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) 
