Spark SQL集合数据类型array\map的取值方式

Spark SQL集合数据类型array\map的取值方式

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作者:http://blog.csdn.net/wang_wbq

本节主要讨论集合数据类型:数组\列表array字典map这两种数据类型的索引,首先我们还是先构造数据结构与DataFrame:

scala> case class A(a: String, b: Int)
defined class A

scala> case class B(c: List[A], d: Map[String, A], e: Map[Int, String], f: Map[A, String])
defined class B

scala> def a_gen(i: Int) = A(s"str_$i", i)
a_gen: (i: Int)A                                                                                                                                                                    

scala> def b_gen(i: Int) = B((1 to 10).map(a_gen).toList, (1 to 10).map(j => s"key_$j" -> a_gen(j)).toMap, (1 to 10).map(j => j -> s"value_$j").toMap, (1 to 10).map(j => a_gen(j) -> s"value_$j").toMap)
b_gen: (i: Int)B

scala> val data = (1 to 10).map(b_gen)

scala> val df = spark.createDataFrame(data)
df: org.apache.spark.sql.DataFrame = [c: array<struct<a:string,b:int>>, d: map<string,struct<a:string,b:int>> ... 2 more fields]

scala> df.show
+--------------------+--------------------+--------------------+--------------------+
|                   c|                   d|                   e|                   f|
+--------------------+--------------------+--------------------+--------------------+
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
|[[str_1, 1], [str...|[key_2 -> [str_2,...|[5 -> value_5, 10...|[[str_8, 8] -> va...|
+--------------------+--------------------+--------------------+--------------------+


scala> df.printSchema
root
 |-- c: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- a: string (nullable = true)
 |    |    |-- b: integer (nullable = false)
 |-- d: map (nullable = true)
 |    |-- key: string
 |    |-- value: struct (valueContainsNull = true)
 |    |    |-- a: string (nullable = true)
 |    |    |-- b: integer (nullable = false)
 |-- e: map (nullable = true)
 |    |-- key: integer
 |    |-- value: string (valueContainsNull = true)
 |-- f: map (nullable = true)
 |    |-- key: struct
 |    |-- value: string (valueContainsNull = true)
 |    |    |-- a: string (nullable = true)
 |    |    |-- b: integer (nullable = false)

数组\列表array的索引方式

我们首先来看一下数组\列表array的索引方式:

//c的数据类型为array,我们可以单纯使用点的方式把数组中的某个结构给提取出来
//同样可以使用expr("c['a']")或col("c")("a")的方式获得相同的结果。
scala> df.select("c.a").show(10, false)
+-----------------------------------------------------------------------+
|a                                                                      |
+-----------------------------------------------------------------------+
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
|[str_1, str_2, str_3, str_4, str_5, str_6, str_7, str_8, str_9, str_10]|
+-----------------------------------------------------------------------+


scala> df.select("c.a").printSchema
root
 |-- a: array (nullable = true)
 |    |-- element: string (containsNull = true)


//这里介绍一个很有用的表达式explode,它能把数组中的元素展开成多行数据
//比如:
//> SELECT explode(array(10, 20));
// 10
// 20
//还有一个比较有用的函数是posexplode,顾名思义,这个函数会增加一列原始数组的索引
scala> df.select(expr("explode(c.a)")).show
+------+
|   col|
+------+
| str_1|
| str_2|
| str_3|
| str_4|
| str_5|
| str_6|
| str_7|
| str_8|
| str_9|
|str_10|
| str_1|
| str_2|
| str_3|
| str_4|
| str_5|
| str_6|
| str_7|
| str_8|
| str_9|
|str_10|
+------+
only showing top 20 rows

scala> df.select(expr("explode(c.a)")).printSchema
root
 |-- col: string (nullable = true)

scala> df.select(expr("explode(c)")).show
+------------+
|         col|
+------------+
|  [str_1, 1]|
|  [str_2, 2]|
|  [str_3, 3]|
|  [str_4, 4]|
|  [str_5, 5]|
|  [str_6, 6]|
|  [str_7, 7]|
|  [str_8, 8]|
|  [str_9, 9]|
|[str_10, 10]|
|  [str_1, 1]|
|  [str_2, 2]|
|  [str_3, 3]|
|  [str_4, 4]|
|  [str_5, 5]|
|  [str_6, 6]|
|  [str_7, 7]|
|  [str_8, 8]|
|  [str_9, 9]|
|[str_10, 10]|
+------------+
only showing top 20 rows

scala> df.select(expr("explode(c)")).printSchema
root
 |-- col: struct (nullable = true)
 |    |-- a: string (nullable = true)
 |    |-- b: integer (nullable = false)

//inline也是一个非常有用的函数,它可以把array[struct[XXX]]直接展开成XXX
scala> df.select(expr("inline(c)")).show
+------+---+
|     a|  b|
+------+---+
| str_1|  1|
| str_2|  2|
| str_3|  3|
| str_4|  4|
| str_5|  5|
| str_6|  6|
| str_7|  7|
| str_8|  8|
| str_9|  9|
|str_10| 10|
| str_1|  1|
| str_2|  2|
| str_3|  3|
| str_4|  4|
| str_5|  5|
| str_6|  6|
| str_7|  7|
| str_8|  8|
| str_9|  9|
|str_10| 10|
+------+---+
only showing top 20 rows

scala> df.select(expr("inline(c)")).printSchema
root
 |-- a: string (nullable = true)
 |-- b: integer (nullable = false)

字典map的索引方式

下面我们来介绍map的类型的索引方式,其实也无外乎就是我们之前常用的几点
1、点表达式 a.b
2、中括号表达式 expr(“a[‘b’]”)
3、小括号表达式 col(“a”)(“b”)
只是最后取得的列名不同

scala> df.select(expr("posexplode(d)")).printSchema
root
 |-- pos: integer (nullable = false)
 |-- key: string (nullable = false)
 |-- value: struct (nullable = true)
 |    |-- a: string (nullable = true)
 |    |-- b: integer (nullable = false)


scala> df.select(expr("posexplode(e)")).printSchema
root
 |-- pos: integer (nullable = false)
 |-- key: integer (nullable = false)
 |-- value: string (nullable = true)

scala> df.select(expr("posexplode(f)")).show
+---+------------+--------+
|pos|         key|   value|
+---+------------+--------+
|  0|  [str_8, 8]| value_8|
|  1|[str_10, 10]|value_10|
|  2|  [str_3, 3]| value_3|
|  3|  [str_1, 1]| value_1|
|  4|  [str_6, 6]| value_6|
|  5|  [str_5, 5]| value_5|
|  6|  [str_7, 7]| value_7|
|  7|  [str_2, 2]| value_2|
|  8|  [str_4, 4]| value_4|
|  9|  [str_9, 9]| value_9|
|  0|  [str_8, 8]| value_8|
|  1|[str_10, 10]|value_10|
|  2|  [str_3, 3]| value_3|
|  3|  [str_1, 1]| value_1|
|  4|  [str_6, 6]| value_6|
|  5|  [str_5, 5]| value_5|
|  6|  [str_7, 7]| value_7|
|  7|  [str_2, 2]| value_2|
|  8|  [str_4, 4]| value_4|
|  9|  [str_9, 9]| value_9|
+---+------------+--------+

scala> df.select(expr("posexplode(f)")).printSchema
root
 |-- pos: integer (nullable = false)
 |-- key: struct (nullable = false)
 |    |-- a: string (nullable = true)
 |    |-- b: integer (nullable = false)
 |-- value: string (nullable = true)

//我们可以使用点表达式去用map的key取value
//如果key不存在这行数据会为null
scala> df.select("d.key_1").show
+----------+
|     key_1|
+----------+
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
|[str_1, 1]|
+----------+


scala> df.select("d.key_1").printSchema
root
 |-- key_1: struct (nullable = true)
 |    |-- a: string (nullable = true)
 |    |-- b: integer (nullable = false)

//数字为key同样可以使用
//对于数字来讲,expr("e[1]")、expr("e['1']")、col("e")(1)、col("e")("1")这四种表达式都可用
//只是最后取得的列名不同
scala> df.select("e.1").show
+-------+
|      1|
+-------+
|value_1|
|value_1|
|value_1|
|value_1|
|value_1|
|value_1|
|value_1|
|value_1|
|value_1|
|value_1|
+-------+


scala> df.select("e.1").printSchema
root
 |-- 1: string (nullable = true)

在学习了struct和array的取值后,再看map的取值是不是就特别简单了,下面我们来看一个难一点的例子

最有意思的就是f这个map了,我们用struct作为map的key
这种情况下,我们可以用namedExpressionSeq表达式类构造这个struct

scala> df.select(expr("f[('str_1' AS a, 1 AS b)]")).show
+---------------------------------------------+
|f[named_struct(a, str_1 AS `a`, b, 1 AS `b`)]|
+---------------------------------------------+
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
|                                      value_1|
+---------------------------------------------+


scala> df.select(expr("f[('str_1' AS a, 1 AS b)]")).printSchema
root
 |-- f[named_struct(a, str_1 AS `a`, b, 1 AS `b`)]: string (nullable = true)

以上这种构造方式当然不是凭空想出来的,依据呢当然还是我之前提到的另一个博客里介绍的查看方式https://blog.csdn.net/wang_wbq/article/details/79673780

我们可以在SqlBase.g4文件中找到以下词法描述

primaryExpression
    : #前面太长不看
    | '(' namedExpression (',' namedExpression)+ ')'         #rowConstructor
    #中间太长不看
    | value=primaryExpression '[' index=valueExpression ']'  #subscript
    #后面太长不看
    ;

valueExpression
    : primaryExpression                                                                      
    #后面太长不看
    ;

namedExpression
    : expression (AS? (identifier | identifierList))?
    ;

从上面我们可以看出:
1、中括号里需要放置valueExpression
2、valueExpression可以是一个primaryExpression
3、primaryExpression可以是一个'(' namedExpression (',' namedExpression)+ ')'结构
4、namedExpression又是一个exp AS alias的结构

因此,显而易见,我们可以用这种方式来构造结构体去匹配map的key

posted @ 2018-03-24 15:15  海角Q  阅读(2279)  评论(0编辑  收藏  举报