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:
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121.205 . 198.92 - - [ 21 /Feb/ 2014 : 00 : 00 : 07 + 0800 ] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0" 121.205 . 198.92 - - [ 21 /Feb/ 2014 : 00 : 00 : 11 + 0800 ] "POST /wp-comments-post.php HTTP/1.1" 302 26 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:23.0) Gecko/20100101 Firefox/23.0" 121.205 . 198.92 - - [ 21 /Feb/ 2014 : 00 : 00 : 12 + 0800 ] "GET /archives/417.html/ HTTP/1.1" 301 26 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0" 121.205 . 198.92 - - [ 21 /Feb/ 2014 : 00 : 00 : 12 + 0800 ] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0" 121.205 . 241.229 - - [ 21 /Feb/ 2014 : 00 : 00 : 13 + 0800 ] "GET /archives/526.html HTTP/1.1" 200 12080 "http://shiyanjun.cn/archives/526.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0" 121.205 . 241.229 - - [ 21 /Feb/ 2014 : 00 : 00 : 15 + 0800 ] "POST /wp-comments-post.php HTTP/1.1" 302 26 "http://shiyanjun.cn/archives/526.html/" "Mozilla/5.0 (Windows NT 5.1; rv:23.0) Gecko/20100101 Firefox/23.0" |
Java实现Spark应用程序(Application)
我们实现的统计分析程序,有如下几个功能点:
从HDFS读取日志数据文件
将每行的第一个字段(IP地址)抽取出来
统计每个IP地址出现的次数
根据每个IP地址出现的次数进行一个降序排序
根据IP地址,调用GeoIP库获取IP所属国家
打印输出结果,每行的格式:[国家代码] IP地址 频率
下面,看我们使用Java实现的统计分析应用程序代码,如下所示:
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package org.shirdrn.spark.job; import java.io.File; import java.io.IOException; import java.util.Arrays; import java.util.Collections; import java.util.Comparator; import java.util.List; import java.util.regex.Pattern; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.FlatMapFunction; import org.apache.spark.api.java.function.Function2; import org.apache.spark.api.java.function.PairFunction; import org.shirdrn.spark.job.maxmind.Country; import org.shirdrn.spark.job.maxmind.LookupService; import scala.Serializable; import scala.Tuple2; public class IPAddressStats implements Serializable { private static final long serialVersionUID = 8533489548835413763L; private static final Log LOG = LogFactory.getLog(IPAddressStats. class ); private static final Pattern SPACE = Pattern.compile( " " ); private transient LookupService lookupService; private transient final String geoIPFile; public IPAddressStats(String geoIPFile) { this .geoIPFile = geoIPFile; try { // lookupService: get country code from a IP address File file = new File( this .geoIPFile); LOG.info( "GeoIP file: " + file.getAbsolutePath()); lookupService = new AdvancedLookupService(file, LookupService.GEOIP_MEMORY_CACHE); } catch (IOException e) { throw new RuntimeException(e); } } @SuppressWarnings ( "serial" ) public void stat(String[] args) { JavaSparkContext ctx = new JavaSparkContext(args[ 0 ], "IPAddressStats" , System.getenv( "SPARK_HOME" ), JavaSparkContext.jarOfClass(IPAddressStats. class )); JavaRDD<String> lines = ctx.textFile(args[ 1 ], 1 ); // splits and extracts ip address filed JavaRDD<String> words = lines.flatMap( new FlatMapFunction<String, String>() { @Override public Iterable<String> call(String s) { // 121.205.198.92 - - [21/Feb/2014:00:00:07 +0800] "GET /archives/417.html HTTP/1.1" 200 11465 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0" // ip address return Arrays.asList(SPACE.split(s)[ 0 ]); } }); // map JavaPairRDD<String, Integer> ones = words.map( new PairFunction<String, String, Integer>() { @Override public Tuple2<String, Integer> call(String s) { return new Tuple2<String, Integer>(s, 1 ); } }); // reduce JavaPairRDD<String, Integer> counts = ones.reduceByKey( new Function2<Integer, Integer, Integer>() { @Override public Integer call(Integer i1, Integer i2) { return i1 + i2; } }); List<Tuple2<String, Integer>> output = counts.collect(); // sort statistics result by value Collections.sort(output, new Comparator<Tuple2<String, Integer>>() { @Override public int compare(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) { if (t1._2 < t2._2) { return 1 ; } else if (t1._2 > t2._2) { return - 1 ; } return 0 ; } }); writeTo(args, output); } private void writeTo(String[] args, List<Tuple2<String, Integer>> output) { for (Tuple2<?, ?> tuple : output) { Country country = lookupService.getCountry((String) tuple._1); LOG.info( "[" + country.getCode() + "] " + tuple._1 + "\t" + tuple._2); } } public static void main(String[] args) { // ./bin/run-my-java-example org.shirdrn.spark.job.IPAddressStats spark://m1:7077 hdfs://m1:9000/user/shirdrn/wwwlog20140222.log /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat if (args.length < 3 ) { System.err.println( "Usage: IPAddressStats <master> <inFile> <GeoIPFile>" ); System.err.println( " Example: org.shirdrn.spark.job.IPAddressStats spark://m1:7077 hdfs://m1:9000/user/shirdrn/wwwlog20140222.log /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/java-examples/GeoIP_DATABASE.dat" ); System.exit( 1 ); } String geoIPFile = args[ 2 ]; IPAddressStats stats = new IPAddressStats(geoIPFile); stats.stat(args); System.exit( 0 ); } } |
具体实现逻辑,可以参考代码中的注释。我们使用Maven管理构建Java程序,首先看一下我的pom配置中所依赖的软件包,如下所示:
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<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:
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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:
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<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:
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cygwin= false case "`uname`" in CYGWIN*) cygwin= true ;; esac SCALA_VERSION=2.10 # Figure out where the Scala framework is installed FWDIR= "$(cd `dirname $0`/..; pwd)" # Export this as SPARK_HOME export SPARK_HOME= "$FWDIR" # Load environment variables from conf/spark-env.sh, if it exists if [ -e "$FWDIR/conf/spark-env.sh" ] ; then . $FWDIR/conf/spark-env.sh fi if [ -z "$1" ]; then echo "Usage: run-example <example-class> [<args>]" >&2 exit 1 fi # Figure out the JAR file that our examples were packaged into. This includes a bit of a hack # to avoid the -sources and -doc packages that are built by publish-local. EXAMPLES_DIR= "$FWDIR" /java-examples SPARK_EXAMPLES_JAR= "" if [ -e "$EXAMPLES_DIR" /*.jar ]; then export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR" /*.jar` fi if [[ -z $SPARK_EXAMPLES_JAR ]]; then echo "Failed to find Spark examples assembly in $FWDIR/examples/target" >&2 echo "You need to build Spark with sbt/sbt assembly before running this program" >&2 exit 1 fi # Since the examples JAR ideally shouldn't include spark-core (that dependency should be # "provided"), also add our standard Spark classpath, built using compute-classpath.sh. CLASSPATH=`$FWDIR/bin/compute-classpath.sh` CLASSPATH= "$SPARK_EXAMPLES_JAR:$CLASSPATH" if $cygwin; then CLASSPATH=`cygpath -wp $CLASSPATH` export SPARK_EXAMPLES_JAR=`cygpath -w $SPARK_EXAMPLES_JAR` fi # Find java binary if [ -n "${JAVA_HOME}" ]; then RUNNER= "${JAVA_HOME}/bin/java" else if [ `command -v java` ]; then RUNNER= "java" else echo "JAVA_HOME is not set" >&2 exit 1 fi fi # Set JAVA_OPTS to be able to load native libraries and to set heap size JAVA_OPTS= "$SPARK_JAVA_OPTS" JAVA_OPTS= "$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH" # Load extra JAVA_OPTS from conf/java-opts, if it exists if [ -e "$FWDIR/conf/java-opts" ] ; then JAVA_OPTS= "$JAVA_OPTS `cat $FWDIR/conf/java-opts`" fi export JAVA_OPTS if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then echo -n "Spark Command: " echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" echo "========================================" echo fi exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" |
在Spark上运行我们开发的Java程序,执行如下命令:
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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:
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:
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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。
总结
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