Spark- word Count案例

1. 新建项目

新建 idea Maven项目工程, 并创建子工程,pom.xml文件中引入spark依赖

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <parent>
        <artifactId>dintalk-classes</artifactId>
        <groupId>cn.dintalk.bigdata</groupId>
        <version>1.0.0</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>

    <artifactId>spark-core</artifactId>

    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.1.1</version>
        </dependency>
    </dependencies>

</project>

2.准备数据文件

 

 

3.代码编写

3.1第一种写法

package cn.dintalk.bigdata.spark.core.wc

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

object Spark01_WordCount {
  def main(args: Array[String]): Unit = {

    // application  -> spark 框架

    // 1. 建立 和 Spark框架的连接
    // JDBC 有 connection , Spark 有 SparkContext
    val sparkConf= new SparkConf()
      .setMaster("local")
      .setAppName("wordCount")

    val sc = new SparkContext(sparkConf)

    // 2. 执行业务操作

    // 2.1 读取文件, 获取一行一行的数据
    val lines: RDD[String] = sc.textFile("datas")
    // 2.2 将行数据进行切分,形成一个一个的单词
    val words: RDD[String] = lines.flatMap(_.split(" "))
    // 2.3 将数据按照单词进行分组,便于统计
    val wordGroup: RDD[(String, Iterable[String])] = words.groupBy(word => word)
    // 2.4 对分组后的数据进行转换
    // (hello,hello,hello), (word,word) -> (hello,3),(word,2)
    val wordCount: RDD[(String, Int)] = wordGroup.map {
      case (word, list) => {
        (word, list.size)
      }
    }
    // 2.5 将转换结果采集到控制台输出
    val tuples: Array[(String, Int)] = wordCount.collect()
    tuples.foreach(println)
    // 3. 关闭连接
    sc.stop()
  }
}

 

 3.2第二种写法

package cn.dintalk.bigdata.spark.core.wc

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

object Spark02_WordCount {
  def main(args: Array[String]): Unit = {

    // application  -> spark 框架

    // 1. 建立 和 Spark框架的连接
    // JDBC 有 connection , Spark 有 SparkContext
    val sparkConf= new SparkConf()
      .setMaster("local")
      .setAppName("wordCount")

    val sc = new SparkContext(sparkConf)

    // 2. 执行业务操作

    // 2.1 读取文件, 获取一行一行的数据
    val lines: RDD[String] = sc.textFile("datas")
    // 2.2 将行数据进行切分,形成一个一个的单词
    val words: RDD[String] = lines.flatMap(_.split(" "))

    val wordToOne: RDD[(String, Int)] = words.map(word => (word, 1))

    val wordGroup: RDD[(String, Iterable[(String, Int)])] = wordToOne
      .groupBy(t => t._1)

    val wordCount: RDD[(String, Int)] = wordGroup.map {
      case (word, list) => {
        list.reduce(
          (t1, t2) => {
            (t1._1, t1._2 + t2._2)
          }
        )
      }
    }
    // 2.5 将转换结果采集到控制台输出
    val tuples: Array[(String, Int)] = wordCount.collect()
    tuples.foreach(println)
    // 3. 关闭连接
    sc.stop()
  }
}

 

 3.3第三种写法

package cn.dintalk.bigdata.spark.core.wc

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

object Spark03_WordCount {
  def main(args: Array[String]): Unit = {

    val sparkConf= new SparkConf()
      .setMaster("local")
      .setAppName("wordCount")
    val sc = new SparkContext(sparkConf)

    val lines: RDD[String] = sc.textFile("datas")

    val words: RDD[String] = lines.flatMap(_.split(" "))
    val wordToOne: RDD[(String, Int)] = words.map((_, 1))
    val wordCount: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)

    val tuples: Array[(String, Int)] = wordCount.collect()
    tuples.foreach(println)

    sc.stop()
  }
}

 

3.4结果验证

4.log4j控制日志输出

4.1resources目录下新建log4j.properties并 配置

log4j.rootLogger=ERROR, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss,SSS}  %5p --- [%50t]  %-80c(line:%5L)  :  %m%n

log4j.appender.R=org.apache.log4j.RollingFileAppender
log4j.appender.R.File=../log/agent.log
log4j.appender.R.MaxFileSize=10240KB
log4j.appender.R.MaxBackupIndex=1

log4j.appender.R.layout=org.apache.log4j.PatternLayout
log4j.appender.R.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss,SSS}  %5p --- [%50t]  %-80c(line:%6L)  :  %m%n

4.2验证日志输出

无多余日志的输出