如何使用pyspark从现有的临时表中解析一个字符串

Question

我有一个现有的Spark DataFrame，具有这样的列：

--------------------
pid | response
--------------------
 12 | {"status":"200"}

响应是字符串列。有没有办法将其投入JSON并提取特定字段？可以像在蜂巢中一样使用横向视图吗？我在使用爆炸的线上查找了一些示例，但后来视图，但似乎与Spark 2.1.1

Answer 1

从pyspark.sql.functions中，您可以使用from_json,get_json_object,json_tuple的任何一个从下面的JSON字符串中提取字段，

>>from pyspark.sql.functions import json_tuple,from_json,get_json_object
>>> from pyspark.sql import SparkSession
>>> spark = SparkSession.builder.getOrCreate()
>>> l = [(12, '{"status":"200"}'),(13,'{"status":"200","somecol":"300"}')]
>>> df = spark.createDataFrame(l,['pid','response'])
>>> df.show()
+---+--------------------+
|pid|            response|
+---+--------------------+
| 12|    {"status":"200"}|
| 13|{"status":"200",...|
+---+--------------------+
>>> df.printSchema()
root
 |-- pid: long (nullable = true)
 |-- response: string (nullable = true)
Using json_tuple :
>>> df.select('pid',json_tuple(df.response,'status','somecol')).show()
+---+---+----+
|pid| c0|  c1|
+---+---+----+
| 12|200|null|
| 13|200| 300|
+---+---+----+
Using from_json:
>>> schema = StructType([StructField("status", StringType()),StructField("somecol", StringType())])
>>> df.select('pid',from_json(df.response, schema).alias("json")).show()
+---+----------+
|pid|      json|
+---+----------+
| 12|[200,null]|
| 13| [200,300]|
+---+----------+
Using get_json_object:
>>> df.select('pid',get_json_object(df.response,'$.status').alias('status'),get_json_object(df.response,'$.somecol').alias('somecol')).show()
+---+------+-------+
|pid|status|somecol|
+---+------+-------+
| 12|   200|   null|
| 13|   200|    300|
+---+------+-------+