PySpark how to parse nested json

Question

I have a json file with the following schema:

root
 |-- count: long (nullable = true)
 |-- results: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- address: string (nullable = true)
 |    |    |-- auto_task_assignment: boolean (nullable = true)
 |    |    |-- deleted_at: string (nullable = true)
 |    |    |-- has_issues: boolean (nullable = true)
 |    |    |-- has_timetable: boolean (nullable = true)
 |    |    |-- id: long (nullable = true)
 |    |    |-- name: string (nullable = true)
 |    |    |-- opening_hours: string (nullable = true)
 |    |    |-- phone_number: string (nullable = true)
 |    |    |-- position_id: long (nullable = true)
 |    |    |-- show_technical_time: boolean (nullable = true)
 |    |    |-- structure_id: long (nullable = true)
 |    |    |-- subcontract_number: string (nullable = true)
 |    |    |-- task_modification: boolean (nullable = true)
 |    |    |-- updated_at: string (nullable = true)

I want to parse results array to obtain DataFrame with all columns listed in schema When trying to use select statement, I'm given an error. df.select("results.*").show() error message: AnalysisException: Can only star expand struct data types. Attribute: `ArrayBuffer(results)` Could you please help me how to filter this json?

sample data:

{'count': 11, 'next': None, 'previous': None, 'results': [{'id': 1, 'name': 'Samodzielny Publiczny Szpital Kliniczny Nr 1 PUM', 'external_id': None, 'structure_id': 1, 'address': '71-252 Szczecin, Ul. Unii Lubelskiej 1 ', 'phone_number': '+48123456789', 'opening_hours': 'pn-pt: 9:00-17:00', 'deleted_at': '2021-05-27T13:02:12.026410+02:00', 'updated_at': '2021-05-27T13:02:12.026417+02:00', 'position_id': None, 'has_timetable': True, 'auto_task_assignment': True, 'task_modification': False, 'has_issues': False, 'show_technical_time': False, 'subcontract_number': None}, {'id': 2, 'name': 'Szpital polowy we wrocławiu', 'external_id': None, 'structure_id': 2, 'address': 'North Montytown, 0861 Greenholt Crescent', 'phone_number': '+48505505505', 'opening_hours': '', 'deleted_at': None, 'updated_at': '2021-11-18T16:15:06.608476+01:00', 'position_id': 49, 'has_timetable': True, 'auto_task_assignment': False, 'task_modification': True, 'has_issues': True, 'show_technical_time': True, 'subcontract_number': '191919919; 191919191991; 19991919919; 1919919 191919919; 191919191991; 19991919919; 1919919....191919919; 191919191991; 19991919919; 1919919 191919919; 191919191991; 19991919919; 1919919191919919; 191919191991; 19991919919; 1919919 191919919; 1919191-255c'}, {'id': 3, 'name': 'Test', 'external_id': None, 'structure_id': 17, 'address': 'ul. Śliczna', 'phone_number': '+48500100107', 'opening_hours': '', 'deleted_at': None, 'updated_at': '2021-11-04T14:22:04.712607+01:00', 'position_id': 33, 'has_timetable': True, 'auto_task_assignment': True, 'task_modification': True, 'has_issues': True, 'show_technical_time': True, 'subcontract_number': '07001234'}]}

I have found a workaround using Pandas DataFrame, but my aim is to do it using Spark

enum = 0
for i in df['results']:
    if enum == 0 :
        df2 = pd.DataFrame(i, index=[0])
        enum=+1
    else:
        df2 = df2.append(i, ignore_index=True)

Expected output is to keep column count that will repeat the same value on each row and extract all columns from results struct, expected schema below:

root
 |-- count: long (nullable = true)
 |-- address: string (nullable = true)
 |-- auto_task_assignment: boolean (nullable = true)
 |-- deleted_at: string (nullable = true)
 |-- has_issues: boolean (nullable = true)
 |-- has_timetable: boolean (nullable = true)
 |-- id: long (nullable = true)
 |-- name: string (nullable = true)
 |-- opening_hours: string (nullable = true)
 |-- phone_number: string (nullable = true)
 |-- position_id: long (nullable = true)
 |-- show_technical_time: boolean (nullable = true)
 |-- structure_id: long (nullable = true)
 |-- subcontract_number: string (nullable = true)
 |-- task_modification: boolean (nullable = true)
 |-- updated_at: string (nullable = true)

Nithish · Accepted Answer · 2021-11-23 21:40:16Z

3

You would need to explode the results array before un-nesting struct fields.

df.withColumn("results", F.explode(F.col("results"))).select("results.*").show()

answered Nov 23, 2021 at 21:40

Nithish

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