I have an existing Spark dataframe that has columns as such:
--------------------
pid | response
--------------------
12 | {"status":"200"}
response is a string column. Is there a way to cast it into JSON and extract specific fields? Can lateral view be used as it is in Hive? I looked up some examples on line that used explode and later view but it doesn't seem to work with Spark 2.1.1
From pyspark.sql.functions , you can use any of from_json,get_json_object,json_tuple to extract fields from json string as below,
>>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|
+---+------+-------+
