pyspark：将字符串转换为结构

Question

我的数据如下-

{
    "Id": "01d3050e",
    "Properties": "{\"choices\":null,\"object\":\"demo\",\"database\":\"pg\",\"timestamp\":\"1581534117303\"}",
    "LastUpdated": 1581530000000,
    "LastUpdatedBy": "System"
}

使用aws胶，我想关联“属性”列，但是由于数据类型是字符串，因此无法完成。将其转换为struct可能会基于阅读此博客来完成-

https://aws.amazon.com/blogs/big-data/simplify-querying-nested-json-with-the-aws-glue-relationalize-transform/

>>> df.show
<bound method DataFrame.show of DataFrame[Id: string, LastUpdated: bigint, LastUpdatedBy: string, Properties: string]>
>>> df.show()
+--------+-------------+-------------+--------------------+
|      Id|  LastUpdated|LastUpdatedBy|          Properties|
+--------+-------------+-------------+--------------------+
|01d3050e|1581530000000|       System|{"choices":null,"...|
+--------+-------------+-------------+--------------------+

我如何使用关系转换器或pyspark中的任何UDF取消嵌套“属性”列以将其分为“选择”，“对象”，“数据库”和“时间戳”列。

Answer 1

创建数据框：

from pyspark.sql import functions as F
list=[["01d3050e","{\"choices\":null,\"object\":\"demo\",\"database\":\"pg\",\"timestamp\":\"1581534117303\"}",1581530000000,"System"]]
df=spark.createDataFrame(list, ['Id','Properties','LastUpdated','LastUpdatedBy'])
df.show(truncate=False)

+--------+----------------------------------------------------------------------------+-------------+-------------+
|Id      |Properties                                                                  |LastUpdated  |LastUpdatedBy|
+--------+----------------------------------------------------------------------------+-------------+-------------+
|01d3050e|{"choices":null,"object":"demo","database":"pg","timestamp":"1581534117303"}|1581530000000|System       |
+--------+----------------------------------------------------------------------------+-------------+-------------+

使用内置的正则表达式，拆分和element_at：

无需使用UDF，内置功能就足够了，并且已经针对大数据任务进行了非常优化。

df.withColumn("Properties", F.split(F.regexp_replace(F.regexp_replace((F.regexp_replace("Properties",'\{|}',"")),'\:',','),'\"|"',"").cast("string"),','))\
.withColumn("choices", F.element_at("Properties",2))\
.withColumn("object", F.element_at("Properties",4))\
.withColumn("database",F.element_at("Properties",6))\
.withColumn("timestamp",F.element_at("Properties",8)).drop("Properties").show()


+--------+-------------+-------------+-------+------+--------+-------------+
|      Id|  LastUpdated|LastUpdatedBy|choices|object|database|    timestamp|
+--------+-------------+-------------+-------+------+--------+-------------+
|01d3050e|1581530000000|       System|   null|  demo|      pg|1581534117303|
+--------+-------------+-------------+-------+------+--------+-------------+