how to store grouped data into json in pyspark - dataframe

Question

I am new to pyspark
I have a dataset which looks like (just a snapshot of few columns)
I want to group my data by key. My key is
CONCAT(a.div_nbr,a.cust_nbr)
My ultimate goal is to convert the data into JSON, formated like this
k1[{v1,v2,....},{v1,v2,....}], k2[{v1,v2,....},{v1,v2,....}],....
e.g
248138339 [{ PRECIMA_ID:SCP 00248 0000138339, PROD_NBR:5553505, PROD_DESC:Shot and a Beer Battered Onion Rings (5553505 and 9285840) , PROD_BRND:Molly's Kitchen,PACK_SIZE:4/2.5 LB, QTY_UOM:CA } ,
{ PRECIMA_ID:SCP 00248 0000138339 , PROD_NBR:6659079 , PROD_DESC:Beef Chuck Short Rib Slices, PROD_BRND:Stockyards , PACK_SIZE:12 LBA , QTY_UOM:CA} ,{...,...,} ],
1384611034793[{},{},{}],....
I have created a dataframe (I am joining two tables basically to get some more fields)
joinstmt = sqlContext.sql(
"SELECT a.precima_id , CONCAT(a.div_nbr,a.cust_nbr) as
key,a.prod_nbr , a.prod_desc,a.prod_brnd , a.pack_size , a.qty_uom , a.sales_opp , a.prc_guidance , a.pim_mrch_ctgry_desc , a.pim_mrch_ctgry_id , b.start_date,b.end_date
FROM scoop_dtl a join scoop_hdr b on (a.precima_id =b.precima_id)")
Now, in order to get the above result I need to group by the result based on key, I did the following
groupbydf = joinstmt.groupBy("key")
This resulted intp a grouped data and after reading I got to know that I cannot use it directly and I need to convert it back into dataframes to store it.
I am new to it, need some help inorder to convert it back into dataframes or I would appreciate if there are any other ways as well.

Answer 1

If your joined dataframe looks like this:
gender age
M 5
F 50
M 10
M 10
F 10
You can then use below code to get desired output
joinedDF.groupBy("gender") \
.agg(collect_list("age").alias("ages")) \
.write.json("jsonOutput.txt")
Output would look like below:
{"gender":"F","ages":[50,10]}
{"gender":"M","ages":[5,10,10]}
In case you have multiple columns like name, salary. You can add columns like below:
df.groupBy("gender")
.agg(collect_list("age").alias("ages"),collect_list("name").alias("names"))
Your output would look like:
{"gender":"F","ages":[50,10],"names":["ankit","abhay"]}
{"gender":"M","ages":[5,10,10],"names":["snchit","mohit","rohit"]}

Answer 2

You cannot use GroupedData directly. It has to be aggregated first. It could be partially covered by aggregation with built-in functions like collect_list but it is simply not possible to achieve desired output, with values used to represent keys, using DataFrameWriter.
In can try something like this instead:
from pyspark.sql import Row
import json
def make_json(kvs):
k, vs = kvs
return json.dumps({k[0]: list(vs)})
(df.select(struct(*keys), values)
.rdd
.mapValues(Row.asDict)
.groupByKey()
.map(make_json))
and saveAsTextFile.