基于Spark3.4.4开发StructuredStreaming读取socket数据

maven依赖文件如下：

XML 复制代码

<?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>cn.lh.pblh123</groupId>
    <artifactId>spark2024</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

<!--    设置国内maven下载镜像源-->
    <repositories>
        <repository>
            <id>alimaven</id>
            <name>aliyun maven</name>
            <url>https://maven.aliyun.com/repository/public</url>
        </repository>
    </repositories>

    <dependencies>
        <dependency> <!-- Spark dependency -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.4.4</version>
            <exclusions>  <!--设置日志级别-->
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>slf4j-log4j12</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency> <!-- Spark dependency -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>3.4.4</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.12</artifactId>
            <version>3.4.4</version> <!-- 请根据实际版本调整 -->
        </dependency>


        <!--        添加spark streaming依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>3.4.4</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.33</version>
        </dependency>

    </dependencies>

    <build>
        <sourceDirectory>src/main/scala</sourceDirectory>
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <scalaVersion>2.12.17</scalaVersion>
                    <args>
                        <arg>-target:jvm-1.8</arg>
                    </args>
                </configuration>
            </plugin>
        </plugins>
    </build>

</project>

源码如下：

Scala 复制代码

package cn.lh.pblh123.spark2024.theorycourse.charpter8

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.streaming.{StreamingQueryException, Trigger}

object StructureNetworkWordCount {

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

    if (args.length != 1 || args(0).trim.isEmpty) {
      System.err.println(s"Usage: ${this.getClass.getSimpleName} <master_url>")
      System.exit(5)
    }

    val murl = args(0)
    val spark = SparkSession.builder().appName(s"${this.getClass.getSimpleName}").master(murl).getOrCreate()

    // 从配置文件或环境变量中读取主机名和端口号
    val host = sys.env.getOrElse("SOCKET_HOST", "localhost")
    val port = sys.env.getOrElse("SOCKET_PORT", "9999").toInt

    try {
      val lines = readSocketStream(spark, host, port)
      import spark.implicits._
      // 导入Spark隐式转换，使得可以使用Spark SQL和Dataset相关操作

      val words = lines.as[String].flatMap(_.split(" "))
      val wordCounts = words.groupBy("value").count()

      val query = wordCounts.writeStream.outputMode("complete")
        .format("console")
        .trigger(Trigger.ProcessingTime("5 seconds"))
        .start()

      query.awaitTermination()

    } catch {
      case e: StreamingQueryException =>
        println(s"Streaming query failed with exception: ${e.getMessage}")
      // 可以在这里添加更多的错误处理逻辑，例如重试机制
      case e: Exception =>
        println(s"An unexpected error occurred: ${e.getMessage}")
      // 可以在这里添加更多的错误处理逻辑
    }



    spark.stop()
  }

  /**
   * 读取 Socket 流数据
   *
   * @param spark SparkSession 实例
   * @param host  主机名
   * @param port  端口号
   * @return 读取的 DataFrame
   */
  def readSocketStream(spark: SparkSession, host: String, port: Int): org.apache.spark.sql.DataFrame = {
    // 读取来自指定主机和端口的socket数据流
    spark.readStream.format("socket")
      .options(Map("host" -> host, "port" -> port.toString))
      .load()
      }
  //    待优化代码如下
  //    val lines = spark.readStream.format("socket").option("host", "localhost").option("port", 9999).load()

}

终端启动nc服务

bash 复制代码

(base) pblh123@LeginR7:~$ nc -lk 9999
i like hadoop
i like spark
你好世界 你

代码运行效果如下，需要先启动nc服务后在启动