Spark单词统计笔记

1.sc
SparkContext,Spark程序的入口点，封装了整个spark运行环境的信息。
2.进入spark-shell

$>spark-shell
$scala>sc

API:
SparkContext
RDD:
resilient distributed dataset,弹性分布式数据集。等价于集合。
spark实现Wordcount

//加载文本文件，以换行符方式切割文本。Array(hello world2,hello world2,...)
val  rdd1 = sc.textFile("/home/ubuntu/test.txt");
val rdd2 = rdd1.flatMap(line=>line.split(" "));
val rdd3 = rdd2.map(word=>(word,1));
val rdd4 = rdd3.reduceByKey(_+_);
rdd4.collect
一行代码：
scala> sc.textFile("/home/ubuntu/test.txt").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect
结果：
res2: Array[(String, Int)] = Array((world2,2), (world4,1), (hello,4), (world3,1))
过滤包含“wor”的单词
scala> sc.textFile("/home/ubuntu/test.txt").flatMap(_.split(" ")).filter(_.contains("wor")).map((_,1)).reduceByKey(_+_).collect
res3: Array[(String, Int)] = Array((world2,2), (world4,1), (world3,1))

windows下：
idea编写Scala程序，引入spark类库，完成wordcount
1.添加Scala框架支持，没有则安装Scala插件（2.11.8），spark最新版本2.3.2（scala2.11.8）
2.maven添加spark依赖

 <dependency>
    <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.11</artifactId>
    <version>2.3.2</version>
 </dependency>

Scala版本

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

/**
  * scala版本
  */
object WordCountScala {
  def main(args: Array[String]): Unit = {
    //创建spark配置对象
    val conf = new SparkConf();
    //conf.setAppName("WordCountScala");
    //设置master属性
    //conf.setMaster("local");
    //通过conf创建sc
    val sc = new SparkContext(conf);

    //加载文本文件
//    val rdd1 = sc.textFile("d:/scala/test.txt");
    val rdd1 = sc.textFile(args(0));
    //压扁
    val rdd2 = rdd1.flatMap(line => line.split("\\s+"));
    //映射w=>(w,1)
    val rdd3 = rdd2.map((_, 1))
    val rdd4 = rdd3.reduceByKey(_ + _)
    val r = rdd4.collect()
    r.foreach(println)

  }
}

java版本

package com.it18zhang.spark.java;

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 scala.Tuple2;

import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;

/**
 * java版本
 */
public class WordCountJava2 {
    public static void main(String[] args) {
        //创建SparkConf对象
        SparkConf conf = new SparkConf();
        //conf.setAppName("WordCountJava2");
        //conf.setMaster("local");

        //上下文
        JavaSparkContext sc = new JavaSparkContext(conf);
        //加载文本文件
//        JavaRDD<String> rdd1 = sc.textFile("d:/scala/test.txt");
        JavaRDD<String> rdd1 = sc.textFile(args[0]);
        //接口回调机制产生匿名内部类对象
        JavaRDD<String> rdd2 = rdd1.flatMap(new FlatMapFunction<String, String>() {
            public Iterator<String> call(String s) throws Exception {
                List<String> list = new ArrayList<String>();
                String[] arr = s.split("\\s+");
                for (String ss:arr){
                    list.add(ss);
                }
                return list.iterator();
            }
        });
        //映射，word=>(word,1)
        JavaPairRDD<String, Integer> rdd3 = rdd2.mapToPair(new PairFunction<String, String, Integer>() {
            public Tuple2<String, Integer> call(String s) throws Exception {
                return new Tuple2<String, Integer>(s, 1);
            }
        });

        //reduce化简
        JavaPairRDD<String, Integer> rdd4 = rdd3.reduceByKey(new Function2<Integer, Integer, Integer>() {
            public Integer call(Integer v1, Integer v2) throws Exception {
                return v1 + v2;
            }
        });

        List<Tuple2<String, Integer>> list = rdd4.collect();
        for (Tuple2 t:list){
            System.out.println(t._1() + ":" + t._2());
        }
    }
}

打包成 SparkDemo1-1.0-SNAPSHOT.jar

spark-submit --master local --class com.it18zhang.spark.scala.WordCountScala --name MyWordCount SparkDemo1-1.0-SNAPSHOT.jar /home/ubuntu/test.txt 

Spark集群模式

1.local
  nothing!
  spark-shell --master local;  //默认
2.standalone
  独立模式
a.复制spark目录到其他主机
b.配置其他主机的所有环境变量
  [/etc/profile]
  SPARK_HOME
  PATH
c.配置master节点的slaves
  s1
  s2
  s3
d.启动spark集群
/soft/spark/sbin/start-all.sh
e.webui
  http://s0:8080/

提交作业到完全分布式spark集群

1.需要启动hadoop集群（只需要hdfs）
  start-hdfs.sh
2.put文件到hdfs
  hdfs dfs -put test.txt /user/ubuntu
3.运行spark-submit
spark-submit --master spark://s0:7077 --class com.it18zhang.spark.scala.WordCountScala --name MyWordCount SparkDemo1-1.0-SNAPSHOT.jar hdfs://s0:8020/user/ubuntu/test.txt 

ubuntu@s0:~$ xcall.sh jps
============ s0 jps =============
3207 NameNode
4504 Jps
3432 SecondaryNameNode
3976 Master
============ s1 jps =============
3522 Worker
3845 Jps
3276 DataNode
============ s2 jps =============
3827 Jps
3276 DataNode
3517 Worker
============ s3 jps =============
3197 DataNode
3758 Jps
3439 Worker

脚本分析
[start-all.sh]
  sbin/spark-config.sh
  sbin/spark-master.sh  //启动master进程
  sbin/spark-slaves.sh  //启动worker进程

webui