CODEWITHSUNDEEP
pythondataframeapache-sparkpyspark
DBA108642
DBA108642

Reputation: 2112

Pyspark merge multiple columns into a json column

I asked the question a while back for python, but now I need to do the same thing in PySpark.

I have a dataframe (df) like so:

|cust_id|address    |store_id|email        |sales_channel|category|
-------------------------------------------------------------------
|1234567|123 Main St|10SjtT  |[email protected]|ecom         |direct  |
|4567345|345 Main St|10SjtT  |[email protected]|instore      |direct  |
|1569457|876 Main St|51FstT  |[email protected]|ecom         |direct  |

and I would like to combine the last 4 fields into one metadata field that is a json like so:

|cust_id|address    |metadata                                                                                     |
-------------------------------------------------------------------------------------------------------------------
|1234567|123 Main St|{'store_id':'10SjtT', 'email':'[email protected]','sales_channel':'ecom', 'category':'direct'}   |
|4567345|345 Main St|{'store_id':'10SjtT', 'email':'[email protected]','sales_channel':'instore', 'category':'direct'}|
|1569457|876 Main St|{'store_id':'51FstT', 'email':'[email protected]','sales_channel':'ecom', 'category':'direct'}   |

Here's the code I used to do this in python:

cols = [
    'store_id',
    'store_category',
    'sales_channel',
    'email'
]

df1 = df.copy()
df1['metadata'] = df1[cols].to_dict(orient='records')
df1 = df1.drop(columns=cols)

but I would like to translate this to PySpark code to work with a spark dataframe; I do NOT want to use pandas in Spark.

Upvotes: 12

Views: 16320

Answers (2)

Brian Wylie
Brian Wylie

Reputation: 2630

@Shu gives a good answer, here's a variant that works out slightly better for my use case. I'm going from Kafka -> Spark -> Kafka and this one liner does exactly what I want. The struct(*) will pack up all the fields in the dataframe.

# Packup the fields in preparation for sending to Kafka sink
kafka_df = df.selectExpr('cast(id as string) as key', 'to_json(struct(*)) as value')

Upvotes: 3

notNull
notNull

Reputation: 31470

Use to_json function to create json object!

Example:

from pyspark.sql.functions import *

#sample data
df=spark.createDataFrame([('1234567','123 Main St','10SjtT','[email protected]','ecom','direct')],['cust_id','address','store_id','email','sales_channel','category'])

df.select("cust_id","address",to_json(struct("store_id","category","sales_channel","email")).alias("metadata")).show(10,False)

#result
+-------+-----------+----------------------------------------------------------------------------------------+
|cust_id|address    |metadata                                                                                |
+-------+-----------+----------------------------------------------------------------------------------------+
|1234567|123 Main St|{"store_id":"10SjtT","category":"direct","sales_channel":"ecom","email":"[email protected]"}|
+-------+-----------+----------------------------------------------------------------------------------------+

to_json by passing list of columns:

ll=['store_id','email','sales_channel','category']

df.withColumn("metadata", to_json(struct([x for x in ll]))).drop(*ll).show()

#result
+-------+-----------+----------------------------------------------------------------------------------------+
|cust_id|address    |metadata                                                                                |
+-------+-----------+----------------------------------------------------------------------------------------+
|1234567|123 Main St|{"store_id":"10SjtT","email":"[email protected]","sales_channel":"ecom","category":"direct"}|
+-------+-----------+----------------------------------------------------------------------------------------+

Upvotes: 32

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