Spark 版本 WordCount

一、环境准备

今天我们看下Spark 版本的 wordcount，IDEA 新建 Maven 项目引入依赖:使用 spark 3.0.0，对应scala 版本2.12

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>

在 resources 目录下创建 log4j.properties 配置文件，设置日志级别

log4j.rootCategory=ERROR, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to ERROR. When running the spark-shell, the
# log level for this class is used to overwrite the root logger's log level, so that
# the user can have different defaults for the shell and regular Spark apps.
log4j.logger.org.apache.spark.repl.Main=ERROR

# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark_project.jetty=ERROR
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=ERROR
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=ERROR
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=ERROR

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

二、编程

版本1

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

object Spark01_WordCount {
  def main(args: Array[String]): Unit = {
    // 获取链接
    val sparkConf = new SparkConf().setMaster("local").setAppName("WordCount")
    val sc = new SparkContext(sparkConf)
    //执行业务操作
    //1、按行读取文件 hello world
    val lines: RDD[String] = sc.textFile("data")
    //2、按分隔分词
    val words: RDD[String] = lines.flatMap(_.split(" "))
    //3、聚合
    val wordGroup: RDD[(String, Iterable[String])] = words.groupBy(word => word)
    val wordToCount: RDD[(String, Int)] = wordGroup.map {
      //模式匹配
      case (word, list) => {
        (word, list.size)
      }
    }
    val arrays : Array[(String, Int)] = wordToCount.collect()
    arrays.foreach(println)
    //关闭链接
    sc.stop()
  }
}

版本二

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

object Spark02_WordCount {
  def main(args: Array[String]): Unit = {
    // 获取链接
    val sparkConf = new SparkConf().setMaster("local").setAppName("WordCount")
    val sc = new SparkContext(sparkConf)
    //执行业务操作
    //1、按行读取文件 hello world
    val lines: RDD[String] = sc.textFile("data")
    //2、分词
    val words: RDD[String] = lines.flatMap(_.split(" "))

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

    //3、聚合
    val groupRDD: RDD[(String, Iterable[(String, Int)])] = wordToOne.groupBy(t => t._1)
    val wordToCount: RDD[(String, Int)] = groupRDD.map {
      case (word, list) => {
        list.reduce(
          (t1, t2) => {
            (t1._1, t2._2 + t2._2)
          }
        )
      }
    }

    val arrays: Array[(String, Int)] = wordToCount.collect()
    arrays.foreach(println)
    //关闭链接
    sc.stop()
  }
}

版本三

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

object Spark03_WordCount {
  def main(args: Array[String]): Unit = {
    // 获取链接
    val sparkConf = new SparkConf().setMaster("local").setAppName("WordCount")
    val sc = new SparkContext(sparkConf)
    //执行业务操作
    //1、按行读取文件 hello world
    val lines: RDD[String] = sc.textFile("data")
    //2、分词
    val words: RDD[String] = lines.flatMap(_.split(" "))

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

    //3、聚合reduceByKey 相同的 key 对 V 进行聚合
    val wordToCount: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)

    val arrays: Array[(String, Int)] = wordToCount.collect()
    arrays.foreach(println)
    //关闭链接
    sc.stop()
  }
}

运行结果

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org.wdh01.bigdata.spark.core.wc.Spark03_WordCount
(hive,2)
(flink,2)
(hello,6)
(spark,2)

Process finished with exit code 0