将结构数组分解为一个表。
此函数采用一个包含结构数组的输入列，并返回一个新列，其中数组中的每个结构将分解为一个单独的行。
Syntax
from pyspark.sql import functions as sf
sf.inline(col)
参数
| 参数 | 类型 | Description |
|---|---|---|
col |
pyspark.sql.Column 或列名 |
要分解的值的输入列。 |
退货
pyspark.sql.Column：内联分解结果的生成器表达式。
例子
示例 1：将内联与单个结构数组列配合使用
import pyspark.sql.functions as sf
df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
df.select('*', sf.inline(df.a)).show()
+----------------+---+---+
| a| a| b|
+----------------+---+---+
|[{1, 2}, {3, 4}]| 1| 2|
|[{1, 2}, {3, 4}]| 3| 4|
+----------------+---+---+
示例 2：将内联与列名一起使用
import pyspark.sql.functions as sf
df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
df.select('*', sf.inline('a')).show()
+----------------+---+---+
| a| a| b|
+----------------+---+---+
|[{1, 2}, {3, 4}]| 1| 2|
|[{1, 2}, {3, 4}]| 3| 4|
+----------------+---+---+
示例 3：将内联与别名一起使用
import pyspark.sql.functions as sf
df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
df.select('*', sf.inline('a').alias("c1", "c2")).show()
+----------------+---+---+
| a| c1| c2|
+----------------+---+---+
|[{1, 2}, {3, 4}]| 1| 2|
|[{1, 2}, {3, 4}]| 3| 4|
+----------------+---+---+
示例 4：将内联与多个结构数组列配合使用
import pyspark.sql.functions as sf
df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a1, ARRAY(NAMED_STRUCT("c",5,"d",6), NAMED_STRUCT("c",7,"d",8)) AS a2')
df.select(
'*', sf.inline('a1')
).select('*', sf.inline('a2')).show()
+----------------+----------------+---+---+---+---+
| a1| a2| a| b| c| d|
+----------------+----------------+---+---+---+---+
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 1| 2| 5| 6|
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 1| 2| 7| 8|
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 3| 4| 5| 6|
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 3| 4| 7| 8|
+----------------+----------------+---+---+---+---+
示例 5：将内联与嵌套结构数组列配合使用
import pyspark.sql.functions as sf
df = spark.sql('SELECT NAMED_STRUCT("a",1,"b",2,"c",ARRAY(NAMED_STRUCT("c",3,"d",4), NAMED_STRUCT("c",5,"d",6))) AS s')
df.select('*', sf.inline('s.c')).show(truncate=False)
+------------------------+---+---+
|s |c |d |
+------------------------+---+---+
|{1, 2, [{3, 4}, {5, 6}]}|3 |4 |
|{1, 2, [{3, 4}, {5, 6}]}|5 |6 |
+------------------------+---+---+
示例 6：将内联与包含 null、空数组和 null 的列一起使用
from pyspark.sql import functions as sf
df = spark.sql('SELECT * FROM VALUES (1,ARRAY(NAMED_STRUCT("a",1,"b",2), NULL, NAMED_STRUCT("a",3,"b",4))), (2,ARRAY()), (3,NULL) AS t(i,s)')
df.show(truncate=False)
+---+----------------------+
|i |s |
+---+----------------------+
|1 |[{1, 2}, NULL, {3, 4}]|
|2 |[] |
|3 |NULL |
+---+----------------------+
df.select('*', sf.inline('s')).show(truncate=False)
+---+----------------------+----+----+
|i |s |a |b |
+---+----------------------+----+----+
|1 |[{1, 2}, NULL, {3, 4}]|1 |2 |
|1 |[{1, 2}, NULL, {3, 4}]|NULL|NULL|
|1 |[{1, 2}, NULL, {3, 4}]|3 |4 |
+---+----------------------+----+----+