如何在PySpark中将字符串转换为字典(JSON)的ArrayType [英] How to cast string to ArrayType of dictionary (JSON) in PySpark

问题描述

尝试将StringType转换为JSON的ArrayType，以生成由CSV生成的数据框.

Trying to cast StringType to ArrayType of JSON for a dataframe generated form CSV.

在Spark2上使用pyspark

我正在处理的CSV文件；如下-

The CSV file I am dealing with; is as follows -

date,attribute2,count,attribute3
2017-09-03,'attribute1_value1',2,'[{"key":"value","key2":2},{"key":"value","key2":2},{"key":"value","key2":2}]'
2017-09-04,'attribute1_value2',2,'[{"key":"value","key2":20},{"key":"value","key2":25},{"key":"value","key2":27}]'

如上所示，它在文字字符串中包含一个属性"attribute3"，从技术上讲，这是一列字典(JSON)，精确长度为2. (这是功能不同的输出)

As shown above, it contains one attribute "attribute3" in literal string, which is technically a list of dictionary(JSON) with exact length of 2. (This is the output of function distinct)

printSchema()

attribute3: string (nullable = true)

我正尝试将"attribute3"强制转换为ArrayType

I am trying to cast the "attribute3" to ArrayType as follows

temp = dataframe.withColumn(
    "attribute3_modified",
    dataframe["attribute3"].cast(ArrayType())
)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: __init__() takes at least 2 arguments (1 given)

实际上，ArrayType需要数据类型作为参数.我尝试使用"json"，但是没有用.

Indeed, ArrayType expects datatype as argument. I tried with "json", but it did not work.

所需输出- 最后，我需要将attribute3转换为ArrayType()或简单的简单Python列表. (我正在尝试避免使用eval)

Desired Output - In the end, I need to convert attribute3 to ArrayType() or plain simple Python list. (I am trying to avoid use of eval)

如何将其转换为ArrayType，以便将其视为JSON列表?

How do I convert it to ArrayType, so that I can treat it as list of JSONs?

我在这里错过了什么吗?

Am I missing anything here?

(文档，未解决这个问题很简单)

(The documentation,does not address this problem in straightforward way)

推荐答案

使用

Use from_json with a schema that matches the actual data in attribute3 column to convert json to ArrayType:

原始数据框:

df.printSchema()
#root
# |-- date: string (nullable = true)
# |-- attribute2: string (nullable = true)
# |-- count: long (nullable = true)
# |-- attribute3: string (nullable = true)

from pyspark.sql.functions import from_json
from pyspark.sql.types import *

创建模式:

schema = ArrayType(
    StructType([StructField("key", StringType()), 
                StructField("key2", IntegerType())]))

使用from_json:

df = df.withColumn("attribute3", from_json(df.attribute3, schema))

df.printSchema()
#root
# |-- date: string (nullable = true)
# |-- attribute2: string (nullable = true)
# |-- count: long (nullable = true)
# |-- attribute3: array (nullable = true)
# |    |-- element: struct (containsNull = true)
# |    |    |-- key: string (nullable = true)
# |    |    |-- key2: integer (nullable = true)

df.show(1, False)
#+----------+----------+-----+------------------------------------+
#|date      |attribute2|count|attribute3                          |
#+----------+----------+-----+------------------------------------+
#|2017-09-03|attribute1|2    |[[value, 2], [value, 2], [value, 2]]|
#+----------+----------+-----+------------------------------------+

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