spark-sql操作array和map和struct类型数据

原文链接（很nice的一篇文章）：https://blog.csdn.net/wang_wbq/article/details/79678168

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>, d: map> ... 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...| +--------------------+--------------------+--------------------+--------------------+

 

 

 

--主要看schema信息

scala> df.printSchema root：c字段是array类型，d字段是map嵌套struct类型，e是map类型，f是map类型key是struct类型  |-- 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)

 

 

• 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)") as.(Seq("a","b"))).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)

 

• 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) // 索引  |-- 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

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