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: