Elasticsearch 集成---Spark Streaming 框架集成

news2025/11/4 19:11:59

一.Spark Streaming 框架介绍

Spark Streaming 是 Spark core API 的扩展，支持实时数据流的处理，并且具有可扩展，

高吞吐量，容错的特点。

数据可以从许多来源获取，如 Kafka ， Flume ， Kinesis 或 TCP sockets ，

并且可以使用复杂的算法进行处理，这些算法使用诸如 map ， reduce ， join 和 window 等高

级函数表示。最后，处理后的数据可以推送到文件系统，数据库等。实际上，您可以将

Spark 的机器学习和图形处理算法应用于数据流。

二.框架集成

1. 创建 Maven 项目

<?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>com.atguigu.es</groupId>
    <artifactId>es-sparkstreaming</artifactId>
    <version>1.0</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.elasticsearch</groupId>
            <artifactId>elasticsearch</artifactId>
            <version>7.8.0</version>
        </dependency>
        <!-- elasticsearch的客户端 -->
        <dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>elasticsearch-rest-high-level-client</artifactId>
            <version>7.8.0</version>
        </dependency>
        <!-- elasticsearch依赖2.x的log4j -->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
            <version>2.8.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.8.2</version>
        </dependency>
        <!--        <dependency>-->
        <!--            <groupId>com.fasterxml.jackson.core</groupId>-->
        <!--            <artifactId>jackson-databind</artifactId>-->
        <!--            <version>2.11.1</version>-->
        <!--        </dependency>-->
        <!--        &lt;!&ndash; junit单元测试 &ndash;&gt;-->
        <!--        <dependency>-->
        <!--            <groupId>junit</groupId>-->
        <!--            <artifactId>junit</artifactId>-->
        <!--            <version>4.12</version>-->
        <!--        </dependency>-->
    </dependencies>
</project>

2.功能实现

package com.atguigu.es

import org.apache.http.HttpHost
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.ReceiverInputDStream
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.elasticsearch.action.index.{IndexRequest, IndexResponse}
import org.elasticsearch.client.{RequestOptions, RestClient, RestHighLevelClient}
import org.elasticsearch.common.xcontent.XContentType

object SparkStreamingESTest {

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

        val sparkConf = new SparkConf().setMaster("local[*]").setAppName("ESTest")
        val ssc = new StreamingContext(sparkConf, Seconds(3))

        val ds: ReceiverInputDStream[String] = ssc.socketTextStream("localhost", 9999)
        ds.foreachRDD(
            rdd => {
                rdd.foreach(
                    data => {
                        val client = new RestHighLevelClient(
                            RestClient.builder(new HttpHost("localhost",9200, "http"))
                        )

                        val ss = data.split(" ")

                        val request = new IndexRequest()
                        request.index("product").id(ss(0))
                        val json =
                            s"""
                              | {  "data" : "${ss(1)}" }
                              |""".stripMargin
                        request.source(json, XContentType.JSON)

                        val response: IndexResponse = client.index(request, RequestOptions.DEFAULT)
                        println(response.getResult)
                        client.close()
                    }
                )
            }
        )

        ssc.start()
        ssc.awaitTermination()
    }
}