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""" |
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TableauHyperApiExtraLogic - a Hyper client library. |
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This library allows packaging CSV content into HYPER format with data type checks |
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""" |
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# package regular expression |
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import re |
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# package to handle numerical structures |
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import numpy |
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# package to handle Data Frames (in this file) |
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import pandas as pd |
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# Custom classes from Tableau Hyper package |
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from tableauhyperapi import HyperProcess, Telemetry, \ |
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Connection, CreateMode, \ |
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NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \ |
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Inserter, \ |
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TableName, \ |
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HyperException |
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class TableauHyperApiExtraLogic: |
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def fn_build_hyper_columns_for_csv(self, logger, timmer, detected_csv_structure): |
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timmer.start() |
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list_to_return = [] |
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for current_field_structure in detected_csv_structure: |
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list_to_return.append(current_field_structure['order']) |
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current_column_type = self.fn_convert_to_hyper_types(current_field_structure['type']) |
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logger.debug('Column ' + str(current_field_structure['order']) + ' having name "' |
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+ current_field_structure['name'] + '" and type "' |
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+ current_field_structure['type'] + '" will become "' |
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+ str(current_column_type) + '"') |
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nullability_value = NULLABLE |
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if current_field_structure['nulls'] == 0: |
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nullability_value = NOT_NULLABLE |
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list_to_return[current_field_structure['order']] = TableDefinition.Column( |
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name=current_field_structure['name'], |
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type=current_column_type, |
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nullability=nullability_value |
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) |
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logger.info('Building Hyper columns completed') |
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timmer.stop() |
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return list_to_return |
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@staticmethod |
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def fn_convert_to_hyper_types(given_type): |
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switcher = { |
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'empty': SqlType.text(), |
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'bool': SqlType.bool(), |
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'int': SqlType.big_int(), |
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'float-dot': SqlType.double(), |
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'date-YMD': SqlType.date(), |
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'date-MDY': SqlType.date(), |
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'date-DMY': SqlType.date(), |
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'time-24': SqlType.time(), |
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'time-12': SqlType.time(), |
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'datetime-24-YMD': SqlType.timestamp(), |
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'datetime-12-MDY': SqlType.timestamp(), |
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'datetime-24-DMY': SqlType.timestamp(), |
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'str': SqlType.text() |
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} |
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identified_type = switcher.get(given_type) |
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if identified_type is None: |
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identified_type = SqlType.text() |
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return identified_type |
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def fn_create_hyper_file_from_csv(self, local_logger, timmer, input_csv_data_frame, |
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in_data_type, given_parameters): |
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hyper_cols = self.fn_build_hyper_columns_for_csv(local_logger, timmer, in_data_type) |
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# The rows to insert into the <hyper_table> table. |
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data_to_insert = self.fn_rebuild_csv_content_for_hyper(local_logger, timmer, |
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input_csv_data_frame, |
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in_data_type) |
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# Starts the Hyper Process with telemetry enabled/disabled to send data to Tableau or not |
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# To opt in, simply set telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU. |
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# To opt out, simply set telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU. |
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with HyperProcess(telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU) as hyper: |
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# Creates new Hyper file <output_hyper_file> |
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# Replaces file with CreateMode.CREATE_AND_REPLACE if it already exists. |
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timmer.start() |
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with Connection(endpoint=hyper.endpoint, |
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database=given_parameters.output_file, |
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create_mode=CreateMode.CREATE_AND_REPLACE) as hyper_connection: |
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local_logger.info('Connection to the Hyper engine ' |
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+ f'file "{given_parameters.output_file}" has been created.') |
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timmer.stop() |
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timmer.start() |
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hyper_connection.catalog.create_schema("Extract") |
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local_logger.info('Hyper schema "Extract" has been created.') |
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hyper_table = TableDefinition( |
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TableName("Extract", "Extract"), |
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columns=hyper_cols |
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) |
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hyper_connection.catalog.create_table(table_definition=hyper_table) |
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local_logger.info('Hyper table "Extract" has been created.') |
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timmer.stop() |
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timmer.start() |
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# Execute the actual insert |
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with Inserter(hyper_connection, hyper_table) as hyper_insert: |
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hyper_insert.add_rows(rows=data_to_insert) |
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hyper_insert.execute() |
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local_logger.info('Data has been inserted into Hyper table') |
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timmer.stop() |
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timmer.start() |
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# Number of rows in the <hyper_table> table. |
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# `execute_scalar_query` is for executing a query |
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# that returns exactly one row with one column. |
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query_to_run = f'SELECT COUNT(*) FROM {hyper_table.table_name}' |
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row_count = hyper_connection.execute_scalar_query(query=query_to_run) |
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local_logger.info(f'Table {hyper_table.table_name} has {row_count} rows') |
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timmer.stop() |
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local_logger.info('Connection to the Hyper engine file has been closed') |
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local_logger.info('Hyper engine process has been shut down') |
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def fn_rebuild_csv_content_for_hyper(self, logger, timmer, input_df, detected_fields_type): |
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timmer.start() |
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input_df.replace(to_replace=[numpy.nan], value=[None], inplace=True) |
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# Cycle through all found columns |
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for current_field in detected_fields_type: |
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fld_nm = current_field['name'] |
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logger.debug(f'Column {fld_nm} has panda_type = ' + str(current_field['panda_type']) |
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+ ' and python type = ' + str(current_field['type'])) |
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input_df[fld_nm] = self.reevaluate_single_column(input_df, fld_nm, current_field) |
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logger.info('Re-building CSV content for maximum Hyper compatibility has been completed') |
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timmer.stop() |
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return input_df.values |
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def reevaluate_single_column(self, given_df, given_field_name, current_field_details): |
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if current_field_details['type'] == 'str': |
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given_df[given_field_name] = given_df[given_field_name].astype(str) |
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elif current_field_details['panda_type'] == 'float64' \ |
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and current_field_details['type'] == 'int': |
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given_df[given_field_name] = given_df[given_field_name].fillna(0).astype('int64') |
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elif current_field_details['type'][0:5] in ('date-', 'datet', 'time-'): |
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given_df[given_field_name] = self.fn_string_to_date(given_field_name, given_df) |
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return given_df[given_field_name] |
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def fn_run_hyper_creation(self, local_logger, timmer, input_data_frame, input_data_type, |
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given_parameters): |
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try: |
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self.fn_create_hyper_file_from_csv(local_logger, timmer, input_data_frame, |
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input_data_type, given_parameters) |
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except HyperException as ex: |
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local_logger.error(str(ex).replace(chr(10), ' ')) |
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exit(1) |
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@staticmethod |
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def fn_string_to_date(in_col_name, in_data_frame): |
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if re.match('-YMD', in_col_name): |
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in_data_frame[in_col_name] = pd.to_datetime(in_data_frame[in_col_name], yearfirst=True) |
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elif re.match('-DMY', in_col_name): |
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in_data_frame[in_col_name] = pd.to_datetime(in_data_frame[in_col_name], dayfirst=True) |
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else: |
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in_data_frame[in_col_name] = pd.to_datetime(in_data_frame[in_col_name]) |
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return in_data_frame[in_col_name] |
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danielgp /
tableau-hyper-management
