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详解Java编写并运行spark应用程序的方法

发布时间：2020-05-23 11:00:29 所属栏目：Java 来源：互联网
导读：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：

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：

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;
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：

<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

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