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PySpark – JSON Functions

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PySpark JSON functions are used to query or extract the elements from JSON string of DataFrame column by path, convert it to struct, map type e.t.c, In this article, I will explain the most used JSON SQL functions with Python examples.

1. PySpark JSON Functions

from_json() – Converts JSON string into Struct type or Map type.

to_json() – Converts MapType or Struct type to JSON string.

json_tuple() – Extract the Data from JSON and create them as a new column.

get_json_object() – Extracts JSON element from a JSON string based on JSON path specified.

schema_of_json() – Create schema string from JSON string

1.1. Create DataFrame with Column Containing JSON String

In order to explain these JSON functions first, let’s create DataFrame with a column containing JSON string.

from pyspark.sql import SparkSession,Row
spark = SparkSession.builder.appName('mytechmint').getOrCreate()

jsonString="""{"Zipcode":704,"ZipCodeType":"STANDARD","City":"PARC PARQUE","State":"PR"}"""
df=spark.createDataFrame([(1, jsonString)],["id","value"])
df.show(truncate=False)
#Output
//+---+--------------------------------------------------------------------------+
//|id |value                                                                     |
//+---+--------------------------------------------------------------------------+
//|1  |{"Zipcode":704,"ZipCodeType":"STANDARD","City":"PARC PARQUE","State":"PR"}|
//+---+--------------------------------------------------------------------------+

2. PySpark JSON Functions Examples

2.1. from_json()

PySpark from_json() the function is used to convert JSON string into Struct type or Map type. The below example converts JSON string to Map key-value pair. I will leave it to you to convert to a struct type.

#Convert JSON string column to Map type
from pyspark.sql.types import MapType,StringType
from pyspark.sql.functions import from_json
df2=df.withColumn("value",from_json(df.value,MapType(StringType(),StringType())))
df2.printSchema()
df2.show(truncate=False)
#Output
//root
// |-- id: integer (nullable = false)
// |-- value: map (nullable = true)
// |    |-- key: string
// |    |-- value: string (valueContainsNull = true)

//+---+---------------------------------------------------------------------------+
//|id |value                                                                      |
//+---+---------------------------------------------------------------------------+
//|1  |[Zipcode -> 704, ZipCodeType -> STANDARD, City -> PARC PARQUE, State -> PR]|
//+---+---------------------------------------------------------------------------+

2.2. to_json()

to_json() function is used to convert DataFrame columns MapType or Struct type to JSON string. Here, I am using df2 that created from above from_json() example.

from pyspark.sql.functions import to_json,col
df2.withColumn("value",to_json(col("value"))) \
   .show(truncate=False)
#Output
//+---+----------------------------------------------------------------------------+
//|id |value                                                                       |
//+---+----------------------------------------------------------------------------+
//|1  |{"Zipcode":"704","ZipCodeType":"STANDARD","City":"PARC PARQUE","State":"PR"}|
//+---+----------------------------------------------------------------------------+

2.3. json_tuple()

Function json_tuple() is used the query or extract the elements from JSON column and create the result as a new columns.

from pyspark.sql.functions import json_tuple
df.select(col("id"),json_tuple(col("value"),"Zipcode","ZipCodeType","City")) \
    .toDF("id","Zipcode","ZipCodeType","City") \
    .show(truncate=False)
#Output
//+---+-------+-----------+-----------+
//|id |Zipcode|ZipCodeType|City       |
//+---+-------+-----------+-----------+
//|1  |704    |STANDARD   |PARC PARQUE|
//+---+-------+-----------+-----------+

2.4. get_json_object()

get_json_object() is used to extract the JSON string based on path from the JSON column.

from pyspark.sql.functions import get_json_object
df.select(col("id"),get_json_object(col("value"),"$.ZipCodeType").alias("ZipCodeType")) \
    .show(truncate=False)
#Output
//+---+-----------+
//|id |ZipCodeType|
//+---+-----------+
//|1  |STANDARD   |
//+---+-----------+

2.5. schema_of_json()

Use schema_of_json() to create schema string from JSON string column.

from pyspark.sql.functions import schema_of_json,lit
schemaStr=spark.range(1) \
    .select(schema_of_json(lit("""{"Zipcode":704,"ZipCodeType":"STANDARD","City":"PARC PARQUE","State":"PR"}"""))) \
    .collect()[0][0]
print(schemaStr)

//struct<City:string,State:string,ZipCodeType:string,Zipcode:bigint>

3. Complete Example

from pyspark.sql import SparkSession,Row
spark = SparkSession.builder.appName('mytechmint').getOrCreate()

jsonString="""{"Zipcode":704,"ZipCodeType":"STANDARD","City":"PARC PARQUE","State":"PR"}"""
df=spark.createDataFrame([(1, jsonString)],["id","value"])
df.show(truncate=False)

#Convert JSON string column to Map type
from pyspark.sql.types import MapType,StringType
from pyspark.sql.functions import from_json
df2=df.withColumn("value",from_json(df.value,MapType(StringType(),StringType())))
df2.printSchema()
df2.show(truncate=False)

from pyspark.sql.functions import to_json,col
df2.withColumn("value",to_json(col("value"))) \
   .show(truncate=False)

from pyspark.sql.functions import json_tuple
df.select(col("id"),json_tuple(col("value"),"Zipcode","ZipCodeType","City")) \
    .toDF("id","Zipcode","ZipCodeType","City") \
    .show(truncate=False)

from pyspark.sql.functions import get_json_object
df.select(col("id"),get_json_object(col("value"),"$.ZipCodeType").alias("ZipCodeType")) \
    .show(truncate=False)

from pyspark.sql.functions import schema_of_json,lit
schemaStr=spark.range(1) \
    .select(schema_of_json(lit("""{"Zipcode":704,"ZipCodeType":"STANDARD","City":"PARC PARQUE","State":"PR"}"""))) \
    .collect()[0][0]
print(schemaStr)

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