)
14      at
org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:569)
15      at
org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)
16      at
akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
17      at
akka.actor.ActorCell.invoke(ActorCell.scala:456)
18      at
akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
19      at
akka.dispatch.Mailbox.run(Mailbox.scala:219)
20      at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
21      at
scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
22      at
scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
23      at
scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
24      at
scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

在Spark集群上运行Java程序

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

01 <plugin>
02      <artifactId>maven-assembly-plugin</artifactId>
03      <configuration>
04           <archive>
05                <manifest>
06                     <mainClass>org.shirdrn.spark.job.UserAgentStats</mainClass>
07                </manifest>
08           </archive>
09           <descriptorRefs>
10                <descriptorRef>jar-with-dependencies</descriptorRef>
11           </descriptorRefs>
12           <excludes>
13                <exclude>*.properties</exclude>
14                <exclude>*.xml</exclude>
15           </excludes>
16      </configuration>
17      <executions>
18           <execution>
19                <id>make-assembly</id>
20                <phase>package</phase>
21                <goals>
22                     <goal>single</goal>
23                </goals>
24           </execution>
25      </executions>
26 </plugin>

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

01 cygwin=false
02 case "`uname`" in
03     CYGWIN*)
cygwin=true;;
04 esac
05  
06 SCALA_VERSION=2.10
07  
08 #
Figure out where the Scala framework is installed
09 FWDIR="$(cd
`dirname $0`/..; pwd)"
10  
11 #
Export this as SPARK_HOME
12 export SPARK_HOME="$FWDIR"
13  
14 #
Load environment variables from conf/spark-env.sh, if it exists
15 if [
-e "$FWDIR/conf/spark-env.sh" ]
; then
16   .
$FWDIR/conf/spark-env.sh
17 fi
18  
19 if [
-z "$1" ]; then
20   echo "Usage:
run-example <example-class> [<args>]" >&2
21   exit 1
22 fi
23  
24 #
Figure out the JAR file that our examples were packaged into. This includes a bit of a hack
25 #
to avoid the -sources and -doc packages that are built by publish-local.
26 EXAMPLES_DIR="$FWDIR"/java-examples
27 SPARK_EXAMPLES_JAR=""
28 if [
-e "$EXAMPLES_DIR"/*.jar
]; then
29   export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/*.jar`
30 fi
31 if [[
-z $SPARK_EXAMPLES_JAR ]]; then
32   echo "Failed
to find Spark examples assembly in $FWDIR/examples/target" >&2
33   echo "You
need to build Spark with sbt/sbt assembly before running this program" >&2
34   exit 1
35 fi
36  
37  
38 #
Since the examples JAR ideally shouldn't include spark-core (that dependency should be
39 #
"provided"), also add our standard Spark classpath, built using compute-classpath.sh.
40 CLASSPATH=`$FWDIR/bin/compute-classpath.sh`
41 CLASSPATH="$SPARK_EXAMPLES_JAR:$CLASSPATH"
42  
43 if $cygwin; then
44     CLASSPATH=`cygpath
-wp $CLASSPATH`
45     export SPARK_EXAMPLES_JAR=`cygpath
-w $SPARK_EXAMPLES_JAR`
46 fi
47  
48 #
Find java binary
49 if [
-n "${JAVA_HOME}" ]; then
50   RUNNER="${JAVA_HOME}/bin/java"
51 else
52   if [
`command -v java`
]; then
53     RUNNER="java"
54   else
55     echo "JAVA_HOME
is not set" >&2
56     exit 1
57   fi
58 fi
59  
60 #
Set JAVA_OPTS to be able to load native libraries and to set heap size
61 JAVA_OPTS="$SPARK_JAVA_OPTS"
62 JAVA_OPTS="$JAVA_OPTS
-Djava.library.path=$SPARK_LIBRARY_PATH"
63 #
Load extra JAVA_OPTS from conf/java-opts, if it exists
64 if [
-e "$FWDIR/conf/java-opts" ]
; then
65   JAVA_OPTS="$JAVA_OPTS
`cat $FWDIR/conf/java-opts`"
66 fi
67 export JAVA_OPTS
68  
69 if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then
70   echo -n "Spark
Command: "
71   echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
72   echo "========================================"
73   echo
74 fi
75  
76 exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"

在Spark上运行我们开发的Java程序，执行如下命令：

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

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

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

如果程序没有错误，能够正常运行，控制台输出程序运行日志，示例如下所示：

01 14/03/10

22:17:24 INFO job.IPAddressStats: GeoIP file:
/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat
02 SLF4J:
Class path contains multiple SLF4J bindings.
03 SLF4J:

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

Found binding in
[jar:file:/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/assembly/target/scala-2.10/spark-assembly_2.10-0.9.0-incubating-hadoop1.0.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]
05 SLF4J:
See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
06 SLF4J:
Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
07 14/03/10
22:17:25 INFO slf4j.Slf4jLogger: Slf4jLogger started
08 14/03/10
22:17:25 INFO Remoting: Starting remoting
09 14/03/10
22:17:25 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://spark@m1:57379]
10 14/03/10
22:17:25 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@m1:57379]
11 14/03/10
22:17:25 INFO spark.SparkEnv: Registering BlockManagerMaster
12 14/03/10
22:17:25 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-local-20140310221725-c1cb
13 14/03/10
22:17:25 INFO storage.MemoryStore: MemoryStore started with capacity 143.8 MB.
14 14/03/10
22:17:25 INFO network.ConnectionManager: Bound socket to port 45189 with id = ConnectionManagerId(m1,45189)
15 14/03/10
22:17:25 INFO storage.BlockManagerMaster: Trying to register BlockManager
16 14/03/10
22:17:25 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager m1:45189 with 143.8 MB RAM
17 14/03/10
22:17:25 INFO storage.BlockManagerMaster: Registered BlockManager
18 14/03/10
22:17:25 INFO spark.HttpServer: Starting HTTP Server
19 14/03/10
22:17:25 INFO server.Server: jetty-7.x.y-SNAPSHOT
20 14/03/10
22:17:25 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:49186
21 14/03/10
22:17:25 INFO broadcast.HttpBroadcast: Broadcast server started athttp://10.95.3.56:49186
22 14/03/10
22:17:25 INFO spark.SparkEnv: Registering MapOutputTracker
23 14/03/10
22:17:25 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-56c3e30d-a01b-4752-83d1-af1609ab2370
24 14/03/10
22:17:25 INFO spark.HttpServer: Starting HTTP Server
25 14/03/10
22:17:25 INFO server.Server: jetty-7.x.y-SNAPSHOT
26 14/03/10
22:17:25 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:52073
27 14/03/10
22:17:26 INFO server.Server: jetty-7.x.y-SNAPSHOT
28 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage/rdd,null}
29 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage,null}
30 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/stage,null}
31 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/pool,null}
32 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages,null}
33 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/environment,null}
34 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/executors,null}
35 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/metrics/json,null}
36 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/static,null}
37 14/03/10
22:17:26 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/,null}
38 14/03/10
22:17:26 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040
39 14/03/10
22:17:26 INFO ui.SparkUI: Started Spark Web UI at http://m1:4040
40 14/03/10

22:17:26 INFO spark.SparkContext: Added JAR
/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/spark-0.0.1-SNAPSHOT-jar-with-dependencies.jar
at http://10.95.3.56:52073/jars/spark-0.0.1-SNAPSHOT-jar-with-dependencies.jar with
timestamp 1394515046396
41 14/03/10
22:17:26 INFO client.AppClient$ClientActor: Connecting to masterspark://m1:7077...
42 14/03/10
22:17:26 INFO storage.MemoryStore: ensureFreeSpace(60341) called with curMem=0, maxMem=150837657
43 14/03/10
22:17:26 INFO storage.MemoryStore: Block broadcast_0 stored as values to memory (estimated size 58.9 KB, free 143.8 MB)
44 14/03/10
22:17:26 INFO cluster.SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20140310221726-0000
45 14/03/10

22:17:27 INFO client.AppClient$ClientActor: Executor added:
app-20140310221726-0000/0 on worker-20140310221648-s1-52544 (s1:52544)
with 1 cores
46 14/03/10

22:17:27 INFO cluster.SparkDeploySchedulerBackend: Granted executor ID
app-20140310221726-0000/0 on hostPort s1:52544 with 1 cores, 512.0 MB
RAM
47 14/03/10

22:17:27 WARN util.NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
48 14/03/10
22:17:27 WARN snappy.LoadSnappy: Snappy native library not loaded
49 14/03/10
22:17:27 INFO client.AppClient$ClientActor: Executor updated: app-20140310221726-0000/0 is now RUNNING
50 14/03/10
22:17:27 INFO mapred.FileInputFormat: Total input paths to process : 1
51 14/03/10
22:17:27 INFO spark.SparkContext: Starting job: collect at IPAddressStats.java:77
52 14/03/10
22:17:27 INFO scheduler.DAGScheduler: Registering RDD 4 (reduceByKey at IPAddressStats.java:70)
53 14/03/10
22:17:27 INFO scheduler.DAGScheduler: Got job 0 (collect at IPAddressStats.java:77) with 1 output partitions (allowLocal=false)
54 14/03/10
22:17:27 INFO scheduler.DAGScheduler: Final stage: Stage 0 (collect at IPAddressStats.java:77)
55 14/03/10
22:17:27 INFO scheduler.DAGScheduler: Parents of final stage: List(Stage 1)
56 14/03/10
22:17:27 INFO scheduler.DAGScheduler: Missing parents: List(Stage 1)
57 14/03/10

22:17:27 INFO scheduler.DAGScheduler: Submitting Stage 1
(MapPartitionsRDD[4] at reduceByKey at IPAddressStats.java:70), which
has no missing parents
58 14/03/10

22:17:27 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from
Stage 1 (MapPartitionsRDD[4] at reduceByKey at IPAddressStats.java:70)
59 14/03/10
22:17:27 INFO scheduler.TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
60 14/03/10
22:17:28 INFO cluster.SparkDeploySchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@s1:59233/user/Executor#-671170811] with ID 0
61 14/03/10
22:17:28 INFO scheduler.TaskSetManager: Starting task 1.0:0 as TID 0 on executor 0: s1 (PROCESS_LOCAL)
62 14/03/10
22:17:28 INFO scheduler.TaskSetManager: Serialized task 1.0:0 as 2396 bytes in 5 ms
63 14/03/10
22:17:29 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager s1:47282 with 297.0 MB RAM
64 14/03/10
22:17:32 INFO scheduler.TaskSetManager: Finished TID 0 in 3376 ms on s1 (progress: 0/1)
65 14/03/10
22:17:32 INFO scheduler.DAGScheduler: Completed ShuffleMapTask(1, 0)
66 14/03/10
22:17:32 INFO scheduler.DAGScheduler: Stage 1 (reduceByKey at IPAddressStats.java:70) finished in 4.420 s
67 14/03/10
22:17:32 INFO scheduler.DAGScheduler: looking for newly runnable stages
68 14/03/10
22:17:32 INFO scheduler.DAGScheduler: running: Set()
69 14/03/10
22:17:32 INFO scheduler.DAGScheduler: waiting: Set(Stage 0)
70 14/03/10
22:17:32 INFO scheduler.DAGScheduler: failed: Set()
71 14/03/10
22:17:32 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 1.0 from pool
72 14/03/10
22:17:32 INFO scheduler.DAGScheduler: Missing parents for Stage 0: List()
73 14/03/10

22:17:32 INFO scheduler.DAGScheduler: Submitting Stage 0
(MapPartitionsRDD[6] at reduceByKey at IPAddressStats.java:70), which is
now runnable
74 14/03/10

22:17:32 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from
Stage 0 (MapPartitionsRDD[6] at reduceByKey at IPAddressStats.java:70)
75 14/03/10
22:17:32 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
76 14/03/10
22:17:32 INFO scheduler.TaskSetManager: Starting task 0.0:0 as TID 1 on executor 0: s1 (PROCESS_LOCAL)
77 14/03/10
22:17:32 INFO scheduler.TaskSetManager: Serialized task 0.0:0 as 2255 bytes in 1 ms
78 14/03/10
22:17:32 INFO spark.MapOutputTrackerMasterActor: Asked to send map output locations for shuffle 0 to spark@s1:33534
79 14/03/10
22:17:32 INFO spark.MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 120 bytes
80 14/03/10
22:17:32 INFO scheduler.TaskSetManager: Finished TID 1 in 282 ms on s1 (progress: 0/1)
81 14/03/10
22:17:32 INFO scheduler.DAGScheduler: Completed ResultTask(0, 0)
82 14/03/10
22:17:32 INFO scheduler.DAGScheduler: Stage 0 (collect at IPAddressStats.java:77) finished in 0.314 s
83 14/03/10
22:17:32 INFO scheduler.TaskSchedulerImpl: Remove TaskSet 0.0 from pool
84 14/03/10
22:17:32 INFO spark.SparkContext: Job finished: collect at IPAddressStats.java:77, took 4.870958309 s
85 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 58.246.49.218     312
86 14/03/10
22:17:32 INFO job.IPAddressStats: [KR] 1.234.83.77     300
87 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 120.43.11.16     212
88 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 110.85.72.254     207
89 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 27.150.229.134     185
90 14/03/10
22:17:32 INFO job.IPAddressStats: [HK] 180.178.52.181     181
91 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 120.37.210.212     180
92 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 222.77.226.83     176
93 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 120.43.11.205     169
94 14/03/10
22:17:32 INFO job.IPAddressStats: [CN] 120.43.9.19     165
95 ...

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

参考链接

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