spark 运行jvm 参数 java调用spark

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编程之翼 2023-07-09 19:07:34

文章标签 spark 运行jvm 参数 java spark hive 文章分类 Spark 大数据

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"
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：

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：

<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：

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：

<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：

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：

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：

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：

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
...

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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