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1
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"""
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2
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CommandLineArgumentManagement - library to manage input parameters from command line
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3
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4
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This library allows handling pre-configured arguments to be received from command line and use them
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5
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to call the main package functions
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6
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"""
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7
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# package helping out to work with SAP HANA
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from hdbcli import dbapi
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9
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# package helping out to work with Oracle MySQL
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import mysql.connector
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11
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# package to facilitate time operations
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12
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from datetime import datetime, timedelta
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13
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# package facilitating Data Frames manipulation
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14
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import pandas as pd
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15
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16
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17
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class DatabaseTalker:
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conn = None
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19
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20
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@staticmethod
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21
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def append_additional_columns_to_data_frame(local_logger, timered, data_frame, session_details):
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22
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if 'additional-columns' in session_details:
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timered.start()
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for crt_column in session_details['additional-columns']:
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25
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if crt_column['value'] == 'utcnow': # special case
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data_frame[crt_column['name']] = datetime.utcnow()
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else:
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data_frame[crt_column['name']] = crt_column['value']
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local_logger.info('Additional column(s) have been added to Pandas DataFrame')
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timered.stop()
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31
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32
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def connect_to_database(self, local_logger, timered, connection_details):
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timered.start()
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local_logger.info('I will attempt to connect to '
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35
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+ connection_details['server-vendor-and-type'] + ' server, layer '
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+ connection_details['server-layer'] + ' which means ('
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+ 'server ' + connection_details['ServerName']
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+ ', port ' + str(connection_details['ServerPort'])
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39
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+ ') using the username ' + connection_details['Username']
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40
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+ ' (' + connection_details['Name'] + ')')
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41
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try:
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# create actual connection
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if connection_details['server-vendor-and-type'] == 'SAP HANA':
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self.conn = dbapi.connect(
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address=connection_details['ServerName'],
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port=connection_details['ServerPort'],
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user=connection_details['Username'],
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password=connection_details['Password'],
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49
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)
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50
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elif connection_details['server-vendor-and-type'] == 'Oracle MySQL':
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self.conn = mysql.connector.connect(
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52
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host=connection_details['ServerName'],
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53
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port=connection_details['ServerPort'],
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user=connection_details['Username'],
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55
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password=connection_details['Password'],
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56
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database='mysql',
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57
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compress=True,
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58
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autocommit=True,
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59
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use_unicode=True,
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60
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charset='utf8mb4',
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get_warnings=True,
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)
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self.conn.set_charset_collation('utf8mb4', 'utf8mb4_0900_ai_ci')
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local_logger.info('Connecting to ' + connection_details['server-vendor-and-type']
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+ ' server completed')
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timered.stop()
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except Exception as e:
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local_logger.error('Error in Connection with details: ')
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local_logger.error(e)
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timered.stop()
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71
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72
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@staticmethod
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73
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def execute_query(local_logger, timered, given_cursor, given_query):
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74
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try:
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75
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timered.start()
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given_cursor.execute(given_query)
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try:
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pt = timedelta(microseconds=(given_cursor.server_processing_time() / 1000))
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79
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local_logger.info('Query executed successfully ' + format(pt))
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80
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except AttributeError:
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81
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local_logger.info('Query executed successfully')
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timered.stop()
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return given_cursor
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84
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except TypeError as e:
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85
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local_logger.error('Error running the query: ')
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86
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local_logger.error(e)
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87
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timered.stop()
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88
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89
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@staticmethod
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90
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def fetch_executed_query(local_logger, timered, given_cursor):
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91
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timered.start()
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92
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local_result_set = given_cursor.fetchall()
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93
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local_logger.info('Result-set has been completely fetched and contains '
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94
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+ str(len(local_result_set)) + ' rows')
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95
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timered.stop()
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96
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return local_result_set
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97
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98
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@staticmethod
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99
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def result_set_to_data_frame(local_logger, timered, given_columns_name, given_result_set):
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100
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timered.start()
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101
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df = pd.DataFrame(data=given_result_set, index=None, columns=given_columns_name)
|
|
102
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local_logger.info('Result-set has been loaded into Pandas DataFrame')
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103
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timered.stop()
|
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104
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return df
|
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105
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danielgp /
db-extractor
