Java Spark读取ElasticSearch数据，并分析

时间 2019-11-11

标签 java spark 读取 elasticsearch 数据分析栏目 Java 繁體版

原文原文链接

直接在ES服务器上获取的数据以下所示java

使用的java代码以下所示（能够看到数据都被封装成了tuple）：node

package mainPackage;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.sql.SparkSession;
import org.elasticsearch.spark.rdd.api.java.JavaEsSpark;
import scala.Tuple2;

import java.util.Map;

public class MainClass {
    public static void main(String[] args){
        run1();
    }
    private static void run1(){
        SparkConf conf = new SparkConf();
        conf.setMaster("local[1]");
        conf.setAppName("SPARK ES");

        conf.set("es.index.auto.create", "true");
        conf.set("es.nodes", "roc-3");
        conf.set("es.port", "8086");

        JavaSparkContext sc = new JavaSparkContext(conf);
        sc.setLogLevel("ERROR");

        JavaPairRDD<String, Map<String, Object>> esRDD =
                JavaEsSpark.esRDD(sc, "my-index4/person");

        for(Tuple2 tuple:esRDD.collect()){
            System.out.print(tuple._1()+"----------");
            System.out.println(tuple._2());
        }

    }
}

运行的语句以下：es6

spark2-submit --master local[*] --packages org.elasticsearch:elasticsearch-hadoop:6.4.0 --class mainPackage.MainClass --repositories http://maven.aliyun.com/nexus/content/groups/public,http://conjars.org/repo/  AID-1.0-SNAPSHOT.jar

若是读取的数据十分巨大，能够用下面的执行语句sql

spark2-submit --master local[*]  --executor-memory 16G --driver-memory 32G  --packages org.elasticsearch:elasticsearch-hadoop:6.4.0 --class mainPackage.MainClass --repositories http://maven.aliyun.com/nexus/content/groups/public,http://conjars.org/repo/ --conf spark.yarn.executor.memoryOverhead=8196 --conf spark.rpc.message.maxSize=2000 --conf spark.driver.maxResultSize=32g --conf spark.kryoserializer.buffer.max.mb=2040 --conf spark.debug.maxToStringFields=200  AID-1.0-SNAPSHOT.jar

设置repository可使用指定的资源库下载jar数据库

最终结果数据apache

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
18/09/07 19:19:21 INFO SparkContext: Running Spark version 2.0.0
……
省略N行
……
18/09/07 19:19:23 INFO MemoryStore: MemoryStore started with capacity 893.1 MB
18/09/07 19:19:23 INFO SparkEnv: Registering OutputCommitCoordinator
18/09/07 19:19:23 INFO Utils: Successfully started service 'SparkUI' on port 4040.
18/09/07 19:19:23 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://10.116.16.141:4040
18/09/07 19:19:23 INFO Executor: Starting executor ID driver on host localhost
18/09/07 19:19:23 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 10681.
18/09/07 19:19:23 INFO NettyBlockTransferService: Server created on 10.116.16.141:10681
18/09/07 19:19:23 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 10.116.16.141, 10681)
18/09/07 19:19:23 INFO BlockManagerMasterEndpoint: Registering block manager 10.116.16.141:10681 with 893.1 MB RAM, BlockManagerId(driver, 10.116.16.141, 10681)
18/09/07 19:19:23 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 10.116.16.141, 10681)
1----------{desc=数据库管理, user=张三, title=工程师}
7----------{desc=数据库管理, user=张5, title=工程师}
13----------{desc=数据库管理, user=张11, title=工程师}
16----------{desc=数据库管理, user=张14, title=工程师}
18----------{desc=数据库管理, user=张16, title=工程师}
2----------{desc=数据库管理, user=张0, title=工程师}
4----------{desc=数据库管理, user=张2, title=工程师}
6----------{desc=数据库管理, user=张4, title=工程师}
15----------{desc=数据库管理, user=张13, title=工程师}
20----------{desc=数据库管理, user=张18, title=工程师}
14----------{desc=数据库管理, user=张12, title=工程师}
19----------{desc=数据库管理, user=张17, title=工程师}
5----------{desc=数据库管理, user=张3, title=工程师}
8----------{desc=数据库管理, user=张6, title=工程师}
9----------{desc=数据库管理, user=张7, title=工程师}
10----------{desc=数据库管理, user=张8, title=工程师}
12----------{desc=数据库管理, user=张10, title=工程师}
21----------{desc=数据库管理, user=张19, title=工程师}
3----------{desc=数据库管理, user=张1, title=工程师}
11----------{desc=数据库管理, user=张9, title=工程师}
17----------{desc=数据库管理, user=张15, title=工程师}

Process finished with exit code 0

一些bug，配置版本和jackon时候须要注意，若是配置成以下就会出问题：json

2.4.4。api

而用这个版本就没事儿bash

默认es6.4.0版本会使用2.6.5版本的jackson来解析json数据，这样就OK服务器

实验证实6.4.0版本es链接6.3.1取数不会遇到版本不兼容问题

最终成功的maven文件配置：

<?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>SparkReadES</groupId>
    <artifactId>AID</artifactId>
    <version>1.0-SNAPSHOT</version>

    <repositories>
        <repository>
            <id>central</id>
            <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
            <snapshots>
                <enabled>true</enabled>
            </snapshots>
        </repository>
    </repositories>


    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.7</maven.compiler.source>
        <maven.compiler.target>1.7</maven.compiler.target>
        <spark.version>2.0.0</spark.version>
        <elasticsearch.version>6.4.0</elasticsearch.version>
    </properties>


    <dependencies>
        <dependency>
            <groupId>org.elasticsearch</groupId>
            <artifactId>elasticsearch-spark-20_2.11</artifactId>
            <version>${elasticsearch.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-yarn_2.11</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

    </dependencies>


    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.0</version>
                <configuration>
                    <encoding>utf-8</encoding><!-- 指定项目源文件编码 -->
                    <source>${maven.compiler.source}</source><!-- 指定项目源文件jdk版本 -->
                    <target>${maven.compiler.target}</target><!-- 指定项目生成目标文件的jdk版本 -->
                </configuration>
            </plugin>

        </plugins>
    </build>
</project>

Done