Spark笔记整理（三）：Spark WC开发与应用部署

Spark WordCount开发

创建的是maven工程，使用的依赖如下：

<dependency>
    <groupId>org.scala-lang</groupId>
    <artifactId>scala-library</artifactId>
    <version>2.10.5</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.10</artifactId>
    <version>1.6.2</version>
</dependency>

spark wc之Java版本

package cn.xpleaf.bigdata.spark.java.core.p1;

import org.apache.spark.SparkConf;
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.apache.spark.api.java.function.VoidFunction;
import scala.Tuple2;

import java.util.Arrays;

/**
 * spark Core 开发
 *
 * 基于Java
 * 计算国际惯例
 *
 * Spark程序的入口：
 *      SparkContext
 *          Java：JavaSparkContext
 *          scala：SparkContext
 *
 * D:/datasparkhello.txt
 *
 * spark RDD的操作分为两种，第一为Transformation，第二为Action
 * 我们将Transformation称作转换算子，Action称作Action算子
 * Transformation算子常见的有：map flatMap reduceByKey groupByKey filter...
 * Action常见的有：foreach collect count save等等
 *
 * Transformation算子是懒加载的，其执行需要Action算子的触发
 * （可以参考下面的代码，只要foreach不执行，即使中间RDD的操作函数有异常也不会报错，因为其只是加载到内存中，并没有真正执行）
 */
public class _01SparkWordCountOps {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf();
        conf.setAppName(_01SparkWordCountOps.class.getSimpleName());
        /**
         * sparkConf中设置的master选择，
         * local
         *      local
         *          spark作业在本地执行，为该spark作业分配一个工作线程
         *      local[N]
         *          spark作业在本地执行，为该spark作业分配N个工作线程
         *      local[*]
         *          spark作业在本地执行，根据机器的硬件资源，为spark分配适合的工作线程，一般也就2个
         *      local[N, M]
         *          local[N, M]和上面最大的区别就是，当spark作业启动或者提交失败之后，可以有M次重试的机会，上面几种没有
         * standalone模式：
         *      就是spark集群中master的地址，spark://uplooking01:7077
         * yarn
         *      yarn-cluster
         *          基于yarn的集群模式，sparkContext的构建和作业的运行都在yarn集群中执行
         *      yarn-client
         *          基于yarn的client模式，sparkContext的构建在本地，作业的运行在集群
         *
         * mesos
         *      mesos-cluster
         *      mesos-client
         */
        String master = "local[*]";
        conf.setMaster(master);
        JavaSparkContext jsc = new JavaSparkContext(conf);
        Integer defaultParallelism = jsc.defaultParallelism();
        System.out.println("defaultParallelism=" + defaultParallelism);
        /**
         * 下面的操作代码，其实就是spark中RDD的DAG图
         */
        JavaRDD<String> linesRDD = jsc.textFile("D:/data/spark/hello.txt");
        System.out.println("linesRDD's partition size is: " + linesRDD.partitions().size());
        JavaRDD<String> wordsRDD = linesRDD.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public Iterable<String> call(String line) throws Exception {
                // int i = 1 / 0;  // 用以验证Transformation算子的懒加载
                return Arrays.asList(line.split(" "));
            }
        });
        JavaPairRDD<String, Integer> pairRDD = wordsRDD.mapToPair(new PairFunction<String, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(String word) throws Exception {
                return new Tuple2<String, Integer>(word, 1);
            }
        });
        JavaPairRDD<String, Integer> retRDD = pairRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer v1, Integer v2) throws Exception {
                return v1 + v2;
            }
        });
        System.out.println("retRDD's partition size is: " + retRDD.partitions().size());
        retRDD.foreach(new VoidFunction<Tuple2<String, Integer>>() {
            @Override
            public void call(Tuple2<String, Integer> tuple) throws Exception {
                System.out.println(tuple._1 + "---" + tuple._2);
            }
        });
        jsc.close();
    }
}

本地执行，输出结果如下：

defaultParallelism=20
......
linesRDD's partition size is: 2
retRDD's partition size is: 2
......
hello---3
you---1
me---1
he---1

spark wc之Java lambda版本

package cn.xpleaf.bigdata.spark.java.core.p1;

import org.apache.spark.SparkConf;
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.apache.spark.api.java.function.VoidFunction;
import scala.Tuple2;

import java.util.Arrays;

/**
 * spark Core 开发
 *
 * 基于Java
 * 计算国际惯例
 *
 * Spark程序的入口：
 *      SparkContext
 *          Java：JavaSparkContext
 *          scala：SparkContext
 *
 * D:/datasparkhello.txt
 *
 * lambda表达式的版本
 */
public class _02SparkWordCountOps {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf();
        conf.setAppName(_02SparkWordCountOps.class.getSimpleName());
        String master = "local";
        conf.setMaster(master);
        JavaSparkContext jsc = new JavaSparkContext(conf);
        /**
         * 下面的操作代码，其实就是spark中RDD的DAG图
         * 现在使用lambda表达式，更加简单清晰
         */
        JavaRDD<String> linesRDD = jsc.textFile("D:/data/spark/hello.txt");
        JavaRDD<String> wordsRDD = linesRDD.flatMap(line -> {return Arrays.asList(line.split(" "));});
        JavaPairRDD<String, Integer> pairRDD = wordsRDD.mapToPair(word -> {return new Tuple2<String, Integer>(word, 1);});
        JavaPairRDD<String, Integer> retRDD = pairRDD.reduceByKey((v1, v2) -> {return v1 + v2;});
        retRDD.foreach(tuple -> {
            System.out.println(tuple._1 + "---" + tuple._2);
        });
        jsc.close();
    }
}

本地执行，输出结果如下：

you---1
he---1
hello---3
me---1

spark wc之scala版本

package cn.xpleaf.bigdata.spark.scala.core.p1

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * 基于Scala的WordCount统计
  *
  * java.net.UnknownHostException: ns1
  *
  * spark系统不认识ns1
  * 在spark的配置文件spark-defaults.conf中添加：
  *     spark.files /home/uplooking/app/hadoop/etc/hadoop/hdfs-site.xml,/home/uplooking/app/hadoop/etc/hadoop/core-site.xml
  */
object _01SparkWordCountOps {
    def main(args: Array[String]): Unit = {
        val conf = new SparkConf()
            .setAppName(s"${_01SparkWordCountOps.getClass().getSimpleName}")
            .setMaster("local")
        val sc = new SparkContext(conf)

        val linesRDD:RDD[String] = sc.textFile("D:/data/spark/hello.txt")
        /*val wordsRDD:RDD[String] = linesRDD.flatMap(line => line.split(" "))
        val parsRDD:RDD[(String, Int)] = wordsRDD.map(word => new Tuple2[String, Int](word, 1))
        val retRDD:RDD[(String, Int)] = parsRDD.reduceByKey((v1, v2) => v1 + v2)
        retRDD.collect().foreach(t => println(t._1 + "..." + t._2))*/

        // 更简洁的方式
        linesRDD.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _).collect().foreach(t => println(t._1 + "..." + t._2))
        sc.stop()
    }
}

本地执行，输出结果如下：

you...1
he...1
hello...3
me...1

应用部署

部署说明

上面的方式其实都是本地执行的，可以把我们的应用部署到Spark集群或Yarn集群上，前面的代码注释也有提到这一点，就是关于Spark作业执行的问题：

/**
 * sparkConf中设置的master选择，
 * local
 *      local
 *          spark作业在本地执行，为该spark作业分配一个工作线程
 *      local[N]
 *          spark作业在本地执行，为该spark作业分配N个工作线程
 *      local[*]
 *          spark作业在本地执行，根据机器的硬件资源，为spark分配适合的工作线程，一般也就2个
 *      local[N, M]
 *          local[N, M]和上面最大的区别就是，当spark作业启动或者提交失败之后，可以有M次重试的机会，上面几种没有
 * standalone模式：
 *      就是spark集群中master的地址，spark://uplooking01:7077
 * yarn
 *      yarn-cluster
 *          基于yarn的集群模式，sparkContext的构建和作业的运行都在yarn集群中执行
 *      yarn-client
 *          基于yarn的client模式，sparkContext的构建在本地，作业的运行在集群
 *
 * mesos
 *      mesos-cluster
 *      mesos-client
 */

local的多种情况可以自己测试一下。

这里只测试部署standalone和yarn-cluster两种模式，实际上yarn-client也测试了，不过报异常，没去折腾。注意用的是Scala的代码。

其实很显然，这里使用的是Spark离线计算的功能（Spark Core）。

程序打包

将前面的scala版本的代码修改为如下：

package cn.xpleaf.bigdata.spark.scala.core.p1

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * 基于Scala的WordCount统计
  *
  * java.net.UnknownHostException: ns1
  *
  * spark系统不认识ns1
  * 在spark的配置文件spark-defaults.conf中添加：
  *     spark.files /home/uplooking/app/hadoop/etc/hadoop/hdfs-site.xml,/home/uplooking/app/hadoop/etc/hadoop/core-site.xml
  */
object _01SparkWordCountOps {
    def main(args: Array[String]): Unit = {
        val conf = new SparkConf()
            .setAppName(s"${_01SparkWordCountOps.getClass().getSimpleName}")
            //.setMaster("local")
        val sc = new SparkContext(conf)

        val linesRDD:RDD[String] = sc.textFile("hdfs://ns1/hello")
        /*val wordsRDD:RDD[String] = linesRDD.flatMap(line => line.split(" "))
        val parsRDD:RDD[(String, Int)] = wordsRDD.map(word => new Tuple2[String, Int](word, 1))
        val retRDD:RDD[(String, Int)] = parsRDD.reduceByKey((v1, v2) => v1 + v2)
        retRDD.collect().foreach(t => println(t._1 + "..." + t._2))*/

        // 更简洁的方式
        linesRDD.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _).collect().foreach(t => println(t._1 + "..." + t._2))
        // collect不是必须要加的，但是如果在standalone的运行模式下，不加就看不到控制台的输出
        // 而在yarn运行模式下，是看不到输出的
        sc.stop()
    }
}

主要是做了两处的修改，一是注释掉setMaster("local")，因为现在不是本地跑了，另外是数据来源，选择的是HDFS上的数据文件。

需要注意的是，要想让Spark集群认识ns1（我的Hadoop集群是HA部署方式），其实有两种方式，一种设置环境变量HADOOP_CONF_DIR，但我测试的时候不生效，依然是无法识别ns1；另外一种是需要在Spark的配置文件spark-defaults.conf中添加spark.files /home/uplooking/app/hadoop/etc/hadoop/hdfs-site.xml,/home/uplooking/app/hadoop/etc/hadoop/core-site.xml，即指定Hadoop的配置文件地址，Hadoop HA的配置，就是在这两个文件中进行的配置。我采用第二种方式有效。

上面准备工作完成后就可以将程序打包了，使用普通的打包或者maven打包都可以，注意不需要将依赖一起打包，因为我们的Spark集群环境中已经存在这些依赖了。

部署到Spark集群上

关于应用的部署，准确来说是submit，官方文档有很详细的说明，可以参考：http://spark.apache.org/docs/latest/submitting-applications.html

先编写下面一个脚本：

[uplooking@uplooking01 spark]$ cat spark-submit-standalone.sh 
#export HADOOP_CONF_DIR=/home/uplooking/app/hadoop/etc/hadoop

/home/uplooking/app/spark/bin/spark-submit 
--class $2 
--master spark://uplooking01:7077 
--executor-memory 1G 
--num-executors 1 
$1 

然后执行下面的命令：

[uplooking@uplooking01 spark]$ ./spark-submit-standalone.sh spark-wc.jar cn.xpleaf.bigdata.spark.scala.core.p1._01SparkWordCountOps

因为在程序代码中已经添加了collect Action算子，所以运行成功后可以直接在控制台中看到输出结果：

hello...3
me...1
you...1
he...1

然后也可以在spark提供的UI界面中看到其提交的作业以及执行结果：

部署到Yarn集群上

先编写下面一个脚本：

[uplooking@uplooking01 spark]$ cat spark-submit-yarn.sh 
#export HADOOP_CONF_DIR=/home/uplooking/app/hadoop/etc/hadoop

/home/uplooking/app/spark/bin/spark-submit 
--class $2 
--master yarn 
--deploy-mode cluster 
--executor-memory 1G 
--num-executors 1 
$1 

执行如下命令：

[uplooking@uplooking01 spark]$ ./spark-submit-yarn.sh spark-wc.jar cn.xpleaf.bigdata.spark.scala.core.p1._01SparkWordCountOps      
18/04/25 17:47:39 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/04/25 17:47:39 INFO yarn.Client: Requesting a new application from cluster with 2 NodeManagers
18/04/25 17:47:39 INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
18/04/25 17:47:39 INFO yarn.Client: Will allocate AM container, with 1408 MB memory including 384 MB overhead
18/04/25 17:47:39 INFO yarn.Client: Setting up container launch context for our AM
18/04/25 17:47:39 INFO yarn.Client: Setting up the launch environment for our AM container
18/04/25 17:47:39 INFO yarn.Client: Preparing resources for our AM container
18/04/25 17:47:40 INFO yarn.Client: Uploading resource file:/home/uplooking/app/spark/lib/spark-assembly-1.6.2-hadoop2.6.0.jar -> hdfs://ns1/user/uplooking/.sparkStaging/application_1524552224611_0005/spark-assembly-1.6.2-hadoop2.6.0.jar
18/04/25 17:47:42 INFO yarn.Client: Uploading resource file:/home/uplooking/jars/spark/spark-wc.jar -> hdfs://ns1/user/uplooking/.sparkStaging/application_1524552224611_0005/spark-wc.jar
18/04/25 17:47:42 INFO yarn.Client: Uploading resource file:/tmp/spark-ae34fa23-5166-4fd3-a4ec-8e5115691801/__spark_conf__6834084285342234312.zip -> hdfs://ns1/user/uplooking/.sparkStaging/application_1524552224611_0005/__spark_conf__6834084285342234312.zip
18/04/25 17:47:43 INFO spark.SecurityManager: Changing view acls to: uplooking
18/04/25 17:47:43 INFO spark.SecurityManager: Changing modify acls to: uplooking
18/04/25 17:47:43 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(uplooking); users with modify permissions: Set(uplooking)
18/04/25 17:47:43 INFO yarn.Client: Submitting application 5 to ResourceManager
18/04/25 17:47:43 INFO impl.YarnClientImpl: Submitted application application_1524552224611_0005
18/04/25 17:47:44 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:44 INFO yarn.Client: 
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: N/A
         ApplicationMaster RPC port: -1
         queue: default
         start time: 1524649663869
         final status: UNDEFINED
         tracking URL: http://uplooking02:8088/proxy/application_1524552224611_0005/
         user: uplooking
18/04/25 17:47:45 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:46 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:47 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:48 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:49 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:50 INFO yarn.Client: Application report for application_1524552224611_0005 (state: ACCEPTED)
18/04/25 17:47:51 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:51 INFO yarn.Client: 
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: 192.168.43.103
         ApplicationMaster RPC port: 0
         queue: default
         start time: 1524649663869
         final status: UNDEFINED
         tracking URL: http://uplooking02:8088/proxy/application_1524552224611_0005/
         user: uplooking
18/04/25 17:47:52 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:53 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:54 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:55 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:56 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:57 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:58 INFO yarn.Client: Application report for application_1524552224611_0005 (state: RUNNING)
18/04/25 17:47:59 INFO yarn.Client: Application report for application_1524552224611_0005 (state: FINISHED)
18/04/25 17:47:59 INFO yarn.Client: 
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: 192.168.43.103
         ApplicationMaster RPC port: 0
         queue: default
         start time: 1524649663869
         final status: SUCCEEDED
         tracking URL: http://uplooking02:8088/proxy/application_1524552224611_0005/
         user: uplooking
18/04/25 17:47:59 INFO util.ShutdownHookManager: Shutdown hook called
18/04/25 17:47:59 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-ae34fa23-5166-4fd3-a4ec-8e5115691801

可以通过yarn提供的Web界面来查看其提交的作业情况：

但是找了日志也没有找到输出的统计结果，所以这种情况下，数据结果的落地就不应该只是输出而已了，可以考虑其它的持久化存储。

总体而言，对比MapReduce，仅仅从Spark Core来看，速度真的是有非常大的提高。

关于wc执行过程的说明

参考下面的图示：

原文链接：http://blog.51cto.com/xpleaf/2107805