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本文转载自：http://shiyanjun.cn/archives/742.html
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：
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 79 // sort statistics result by value 80 Collections.sort(output, new Comparator<Tuple2<String, Integer>>() { 81 @Override 82 public int compare(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) { 83 if(t1._2 < t2._2) { 84 return 1; 85 } else if(t1._2 > t2._2) { 86 return -1; 87 } 88 return 0; 89 } 90 }); 91 92 writeTo(args, output); 93 94 } 95 96 private void writeTo(String[] args, List<Tuple2<String, Integer>> output) { 97 for (Tuple2<?, ?> tuple : output) { 98 Country country = lookupService.getCountry((String) tuple._1); 99 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.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 105 if (args.length < 3) { 106 System.err.println("Usage: IPAddressStats <master> <inFile> <GeoIPFile>"); 107 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"); 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> <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$WorkQueue.runTask(ForkJoinPool.java:1339) at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
在Spark集群上运行Java程序
这里，我使用了Maven管理构建Java程序，实现上述代码以后，使用Maven的maven-assembly-plugin插件，配置内容如下所示：
<plugin> <artifactId>maven-assembly-plugin</artifactId> <configuration> <archive> <manifest> <mainClass>org.shirdrn.spark.job.UserAgentStats</mainClass> </manifest> </archive> <descriptorRefs> <descriptorRef>jar-with-dependencies</descriptorRef> </descriptorRefs> <excludes> <exclude>*.properties</exclude> <exclude>*.xml</exclude> </excludes> </configuration> <executions> <execution> <id>make-assembly</id> <phase>package</phase> <goals> <goal>single</goal> </goals> </execution> </executions> </plugin>
将相关依赖库文件都打进程序包里面，最后拷贝JAR文件到Linux系统下（不一定非要在Spark集群的Master节点上），保证该节点上Spark的环境变量配置正确即可看。Spark软件发行包解压缩后，可以看到脚本bin/run-example，我们可以直接修改该脚本，将对应的路径指向我们实现的Java程序包（修改变量EXAMPLES_DIR以及我们的JAR文件存放位置相关的内容），使用该脚本就可以运行，脚本内容如下所示：
1 cygwin=false 2 case "`uname`" in 3 CYGWIN*) cygwin=true;; 4 esac 5 6 SCALA_VERSION=2.10 7 8 # Figure out where the Scala framework is installed 9 FWDIR="$(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 ./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：
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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原文地址：http://www.cnblogs.com/abapscript/p/4714739.html
