詳解Java編寫并運行spark應(yīng)用程序的方法

更新時間：2017年09月29日 10:05:30 作者：時延軍

這篇文章主要介紹了詳解Java編寫并運行spark應(yīng)用程序的方法，內(nèi)容詳細(xì)，結(jié)合了作者實際工作中的問題進行具體分析，具有一定參考價值。

我們首先提出這樣一個簡單的需求：

現(xiàn)在要分析某網(wǎng)站的訪問日志信息，統(tǒng)計來自不同IP的用戶訪問的次數(shù)，從而通過Geo信息來獲得來訪用戶所在國家地區(qū)分布狀況。這里我拿我網(wǎng)站的日志記錄行示例，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實現(xiàn)Spark應(yīng)用程序（Application）

我們實現(xiàn)的統(tǒng)計分析程序，有如下幾個功能點：

從HDFS讀取日志數(shù)據(jù)文件

將每行的第一個字段（IP地址）抽取出來

統(tǒng)計每個IP地址出現(xiàn)的次數(shù)

根據(jù)每個IP地址出現(xiàn)的次數(shù)進行一個降序排序

根據(jù)IP地址，調(diào)用GeoIP庫獲取IP所屬國家

打印輸出結(jié)果，每行的格式：[國家代碼] IP地址頻率

下面，看我們使用Java實現(xiàn)的統(tǒng)計分析應(yīng)用程序代碼，如下所示：

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);
  }
}

具體實現(xiàn)邏輯，可以參考代碼中的注釋。我們使用Maven管理構(gòu)建Java程序，首先看一下我的pom配置中所依賴的軟件包，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>

需要說明的是，當(dāng)我們將程序在Spark集群上運行時，它要求我們的編寫的Job能夠進行序列化，如果某些字段不需要序列化或者無法序列化，可以直接使用transient修飾即可，如上面的屬性lookupService沒有實現(xiàn)序列化接口，使用transient使其不執(zhí)行序列化，否則的話，可能會出現(xiàn)類似如下的錯誤：

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集群上運行Java程序

這里，我使用了Maven管理構(gòu)建Java程序，實現(xiàn)上述代碼以后，使用Maven的maven-assembly-plugin插件，配置內(nèi)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>

將相關(guān)依賴庫文件都打進程序包里面，最后拷貝JAR文件到Linux系統(tǒng)下（不一定非要在Spark集群的Master節(jié)點上），保證該節(jié)點上Spark的環(huán)境變量配置正確即可看。Spark軟件發(fā)行包解壓縮后，可以看到腳本bin/run-example，我們可以直接修改該腳本，將對應(yīng)的路徑指向我們實現(xiàn)的Java程序包（修改變量EXAMPLES_DIR以及我們的JAR文件存放位置相關(guān)的內(nèi)容），使用該腳本就可以運行，腳本內(nèi)容如下所示：

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上運行我們開發(fā)的Java程序，執(zhí)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

我實現(xiàn)的程序類org.shirdrn.spark.job.IPAddressStats運行需要3個參數(shù)：

Spark集群主節(jié)點URL：例如我的是spark://m1:7077

輸入文件路徑：業(yè)務(wù)相關(guān)的，我這里是從HDFS上讀取文件hdfs://m1:9000/user/shirdrn/wwwlog20140222.log

GeoIP庫文件：業(yè)務(wù)相關(guān)的，用來計算IP地址所屬國家的外部文件

如果程序沒有錯誤，能夠正常運行，控制臺輸出程序運行日志，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
...

我們也可以通過Web控制臺來查看當(dāng)前執(zhí)行應(yīng)用程序（Application）的狀態(tài)信息，通過Master節(jié)點的8080端口（如：http://m1:8080/）就能看到集群的應(yīng)用程序（Application）狀態(tài)信息。

另外，需要說明的時候，如果在Unix環(huán)境下使用Eclipse使用Java開發(fā)Spark應(yīng)用程序，也能夠直接通過Eclipse連接Spark集群，并提交開發(fā)的應(yīng)用程序，然后交給集群去處理。

總結(jié)

以上就是本文關(guān)于詳解Java編寫并運行spark應(yīng)用程序的方法的全部內(nèi)容，希望對大家有所幫助。有什么問題可以隨時留言，小編會及時回復(fù)大家。

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