How can you parse a string that is json from an existing temp table using PySpark?

Question

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

Answer 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|
+---+------+-------+