spark join操作解读

时间：2023-03-09 06:50:09
spark join操作解读

本文主要介绍spark join相关操作，Java描述。

讲述三个方法spark join，left-outer-join，right-outer-join

我们以实例来进行说明。我的实现步骤记录如下。

1、数据准备

2、HSQL描述

3、Spark描述

1、数据准备

我们准备两张Hive表，分别是orders（订单表）和drivers（司机表），通过driver_id字段进行关联。数据如下：

orders

hive (gulfstream_test)> select * from orders;
OK
orders.order_id orders.driver_id

Time taken:  row(s)

drivers

hive (gulfstream_test)> select * from drivers;
OK
drivers.driver_id       drivers.car_id

Time taken:  row(s)

2、HSQL描述

JOIN

自然连接，输出连接键匹配的记录。

hive (gulfstream_test)> select * from orders t1 join drivers t2 on (t1.driver_id = t2.driver_id) ;
OK
t1.order_id     t1.driver_id    t2.driver_id    t2.car_id

Time taken:  row(s)

LEFT OUTER JOIN

左外链接，输出连接键匹配的记录，左侧的表无论匹配与否都输出。

hive (gulfstream_test)> select * from orders t1 left outer join drivers t2 on (t1.driver_id = t2.driver_id) ;
OK
t1.order_id     t1.driver_id    t2.driver_id    t2.car_id

        NULL    NULL
        NULL    NULL
Time taken:  row(s)

RIGHT OUTER JOIN

右外连接，输出连接键匹配的记录，右侧的表无论匹配与否都输出。

hive (gulfstream_test)> select * from orders t1 right outer join drivers t2 on (t1.driver_id = t2.driver_id) ;
OK
t1.order_id     t1.driver_id    t2.driver_id    t2.car_id

Time taken:  row(s)

3、Spark描述

Join.java

spark实现join的方式也是通过RDD的算子，spark同样提供了三个算子join，leftOuterJoin，rightOuterJoin。

在下面给出的例子中，我们通过spark-hive读取了Hive表中的数据，并将DataFrame转化成了RDD。

在join之后，通过collect()函数把数据拉到Driver端本地，并通过标准输出打印。

需要指出的是：

1）join算子（join，leftOuterJoin，rightOuterJoin）只能通过PairRDD使用；

2）join算子操作的Tuple2<Object1, Object2>类型中，Object1是连接键，我只试过Integer和String，Object2比较灵活，甚至可以是整个Row。

package com.kangaroo.studio.algorithms.join;

import com.google.common.base.Optional;
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.PairFunction;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.hive.HiveContext;
import scala.Tuple2;

import java.io.Serializable;
import java.util.Iterator;

/*
*   spark-submit --queue=root.zhiliangbu_prod_datamonitor spark-join-1.0-SNAPSHOT-jar-with-dependencies.jar
* */
public class Join implements Serializable {

    private transient JavaSparkContext javaSparkContext;
    private transient HiveContext hiveContext;

    /*
    *   初始化Load
    *   创建sparkContext, sqlContext, hiveContext
    * */
    public Join() {
        initSparckContext();
        initHiveContext();
    }

    /*
    *   创建sparkContext
    * */
    private void initSparckContext() {
        String warehouseLocation = System.getProperty("user.dir");
        SparkConf sparkConf = new SparkConf()
                .setAppName("spark-join")
                .set("spark.sql.warehouse.dir", warehouseLocation)
                .setMaster("yarn-client");
        javaSparkContext = new JavaSparkContext(sparkConf);
    }

    /*
    *   创建hiveContext
    *   用于读取Hive中的数据
    * */
    private void initHiveContext() {
        hiveContext = new HiveContext(javaSparkContext);
    }

    public void join() {
        /*
        *   生成rdd1
        * */
        String query1 = "select * from gulfstream_test.orders";
        DataFrame rows1 = hiveContext.sql(query1).select("order_id", "driver_id");
        JavaPairRDD<String, String> rdd1 = rows1.toJavaRDD().mapToPair(new PairFunction<Row, String, String>() {
            @Override
            public Tuple2<String, String> call(Row row) throws Exception {
                String orderId = (String)row.get(0);
                String driverId = (String)row.get(1);
                return new Tuple2<String, String>(driverId, orderId);
            }
        });
        /*
        *   生成rdd2
        * */
        String query2 = "select * from gulfstream_test.drivers";
        DataFrame rows2 = hiveContext.sql(query2).select("driver_id", "car_id");
        JavaPairRDD<String, String> rdd2 = rows2.toJavaRDD().mapToPair(new PairFunction<Row, String, String>() {
            @Override
            public Tuple2<String, String> call(Row row) throws Exception {
                String driverId = (String)row.get(0);
                String carId = (String)row.get(1);
                return new Tuple2<String, String>(driverId, carId);
            }
        });
        /*
        *   join
        * */
        System.out.println(" ****************** join *******************");
        JavaPairRDD<String, Tuple2<String, String>> joinRdd = rdd1.join(rdd2);
        Iterator<Tuple2<String, Tuple2<String, String>>> it1 = joinRdd.collect().iterator();
        while (it1.hasNext()) {
            Tuple2<String, Tuple2<String, String>> item = it1.next();
            System.out.println("driver_id:" + item._1 + ", order_id:" + item._2._1 + ", car_id:" + item._2._2 );
        }

        /*
        *   leftOuterJoin
        * */
        System.out.println(" ****************** leftOuterJoin *******************");
        JavaPairRDD<String, Tuple2<String, Optional<String>>> leftOuterJoinRdd = rdd1.leftOuterJoin(rdd2);
        Iterator<Tuple2<String, Tuple2<String, Optional<String>>>> it2 = leftOuterJoinRdd.collect().iterator();
        while (it2.hasNext()) {
            Tuple2<String, Tuple2<String, Optional<String>>> item = it2.next();
            System.out.println("driver_id:" + item._1 + ", order_id:" + item._2._1 + ", car_id:" + item._2._2 );
        }

        /*
        *   rightOuterJoin
        * */
        System.out.println(" ****************** rightOuterJoin *******************");
        JavaPairRDD<String, Tuple2<Optional<String>, String>> rightOuterJoinRdd = rdd1.rightOuterJoin(rdd2);
        Iterator<Tuple2<String, Tuple2<Optional<String>, String>>> it3 = rightOuterJoinRdd.collect().iterator();
        while (it3.hasNext()) {
            Tuple2<String, Tuple2<Optional<String>, String>> item = it3.next();
            System.out.println("driver_id:" + item._1 + ", order_id:" + item._2._1 + ", car_id:" + item._2._2 );
        }
    }

    public static void main(String[] args) {
        Join sj = new Join();
        sj.join();
    }

}

pom.xml

pom依赖

这里只依赖spark-core和spark-hive两个jar。

<dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.10</artifactId>
            <version>1.6.0</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.10</artifactId>
            <version>1.6.0</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>

打包方式

 <build>
        <plugins>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <archive>
                        <manifest>
                            <!--这里要替换成jar包main方法所在类 -->
                            <mainClass>com.kangaroo.studio.algorithms.join.Join</mainClass>
                        </manifest>
                    </archive>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id> <!-- this is used for inheritance merges -->
                        <phase>package</phase> <!-- 指定在打包节点执行jar包合并操作 -->
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>1.6</source>
                    <target>1.6</target>
                </configuration>
            </plugin>
        </plugins>
    </build>

执行结果

其中Optional.absent()表示的就是null，可以看到和HSQL是一致的。

Application ID is application_1508228032068_2746260, trackingURL: http://10.93.21.21:4040
 ****************** join *******************
driver_id:, order_id:, car_id:
 ****************** leftOuterJoin *******************
driver_id:, order_id:, car_id:Optional.absent()
driver_id:, order_id:, car_id:Optional.absent()
driver_id:, order_id:, car_id:Optional.of()
 ****************** rightOuterJoin *******************
driver_id:, order_id:Optional.absent(), car_id:
driver_id:, order_id:Optional.of(), car_id:

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