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import csv |
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from BasicNeeds import BasicNeeds as cls_bn |
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from TypeDetermination import TypeDetermination |
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from datetime import datetime,time |
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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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escape_name, escape_string_literal, \ |
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TableName, \ |
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HyperException, \ |
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Timestamp |
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class TableauHyperApiExtraLogic: |
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def fn_build_hyper_columns_for_csv(given_file_name, csv_field_separator, detected_csv_structure, verbose): |
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list_hyper_table_columns_to_return = [] |
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for current_field_structure in detected_csv_structure: |
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list_hyper_table_columns_to_return.append(current_field_structure['order']) |
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current_column_type = TableauHyperApiExtraLogic.fn_convert_to_hyper_types(current_field_structure['type']) |
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cls_bn.fn_optional_print(cls_bn, verbose, 'Column ' |
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+ 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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list_hyper_table_columns_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=NULLABLE |
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) |
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return list_hyper_table_columns_to_return |
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def fn_convert_and_validate_content(self, crt_value, crt_type): |
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if crt_value == '': |
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return None |
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else: |
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if crt_type == 'int': |
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return int(crt_value) |
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elif crt_type == 'float-USA': |
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return float(crt_value) |
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elif crt_type == 'date-iso8601': |
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tm = datetime.strptime(crt_value, '%Y-%m-%d') |
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return datetime(tm.year, tm.month, tm.day) |
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elif crt_type == 'date-USA': |
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tm = datetime.strptime(crt_value, '%m/%d/%Y') |
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return datetime(tm.year, tm.month, tm.day) |
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elif crt_type == 'time-24': |
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tm = datetime.strptime(crt_value, '%H:%M:%S') |
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return time(tm.hour, tm.minute, tm.second) |
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elif crt_type == 'time-24-us': |
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tm = datetime.strptime(crt_value, '%H:%M:%S.%f') |
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return time(tm.hour, tm.minute, tm.second, tm.microsecond) |
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elif crt_type == 'time-USA': |
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tm = datetime.strptime(crt_value, '%I:%M:%S') |
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return time(tm.hour, tm.minute, tm.second) |
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elif crt_type == 'datetime-iso8601': |
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tm = datetime.fromisoformat(crt_value) |
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return Timestamp(tm.year, tm.month, tm.day, tm.hour, tm.minute, tm.second) |
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elif crt_type == 'datetime-iso8601-us': |
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tm = datetime.fromisoformat(crt_value) |
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return Timestamp(tm.year, tm.month, tm.day, tm.hour, tm.minute, tm.second, tm.microsecond) |
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else: |
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return crt_value.replace('"', '\\"') |
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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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'int': SqlType.big_int(), |
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'float-USA': SqlType.double(), |
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'date-iso8601': SqlType.date(), |
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'date-USA': SqlType.date(), |
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'time-24': SqlType.time(), |
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'time-24-us': SqlType.time(), |
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'time-USA': SqlType.time(), |
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'datetime-iso8601': 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_rebuild_csv_content_for_hyper(given_file_name, csv_field_separator, detected_fields_type, verbose): |
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csv_content_for_hyper = [] |
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with open(given_file_name, newline='') as csv_file: |
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csv_object = csv.DictReader(csv_file, delimiter=csv_field_separator) |
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# parse rows with index |
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for row_idx, row_content in enumerate(csv_object): |
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csv_content_for_hyper.append(row_idx) |
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csv_content_for_hyper[row_idx] = [] |
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print_prefix = 'On the row ' + str((row_idx + 1)) |
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# parse all columns with index |
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for col_idx, column_name in enumerate(csv_object.fieldnames): |
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csv_content_for_hyper[row_idx].append(col_idx) |
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csv_content_for_hyper[row_idx][col_idx] = \ |
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self.fn_convert_and_validate_content(self, |
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row_content[csv_object.fieldnames[col_idx]], |
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detected_fields_type[col_idx]['type']) |
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cls_bn.fn_optional_print(cls_bn, verbose, print_prefix + ' column ' + str(col_idx) |
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+ ' having the name [' + csv_object.fieldnames[col_idx] + '] ' |
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+ ' has the value <' + row_content[csv_object.fieldnames[col_idx]] |
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+ '> which was interpreted as <<' |
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+ str(csv_content_for_hyper[row_idx][col_idx]) |
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+ '>>') |
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return csv_content_for_hyper |
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def fn_run_create_hyper_file_from_csv(input_csv_file, |
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csv_field_separator, |
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output_hyper_file, |
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verbose): |
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detected_csv_structure = TypeDetermination.fn_detect_csv_structure(TypeDetermination, |
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input_csv_file, |
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csv_field_separator, |
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verbose) |
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hyper_table_columns = TableauHyperApiExtraLogic.fn_build_hyper_columns_for_csv(input_csv_file, |
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csv_field_separator, |
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detected_csv_structure, |
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verbose) |
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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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with Connection(endpoint=hyper.endpoint, |
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database=output_hyper_file, |
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create_mode=CreateMode.CREATE_AND_REPLACE) as hyper_connection: |
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hyper_connection.catalog.create_schema("Extract") |
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hyper_table = TableDefinition( |
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name=TableName("Extract", "Extract"), |
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columns=hyper_table_columns |
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) |
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hyper_connection.catalog.create_table(table_definition=hyper_table) |
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print("The connection to the Hyper engine file has been created.") |
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''' |
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VERDICT: does not work as DOUBLE or INT are not accepting empty values... :-( |
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print("I am about to execute command: " |
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+ f"COPY {hyper_table.table_name} from {escape_string_literal(input_csv_file)} with " |
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f"(format csv, NULL 'NULL', delimiter '{csv_field_separator}', header)") |
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# Load all rows into "Customers" table from the CSV file. |
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# `execute_command` executes a SQL statement and returns the impacted row count. |
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count_in_target_table = hyper_connection.execute_command( |
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command=f"COPY {hyper_table.table_name} from {escape_string_literal(input_csv_file)} with " |
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f"(format csv, NULL 'NULL', delimiter '{csv_field_separator}', header)") |
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print(f"The number of rows in table {hyper_table.table_name} is {count_in_target_table}.") |
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''' |
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# The rows to insert into the <hyper_table> table. |
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data_to_insert = TableauHyperApiExtraLogic.fn_rebuild_csv_content_for_hyper(input_csv_file, |
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csv_field_separator, |
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detected_csv_structure, |
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verbose) |
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# Execute the actual insert |
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with Inserter(hyper_connection, hyper_table) as hyper_inserter: |
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hyper_inserter.add_rows(rows=data_to_insert) |
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hyper_inserter.execute() |
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# Number of rows in the <hyper_table> table. |
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# `execute_scalar_query` is for executing a query that returns exactly one row with one column. |
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row_count = hyper_connection.\ |
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execute_scalar_query(query=f"SELECT COUNT(*) FROM {hyper_table.table_name}") |
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print(f"The number of rows in table {hyper_table.table_name} is {row_count}.") |
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print("The connection to the Hyper file has been closed.") |
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print("The Hyper process has been shut down.") |
danielgp /
tableau-hyper-management
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