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Spark的Dataset操作(五)-多表操作 join

[日期:2017-09-29] 来源:csdn  作者:野男孩 [字体: ]

Spark的Dataset操作(五)-多表操作 join

不说废话了,直接上代码。
先看两个源数据表的定义:

scala> val df1 = spark.createDataset(Seq(("aaa", 1, 2), ("bbb", 3, 4), ("ccc", 3, 5), ("bbb", 4, 6)) ).toDF("key1","key2","key3")
df1: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field]

scala> val df2 = spark.createDataset(Seq(("aaa", 2, 2),    ("bbb", 3, 5),    ("ddd", 3, 5),    ("bbb", 4, 6), ("eee", 1, 2), ("aaa", 1, 5), ("fff",5,6))).toDF("key1","key2","key4")
df2: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field]

scala> df1.printSchema
root
 |-- key1: string (nullable = true)
 |-- key2: integer (nullable = false)
 |-- key3: integer (nullable = false)


scala> df2.printSchema
root
 |-- key1: string (nullable = true)
 |-- key2: integer (nullable = false)
 |-- key4: integer (nullable = false)

scala> df1.show()
+----+----+----+
|key1|key2|key3|
+----+----+----+
| aaa|   1|   2|
| bbb|   3|   4|
| ccc|   3|   5|
| bbb|   4|   6|
+----+----+----+

scala> df2.show()
+----+----+----+
|key1|key2|key4|
+----+----+----+
| aaa|   2|   2|
| bbb|   3|   5|
| ddd|   3|   5|
| bbb|   4|   6|
| eee|   1|   2|
| aaa|   1|   5|
| fff|   5|   6|
+----+----+----+

 

Spark对join的支持很丰富,等值连接,条件连接,自然连接都支持。连接类型包括内连接,外连接,左外连接,右外连接,左半连接以及笛卡尔连接。

下面一一示例,先看内连接

/*
内连接 select * from df1 join df2 on df1.key1=df2.key1
*/
scala> val df3 = df1.join(df2,"key1")
df3: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 3 more fields]

scala> df3.printSchema
root
 |-- key1: string (nullable = true)
 |-- key2: integer (nullable = false)
 |-- key3: integer (nullable = false)
 |-- key2: integer (nullable = false)
 |-- key4: integer (nullable = false)

scala> df3.show
+----+----+----+----+----+
|key1|key2|key3|key2|key4|
+----+----+----+----+----+
| aaa|   1|   2|   1|   5|
| aaa|   1|   2|   2|   2|
| bbb|   3|   4|   4|   6|
| bbb|   3|   4|   3|   5|
| bbb|   4|   6|   4|   6|
| bbb|   4|   6|   3|   5|
+----+----+----+----+----+

/*
还是内连接,这次用joinWith。和join的区别是连接后的新Dataset的schema会不一样,注意和上面的对比一下。
*/
scala> val df4=df1.joinWith(df2,df1("key1")===df2("key1"))
df4: org.apache.spark.sql.Dataset[(org.apache.spark.sql.Row, org.apache.spark.sql.Row)] = [_1: struct<key1: string, key2: int ... 1 more field>, _2: struct<key1: string, key2: int ... 1 more field>]

scala> df4.printSchema
root
 |-- _1: struct (nullable = false)
 |    |-- key1: string (nullable = true)
 |    |-- key2: integer (nullable = false)
 |    |-- key3: integer (nullable = false)
 |-- _2: struct (nullable = false)
 |    |-- key1: string (nullable = true)
 |    |-- key2: integer (nullable = false)
 |    |-- key4: integer (nullable = false)

scala> df4.show
+---------+---------+
|       _1|       _2|
+---------+---------+
|[aaa,1,2]|[aaa,1,5]|
|[aaa,1,2]|[aaa,2,2]|
|[bbb,3,4]|[bbb,4,6]|
|[bbb,3,4]|[bbb,3,5]|
|[bbb,4,6]|[bbb,4,6]|
|[bbb,4,6]|[bbb,3,5]|
+---------+---------+

然后是外连接:

/*
select * from df1 outer join df2 on df1.key1=df2.key1 
*/
scala> val df5 = df1.join(df2,df1("key1")===df2("key1"), "outer")
df5: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]

scala> df5.show
+----+----+----+----+----+----+
|key1|key2|key3|key1|key2|key4|
+----+----+----+----+----+----+
|null|null|null| ddd|   3|   5|
| ccc|   3|   5|null|null|null|
| aaa|   1|   2| aaa|   2|   2|
| aaa|   1|   2| aaa|   1|   5|
| bbb|   3|   4| bbb|   3|   5|
| bbb|   3|   4| bbb|   4|   6|
| bbb|   4|   6| bbb|   3|   5|
| bbb|   4|   6| bbb|   4|   6|
|null|null|null| fff|   5|   6|
|null|null|null| eee|   1|   2|
+----+----+----+----+----+----+

下面是左外连接,右外连接和左半连接:

/*
左外连接
*/
scala> val df6 = df1.join(df2,df1("key1")===df2("key1"), "left_outer")
df6: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]

scala> df6.show
+----+----+----+----+----+----+
|key1|key2|key3|key1|key2|key4|
+----+----+----+----+----+----+
| aaa|   1|   2| aaa|   1|   5|
| aaa|   1|   2| aaa|   2|   2|
| bbb|   3|   4| bbb|   4|   6|
| bbb|   3|   4| bbb|   3|   5|
| ccc|   3|   5|null|null|null|
| bbb|   4|   6| bbb|   4|   6|
| bbb|   4|   6| bbb|   3|   5|
+----+----+----+----+----+----+

/*
右外连接
*/
scala> val df7 = df1.join(df2,df1("key1")===df2("key1"), "right_outer")
df7: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]

scala> df7.show
+----+----+----+----+----+----+
|key1|key2|key3|key1|key2|key4|
+----+----+----+----+----+----+
| aaa|   1|   2| aaa|   2|   2|
| bbb|   4|   6| bbb|   3|   5|
| bbb|   3|   4| bbb|   3|   5|
|null|null|null| ddd|   3|   5|
| bbb|   4|   6| bbb|   4|   6|
| bbb|   3|   4| bbb|   4|   6|
|null|null|null| eee|   1|   2|
| aaa|   1|   2| aaa|   1|   5|
|null|null|null| fff|   5|   6|
+----+----+----+----+----+----+

/*
左半连接
*/
scala> val df8 = df1.join(df2,df1("key1")===df2("key1"), "leftsemi")
df8: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field]

scala> df8.show
+----+----+----+
|key1|key2|key3|
+----+----+----+
| aaa|   1|   2|
| bbb|   3|   4|
| bbb|   4|   6|
+----+----+----+

笛卡尔连接不太常用,毕竟现在用spark玩的表都大得很,做这种全连接成本太大了。

/*
笛卡尔连接
*/
scala> val df9 = df1.crossJoin(df2)
df9: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]

scala> df9.count
res17: Long = 28

/* 就显示前10条结果吧 */
scala> df9.show(10)
+----+----+----+----+----+----+
|key1|key2|key3|key1|key2|key4|
+----+----+----+----+----+----+
| aaa|   1|   2| aaa|   2|   2|
| aaa|   1|   2| bbb|   3|   5|
| aaa|   1|   2| ddd|   3|   5|
| aaa|   1|   2| bbb|   4|   6|
| aaa|   1|   2| eee|   1|   2|
| aaa|   1|   2| aaa|   1|   5|
| aaa|   1|   2| fff|   5|   6|
| bbb|   3|   4| aaa|   2|   2|
| bbb|   3|   4| bbb|   3|   5|
| bbb|   3|   4| ddd|   3|   5|
+----+----+----+----+----+----+
only showing top 10 rows

下面这个例子还是个等值连接,区别之前的等值连接是去调用两个表的重复列,就像自然连接一样:

/*
基于两个公共字段key1和key的等值连接
*/
scala> val df10 = df1.join(df2, Seq("key1","key2"))
df10: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 2 more fields]

scala> df10.show
+----+----+----+----+
|key1|key2|key3|key4|
+----+----+----+----+
| aaa|   1|   2|   5|
| bbb|   3|   4|   5|
| bbb|   4|   6|   6|
+----+----+----+----+

条件连接在spark的低版本好像是不支持的,反正现在是ok啦~

/*
select df1.*,df2.* from df1 join df2 
on df1.key1=df2.key1 and df1.key2>df2.key2
*/
scala> val df11 = df1.join(df2, df1("key1")===df2("key1") && df1("key2")>df2("key2"))
df11: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]

scala> df11.show
+----+----+----+----+----+----+
|key1|key2|key3|key1|key2|key4|
+----+----+----+----+----+----+
| bbb|   4|   6| bbb|   3|   5|
+----+----+----+----+----+----+

上一篇: Spark的Dataset操作(四)-其他单表操作

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