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": "someGuid",
        "data": {
            "id": "someGuid",
            "data": {
                "players": {
                    "player_1": {
                        "id": "player_1",
                        "locationId": "someGuid",
                        "name": "someName",
                        "assets": {
                            "assetId1": {
                                "isActive": true,
                                "playlists": {
                                    "someId1": true,
                                    "someOtherId1": false
                                }
                            },
                            "assetId2": {
                                "isActive": true,
                                "playlists": {
                                    "someId1": true
                                }
                            }
                        }
                    },
                    "player_2": {
                        "id": "player_2",
                        "locationId": "someGuid",
                        "name": "someName",
                        "dict": {
                            "assetId3": {
                                "isActive": true,
                                "playlists": {
                                    "someId1": true,
                                    "someOtherId1": false
                                }
                            },
                            "assetId4": {
                                "isActive": true,
                                "playlists": {
                                    "someId1": true
                                }
                            }
                        }
                    }
                }
            },
            "lastRefreshed": "2020-01-23T19:29:15.6354794Z",
            "expiresAt": "9999-12-31T23:59:59.9999999",
            "dataSourceId": "someId"
        }
    }

I'm having difficulty trying to figure out a way using python or sql in pyspark on Azure Databricks to turn this json into a tabular format like this:

+===========+=============+===============+===========+==============+=============+=================+
| Location  | Player_ID   |    Player     | Asset_ID  | Asset_Active | Playlist_ID | Playlist_Status |
+===========+=============+===============+===========+==============+=============+=================+
|  someId   | player_1    | ThisIsAPlayer | anotherId | TRUE         | someOtherId | FALSE           |
+-----------+-------------+---------------+-----------+--------------+-------------+-----------------+

The challenge is transforming the players property above to multiple rows per location. A location may have any number of players of varying ids. I perhaps would not be asking this question if the property players was an array of player objects instead of a dictionary, but i have no control over the structure of this document, so this is what I must work with. This is a non-issue in something like PowerBI, where the manipulation of the data is more straight forward.

The farthest I've been able to get is doing something like this:

df = spark.read.json(filePath).select("data.id", "data.lastRefreshed", "data.expiresAt","data.dataSourceId","data.data.players.*")

But this results in a dataframe/table that expands all the nested structs undernearth players to columns. I've scoured SO looking for someone with a similar situation, but no luck.

How do I go about exploding/expanding the players column in this dataframe to separate rows?

In pyspark, I'm dealing with Spark 2.4.3

take a look at this stackoverflow.com/questions/60646254/… — murtihash
– murtihash, Commented Mar 12, 2020 at 17:38

jxc · Accepted Answer · 2020-03-13 15:30:08Z

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|
+--------+---------+--------+--------+--------+------------+---------------+

This was incredibly useful, and did solve my immediate problem. I don't know if I would have ever come up with a solution like this. How did you arrive at this answer?

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