Can I transform a complex json object to multiple rows in a dataframe in Azure Databricks using pyspark?

Question

I have some json that\'s being read from a file where each row looks something like this:

    {
        \"id\": \"someGu         


        

Answer 1

You can try from_json function to convert the column/field from StructType into MapType, explode and then find your desired fields. for you example JSON, you will need to do this several times:

from pyspark.sql.functions import explode, from_json, to_json, json_tuple, coalesce

df.select(explode(from_json(to_json('data.data.players'),"map<string,string>"))) \
  .select(json_tuple('value', 'locationId', 'id', 'name', 'assets', 'dict').alias('Location', 'Player_ID', 'Player', 'assets', 'dict')) \
  .select('*', explode(from_json(coalesce('assets','dict'),"map<string,struct<isActive:boolean,playlists:string>>"))) \
  .selectExpr(
    'Location',
    'Player_ID',
    'Player', 
    'key as Asset_ID',
    'value.isActive',  
    'explode(from_json(value.playlists, "map<string,string>")) as (Playlist_ID, Playlist_Status)'
  ) \
.show()
+--------+---------+--------+--------+--------+------------+---------------+
|Location|Player_ID|  Player|Asset_ID|isActive| Playlist_ID|Playlist_Status|
+--------+---------+--------+--------+--------+------------+---------------+
|someGuid| player_1|someName|assetId1|    true|     someId1|           true|
|someGuid| player_1|someName|assetId1|    true|someOtherId1|          false|
|someGuid| player_1|someName|assetId2|    true|     someId1|           true|
|someGuid| player_2|someName|assetId3|    true|     someId1|           true|
|someGuid| player_2|someName|assetId3|    true|someOtherId1|          false|
|someGuid| player_2|someName|assetId4|    true|     someId1|           true|
+--------+---------+--------+--------+--------+------------+---------------+