def test__unflatten_one_column(self): columns = [{ 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE' }] expected = { 'test_schema': { 'test_table': { 'metadata': { 'comment': None, 'name': 'test_table', 'type': 'BASE TABLE', 'schema': 'test_schema', }, 'columns': [ { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' } ] } } } result = generate.unflatten(columns) self.assertEqual(result, expected)
def test__unflatten_one_column(self): columns = [{ 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE' }] expected = { 'test_schema': { 'test_table': { 'metadata': { 'comment': None, 'name': 'test_table', 'type': 'BASE TABLE', 'schema': 'test_schema', }, 'columns': { 'id': { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' }, }, 'stats': { 'has_stats': { 'id': 'has_stats', 'label': 'Has Stats?', 'value': False, 'description': 'Indicates whether there are statistics for this table', 'include': False, }, }, } } } result = generate.unflatten(columns) self.assertEqual(result, expected)
def unflatten(columns): """Given a list of column dictionaries following this layout: [{ 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE' }] unflatten will convert them into a dict with this nested structure: { 'test_schema': { 'test_table': { 'metadata': { 'comment': None, 'name': 'test_table', 'type': 'BASE TABLE', 'schema': 'test_schema', }, 'columns': { "id": { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' } } } } } Required keys in each column: table_schema, table_name, column_index Keys prefixed with 'column_' end up in per-column data and keys prefixed with 'table_' end up in table metadata. Keys without either prefix are ignored. """ structured = {} for entry in columns: schema_name = entry['table_schema'] table_name = entry['table_name'] if schema_name not in structured: structured[schema_name] = {} schema = structured[schema_name] if table_name not in schema: metadata = get_stripped_prefix(entry, 'table_') stats = get_stripped_prefix(entry, 'stats:') stats_dict = format_stats(stats) schema[table_name] = { 'metadata': metadata, 'stats': stats_dict, 'columns': {} } table = schema[table_name] column = get_stripped_prefix(entry, 'column_') # the index should really never be that big so it's ok to end up # serializing this to JSON (2^53 is the max safe value there) column['index'] = bigint(column['index']) table['columns'][column['name']] = column return structured
def unflatten(columns): """Given a list of column dictionaries following this layout: [{ 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE' }] unflatten will convert them into a dict with this nested structure: { 'test_schema': { 'test_table': { 'metadata': { 'comment': None, 'name': 'test_table', 'type': 'BASE TABLE', 'schema': 'test_schema', }, 'columns': { "id": { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' } } } } } Required keys in each column: table_schema, table_name, column_index Keys prefixed with 'column_' end up in per-column data and keys prefixed with 'table_' end up in table metadata. Keys without either prefix are ignored. """ structured = {} for entry in columns: schema_name = entry['table_schema'] table_name = entry['table_name'] if schema_name not in structured: structured[schema_name] = {} schema = structured[schema_name] if table_name not in schema: metadata = get_stripped_prefix(entry, 'table_') stats = get_stripped_prefix(entry, 'stats:') stats_dict = format_stats(stats) schema[table_name] = { 'metadata': metadata, 'stats': stats_dict, 'columns': {} } table = schema[table_name] column = get_stripped_prefix(entry, 'column_') # the index should really never be that big so it's ok to end up # serializing this to JSON (2^53 is the max safe value there) column['index'] = bigint(column['index']) table['columns'][column['name']] = column return structured
def test__unflatten_multiple_schemas(self): columns = [ { 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE' }, { 'column_comment': None, 'column_index': Decimal('2'), 'column_name': 'name', 'column_type': 'text', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE' }, { 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'other_test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE', }, { 'column_comment': None, 'column_index': Decimal('2'), 'column_name': 'email', 'column_type': 'character varying', 'table_comment': None, 'table_name': 'other_test_table', 'table_schema': 'test_schema', 'table_type': 'BASE TABLE', }, { 'column_comment': None, 'column_index': Decimal('1'), 'column_name': 'id', 'column_type': 'integer', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'other_test_schema', 'table_type': 'BASE TABLE' }, { 'column_comment': None, 'column_index': Decimal('2'), 'column_name': 'name', 'column_type': 'text', 'table_comment': None, 'table_name': 'test_table', 'table_schema': 'other_test_schema', 'table_type': 'BASE TABLE' }, ] expected = { 'test_schema': { 'test_table': { 'metadata': { 'comment': None, 'name': 'test_table', 'type': 'BASE TABLE', 'schema': 'test_schema', }, 'columns': [ { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' }, { 'type': 'text', 'comment': None, 'index': Decimal('2'), 'name': 'name', } ], }, 'other_test_table': { 'metadata': { 'comment': None, 'name': 'other_test_table', 'type': 'BASE TABLE', 'schema': 'test_schema', }, 'columns': [ { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' }, { 'type': 'character varying', 'comment': None, 'index': Decimal('2'), 'name': 'email', } ] } }, 'other_test_schema': { 'test_table': { 'metadata': { 'comment': None, 'name': 'test_table', 'type': 'BASE TABLE', 'schema': 'other_test_schema', }, 'columns': [ { 'type': 'integer', 'comment': None, 'index': bigint(1), 'name': 'id' }, { 'type': 'text', 'comment': None, 'index': Decimal('2'), 'name': 'name', } ], }, } } result = generate.unflatten(columns) self.assertEqual(result, expected)