idea下Spring Boot 集成 scala实现 wordCount

idea 下使用 Scalc 实现 wordCount

idea -> 进入 plugins 安装 Scala 插件需重新启动 idea
进入 project structure -> 下载 Scala SDK （速度很慢很慢，耐心等待，下载不全会导致后面编译运行程序出bug）如下图点击加号下载即可，我的 Scala 版本是2.12.16、spark版本是3.0.1，版本依赖可以参考下图，更多信息点击链接查看其实就是maven仓库
安装完这个之后就可以创建 springboot 项目，创建好之后，依旧是打开 project structure 选择你刚才下载好的 Scala 即可

pom依赖可以参考我的

<?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>sparkTest</artifactId>
    <version>1.0-SNAPSHOT</version>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <version>2.2.7.RELEASE</version>
        <artifactId>spring-boot-starter-parent</artifactId>
    </parent>
    <properties>
        <scala.version>2.12</scala.version>
        <spark.version>3.0.1</spark.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
            <version>2.2.7.RELEASE</version>
            <exclusions>
                <exclusion>
                    <groupId>org.springframework.boot</groupId>
                    <artifactId>spring-boot-starter-logging</artifactId>
                </exclusion>
                <exclusion>
                    <groupId>com.google.guava</groupId>
                    <artifactId>guava</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <optional>true</optional>
        </dependency>

        <!--        https://mvnrepository.com/artifact/org.apache.spark/spark-sql_2.10-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--        因为我们需要使用Scala代码，所以我们还需要加入scala-library依赖-->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.12.9</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/com.databricks/spark-xml -->
        <dependency>
            <groupId>com.databricks</groupId>
            <artifactId>spark-xml_2.11</artifactId>
            <version>0.11.0</version>
        </dependency>

    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-jar-plugin</artifactId>
                <version>2.6</version>
                <configuration>
                    <archive>
                        <manifest>
                            <addClasspath>true</addClasspath>
                            <classpathPrefix>lib/</classpathPrefix>
                            <mainClass>lambert.fun.bean.Main</mainClass>
                        </manifest>
                    </archive>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-dependency-plugin</artifactId>
                <version>2.10</version>
                <executions>
                    <execution>
                        <id>copy-dependencies</id>
                        <phase>package</phase>
                        <goals>
                            <goal>copy-dependencies</goal>
                        </goals>
                        <configuration>
                            <outputDirectory>${project.build.directory}/lib</outputDirectory>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <id>scala-compile-first</id>
                        <goals>
                            <goal>compile</goal>
                        </goals>
                        <configuration>
                            <includes>
                                <include>**/*.scala</include>
                            </includes>
                        </configuration>
                    </execution>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                        <configuration>
                            <jvmArgs>
                                <!-- 需要的配置 -->
                                <jvmArg>-Xss8m</jvmArg>
                            </jvmArgs>
                        </configuration>
                    </execution>
                    <execution>
                        <id>scala-test-compile</id>
                        <goals>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <excludes>
                        <exclude>
                            <groupId>org.projectlombok</groupId>
                            <artifactId>lombok</artifactId>
                        </exclude>
                    </excludes>
                    <scalaVersion>2.12.11</scalaVersion>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

创建 Scalc opject 类，单词统计代码如下

package lambert.fun.spark

import org.apache.log4j.{Level, Logger}
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession

/** *
 *
 * @author Lambert
 *         date 2022-08-17
 *         explain 
 */
object Test {
  def main(args: Array[String]): Unit = {
    // 过滤无效日志
    Logger.getLogger("org").setLevel(Level.ERROR)
//    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)

    //    val spark = SparkSession
    //      .builder
    //      .appName("calc")
    //      .config("spark.master", "spark://192.168.1.247:7077")
    //      .getOrCreate()

    //    spark.sparkContext.textFile("datas")
    //    spark.sparkContext.setLogLevel("ERROR")

    // 建立spark 与框架的连接
    val sparkConf = new SparkConf().setMaster("local").setAppName("WordCount")
    val sc = new SparkContext(sparkConf)
//    sc.setLogLevel("ERROR")
    //    执行的操作
    // 读取文档
    val lines: RDD[String] = sc.textFile("datas")
    // 以空格分割线
    val words: RDD[String] = lines.flatMap(_.split(" "))
    //
    //    //    方法1 单纯用scala实现
    //    //    // 将数据进行分组方便统计
    //    //    val wordGroup: RDD[(String, Iterable[String])] = words.groupBy(word => word)
    //    //    // 对分组后的数据进行转换
    //    //    val wordToCount = wordGroup.map {
    //    //      case (word, list) => {
    //    //        (word, list.size)
    //    //      }
    //    //    }
    //
    //    方法2 用spark提供的api reduceByKey实现
    val wordToOne = words.map(
      word => (word, 1)
    )
    var wordToCount = wordToOne.reduceByKey(_ + _)

    // 将数据转换为集合
    val array: Array[(String, Int)] = wordToCount.collect()
    array.foreach(println)
    //    关闭连接
    sc.stop()
  }
}

运行程序后输出可能有有很多，这时就需要对 log4j 信息进行选择性输出，另外我在pom里已经将Spring Boot 的log给去了

log4j.properties 创建该文件，创建好后，运行程序可能还会输出一大堆信息，此时需要将该文件拷贝进 target 目录即可

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 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

第二种办法在 application.yml 里加入

  spark:
    rpc:
      message:
        maxSize: 1024

第三种办法在 object 代码里加入以下代码：

      // 过滤无效日志，这个是我最终使用的方式
      Logger.getLogger("org").setLevel(Level.ERROR)
      //    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
      // 代码里写这个也可以都可以生效
       spark.sparkContext.setLogLevel("ERROR")

posted @ 2022-08-18 09:30 lambertlt 阅读(277) 评论(0) 编辑收藏举报

会员力量，点亮园子希望

刷新页面返回顶部

lambert's blog

停杯投箸不能食，拔剑四顾心茫然

idea下Spring Boot 集成 scala实现 wordCount

公告