WordCount

作者: 一只特立独行的猪1991 | 来源:发表于2020-05-05 11:24 被阅读0次
    1. 新建Maven项目

      • 选择模板:scala-archetype-simple
    2. 配置pom文件

      <?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">
          <modelVersion>4.0.0</modelVersion>
          <groupId>org.example</groupId>
          <artifactId>learning</artifactId>
          <version>1.0-SNAPSHOT</version>
      
          <properties>
              <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
              <spark.version>2.2.0</spark.version>
              <scala.version>2.11.8</scala.version>
              <hadoop.version>2.6.5</hadoop.version>
              <hive.version>1.2.1</hive.version>
          </properties>
      
          <dependencies>
              <!--Spark相关的依赖-->
              <dependency>
                  <groupId>org.apache.spark</groupId>
                  <artifactId>spark-core_2.11</artifactId>
                  <version>${spark.version}</version>
              </dependency>
              <dependency>
                  <groupId>org.apache.spark</groupId>
                  <artifactId>spark-sql_2.11</artifactId>
                  <version>${spark.version}</version>
              </dependency>
              <dependency>
                  <groupId>org.apache.spark</groupId>
                  <artifactId>spark-hive_2.11</artifactId>
                  <version>${spark.version}</version>
              </dependency>
              <dependency>
                  <groupId>org.apache.spark</groupId>
                  <artifactId>spark-streaming_2.11</artifactId>
                  <version>${spark.version}</version>
              </dependency>
              <!--hadoop依赖-->
              <dependency>
                  <groupId>org.apache.hadoop</groupId>
                  <artifactId>hadoop-common</artifactId>
                  <version>${hadoop.version}</version>
              </dependency>
              <!--hive依赖包-->
              <dependency>
                  <groupId>org.apache.hive</groupId>
                  <artifactId>hive-exec</artifactId>
                  <version>${hive.version}</version>
              </dependency>
              <!--mysql驱动包-->
              <dependency>
                  <groupId>mysql</groupId>
                  <artifactId>mysql-connector-java</artifactId>
                  <version>5.1.38</version>
              </dependency>
          </dependencies>
      </project>
      
    3. 新建WordCount

      package src.main.scala.spark
      
      import org.apache.spark.{SparkConf, SparkContext}
      
      object WordCount {
        def main(args: Array[String]) {
          //创建一个Scala版本的Spark Context
          val conf = new SparkConf().setMaster("local[*]").setAppName("WordCount")
          val sc = new SparkContext(conf)
          //读取本地数据
          val lines = sc.textFile("D:\\workspace\\IdeaProjects\\learningscala\\src\\main\\resources\\word_count.txt")
          //把它切分成一个
          val words = lines.flatMap(line => line.split(" "))
          //转化为键值对并计数
          val wordCounts = words.map(word => (word, 1)).reduceByKey { case (x, y) => x + y }
          //打印统计结果
          wordCounts.collect().foreach(println)
          //关闭Spark
          sc.stop()
        }
      }
      
    4. 在resources目录下新建log4j.properties和word_count.txt

      log4j.properties文件

      #
      # Licensed to the Apache Software Foundation (ASF) under one or more
      # contributor license agreements.  See the NOTICE file distributed with
      # this work for additional information regarding copyright ownership.
      # The ASF licenses this file to You under the Apache License, Version 2.0
      # (the "License"); you may not use this file except in compliance with
      # the License.  You may obtain a copy of the License at
      #
      #    http://www.apache.org/licenses/LICENSE-2.0
      #
      # Unless required by applicable law or agreed to in writing, software
      # distributed under the License is distributed on an "AS IS" BASIS,
      # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
      # See the License for the specific language governing permissions and
      # limitations under the License.
      #
      
      # Set everything to be logged to the console
      log4j.rootCategory=WARN, 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 WARN. 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=WARN
      
      # Settings to quiet third party logs that are too verbose
      log4j.logger.org.spark_project.jetty=WARN
      log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
      log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
      log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
      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
      

      word_count.txt文件

      Give me the strength lightly to bear my joys and sorrows.
      Give me the strength to make my love fruitful in service.
      Give me the strength never to disown the poor or bend my knees before insolent might.
      Give me the strength to raise my mind high above daily trifles.
      And give me the strength to surrender my strength to thy will with love.
      
    5. 运行验证

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