A utils package for Yes4All SOP
Project description
This is a utils package served for SOP Data Analytics team at Yes4All. It contains various modules to work with PostgreSQL, MinIO, Google API, Airflow, Telegram…
Contents Overview
Install package
$ pip install --upgrade sop-deutilsModules usage
Airflow
Use case: When having a new scheduled task file on Airflow.
Functional:
Auto naming DAG ID and alerting failed DAG to Telegram:
Sample code of base config Airflow dag file:
from airflow import DAG
from airflow.decorators import task
from sop_deutils.y4a_airflow import auto_dag_id, telegram_alert
default_args = {
"retries": 20, # number times to retry when the task is failed
"retry_delay": timedelta(minutes=7), # time delay among retries
"start_date": datetime(2023, 7, 14, 0, 0, 0), # date that the DAG start to run
"owner": 'duikha', # account name of DAG owner
"on_failure_callback": telegram_alert, # this contains function to alert to Telegram when the DAG/task is failed
"execution_timeout": timedelta(hours=4), # limit time the DAG run
}
dag = DAG(
dag_id=auto_dag_id(), # this contains function to name the DAG based on the file directory
description='Sample DAG', # description about the DAG
schedule_interval="1 6 * * *", # schedule for the DAG run
default_args=default_args, # default arguments contains dictionary of predefined params above
catchup=False, # If True, the DAG will backfill tasks from the start_date to current date
)
with dag:
@task(owner='linhvu') # account name of task owner. if not specified, the owner is the same as the DAG owner
def function_1():
...
@task(owner='trieuna') # account name of task owner. if not specified, the owner is the same as the DAG owner
def function_2():
...
function_1() >> function_2()List of account name can be found here.
GoogleSheet
Use case: When interacting with Google Sheet.
Functional:
2.1 initialize
Firstly, import GoogleSheet utils module class. If want to use personal credentials, provide the dictionary of credentials as value of parameter user_creds.
from sop_deutils.gg_api.y4a_sheet import GGSheetUtils
sheet_utils = GGSheetUtils(
user_creds=None,
)2.2 create_spread_sheet
To create a new spread sheet, using create_spread_sheet method, it has the following parameters:
sheet_name (required): Name of the sheet to create. (str)
folder_id (optional): ID of the folder contains spreadsheet. The default value is None. (str)
share_to (optional): List of email to share the spreadsheet. The default value is []. (list)
The method will return the created spreadsheet id.
spread_sheet_id = sheet_utils.create_spread_sheet(
sheet_name='your-sheet-name',
folder_id='your-folder-id',
share_to=['longnc@yes4all.com'],
)
print(spread_sheet_id)Output:
1vTjZOcRfd5eiF5Qo8DCha29Vdt0zvYP11XPbq54eCMg2.3 list_all_work_sheets
To get all available worksheet of spreadsheet, using list_all_work_sheets method, it has the following parameter:
sheet_id (required): Spreadsheet id. (str)
The method will return list all worksheets of spreadsheet.
ws_list = sheet_utils.list_all_work_sheets(
sheet_id='your-sheet-id',
)
print(ws_list)Output:
['Sheet1']2.4 delete_work_sheet
To delete specific worksheet of spreadsheet, using delete_work_sheet method, it has the following parameters:
sheet_id (required): Spreadsheet id. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
sheet_utils.delete_work_sheet(
sheet_id='your-sheet-id',
sheet_name='your-sheet-name',
)2.5 clear_work_sheet
To clear all data of specific worksheet of spreadsheet, using clear_work_sheet method, it has the following parameters:
sheet_id (required): Spreadsheet id. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
delete_cells (optional): Whether to delete all cells. The default value is False. (bool)
sheet_utils.clear_work_sheet(
sheet_id='your-sheet-id',
sheet_name='your-sheet-name',
)2.6 get_data
To get data from the given sheet, using get_data method, it has the following parameters:
sheet_id (required): Spreadsheet id. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
range_from (optional): The begining of the range of data from sheet to get. The default value is None. If None, the range from will be the first cell of the sheet. (str)
range_to (optional): The end of the range of data from sheet to get. The default value is None. If None, the range to will be the last cell of the sheet. (str)
columns_first_row (optional): Whether to convert the first row to columns. The default value is False. (bool)
auto_format_columns (optional): Whether format columns name of dataframe (lowercase, replace special characters with underscore…). The default value is False. (bool)
The method will return the dataframe contains data from sheet.
df = sheet_utils.get_data(
sheet_id='your-sheet-id',
columns_first_row=True,
)
print(df)Output:
| Column1 Header | Column2 Header | Column3 Header |
| ---------------| ---------------| ---------------|
| Row1 Value1 | Row1 Value2 | Row1 Value3 |
| Row2 Value1 | Row2 Value2 | Row2 Value3 |
| Row3 Value1 | Row3 Value2 | Row3 Value3 |2.7 insert_data
To insert data to the given sheet, using insert_data method, it has the following parameters:
data (required): Dataframe containing data to insert. (pd.DataFrame)
sheet_id (required): Spreadsheet ID. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
from_row_index (optional): The index of the row from which to begin inserting. The default value is 1. (int)
insert_column_names (optional): Whether to insert column names. The default value is False. (bool)
parse_input (optional): Whether to parse input values as if the user typed them into the UI. The default value is True. (bool)
pre_process (optional): Whether to process input based on the pre-defined function of DA. The default value is True. (bool)
sheet_utils.insert_data(
data=df,
sheet_id='your-sheet-id',
from_row_index=2,
insert_column_names=False,
)2.8 update_data
To update data of the given sheet, using the update_data method, it has the following parameters:
data (required): Dataframe containing data to update. (pd.DataFrame)
sheet_id (required): Spreadsheet ID. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
range_from (optional): The beginning of the range of data to update. The default value is 'A1'. (str)
parse_input (optional): Whether to parse input values as if the user typed them into the UI. The default value is True. (bool)
pre_process (optional): Whether to process input based on the pre-defined function of DA. The default value is True. (bool)
sheet_utils.update_data(
data=new_df,
sheet_id='your-sheet-id',
range_from='A4',
)2.9 remove_data
To remove data from a specific range of the given sheet, using the remove_data method, it has the following parameters:
sheet_id (required): Spreadsheet ID. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
list_range (optional): List of data ranges to remove. The default value is ['A1:Z1', 'A4:Z4']. (list)
sheet_utils.remove_data(
sheet_id='your-sheet-id',
list_range=[
'A2:D5',
'E5:G6',
],
)2.10 add_work_sheet
To add new worksheet from the given spreadsheet, using add_work_sheet method, it has the following parameters:
title (required): Title of the new worksheet. (str)
sheet_id (required): Spreadsheet id. (str)
num_rows (optional): Number rows of the new worksheet. The default value is 1000. (int)
num_cols (optional): Number columns of the new worksheet. The default value is 26. (int)
The method will return worksheet object that is compatible with gspread library. (This worksheet object will has the same attributes and methods as the gspread worksheet object)
ws = sheet_utils.add_work_sheet(
title='New Work Sheet',
sheet_id='your-sheet-id',
)2.11 get_spread_sheet_id
To get the spreadsheet id from the given spreadsheet title, using get_spread_sheet_id method, it has the following parameters:
title (required): Title of the spreadsheet. (str)
folder_id (optional): The id of folder that contains the spreadsheet. The default value is None. (str)
The method will return the spreadsheet id.
sheet_id = sheet_utils.get_spread_sheet_id(
title='Your Sheet Title',
)
print(sheet_id)Output:
'your-sheet-id'2.12 open_spread_sheet_by_title
To open the spreadsheet from the given spreadsheet title, using open_spread_sheet_by_title method, it has the following parameters:
title (required): Title of the spreadsheet. (str)
folder_id (optional): The id of folder that contains the spreadsheet. The default value is None. (str)
The method will return spreadsheet object that is compatible with gspread library. (This spreadsheet object will has the same attributes and methods as the gspread spreadsheet object)
ss = sheet_utils.open_spread_sheet_by_title(
title='Your Sheet Title',
)2.13 open_spread_sheet
To open the spreadsheet from the given spreadsheet id, using open_spread_sheet method, it has the following parameters:
sheet_id (required): ID of the spreadsheet. (str)
The method will return spreadsheet object that is compatible with gspread library. (This spreadsheet object will has the same attributes and methods as the gspread spreadsheet object)
ss = sheet_utils.open_spread_sheet(
sheet_id='your-sheet-id',
)2.14 gspread_load_data
To load data to the given sheet, using gspread_load_data method. This method is integrated with GSpread load data function that provides the high efficiency and convenience, it can be used as the alternative of two methods insert_data and update_data, it has the following parameters:
data (required): Dataframe containing data to load. (pd.DataFrame)
sheet_id (required): Spreadsheet ID. (str)
sheet_name (optional): Worksheet name. The default value is 'Sheet1'. (str)
from_row (optional): Row at which to start loading the DataFrame. The default value is 1. (int)
from_col (optional): Column at which to start loading the DataFrame. The default value is 1. (int)
include_index (optional): Whether to include the DataFrame’s index as an additional column. The default value is False. (bool)
include_column (optional): Whether to add a header row or rows before data with column names (if include_index is True, the index’s name(s) will be used as its columns’ headers). The default value is True. (bool)
resize_worksheet (optional): If True, changes the worksheet’s size to match the shape of the provided DataFrame, if False, worksheet will only be resized as necessary to contain the DataFrame contents. The default value is False. (bool)
allow_formulas (optional): Whether to interprets =foo as a formula in cell values; otherwise all text beginning with = is escaped to avoid its interpretation as a formula. The default value is True. (bool)
string_escaping (optional): Determines when string values are escaped as text literals (by adding an initial ' character) in requests to Sheets API, 3 parameter values are accepted: (‘default’: only escape strings starting with a literal ' character. ‘off’: escape nothing; cell values starting with a ' will be interpreted by sheets as an escape character followed by a text literal. ‘full’: escape all string values), the escaping done when allow_formulas=False (escaping string values beginning with =) is unaffected by this parameter’s value. The default value is 'default'. (str)
sheet_utils.gspread_load_data(
data=df,
sheet_id='your-sheet-id',
sheet_name='Sheet1',
from_row=3,
from_col=4,
include_index=True,
include_column=True,
)MinIO
MinIO is an object storage, it is API compatible with the Amazon S3 cloud storage service. MinIO can be used as a datalake to store unstructured data (photos, videos, log files, backups, and container images) and structured data.
Use case: when need to store raw data or get raw data from datalake. Notes that the stored data extension must be .parquet .
Notes about how to determine the file_path parameter in minIO when using this module:
For example, if the directory to the data file in minIO is as above, then the file_path is "/scraping/amazon_vendor/avc_bulk_buy_request/2023/9/24/batch_1695525619" (after removing bucket name, data storage mode, and data file extension).
Functional:
3.1 initialize
Firstly, import minIO utils module class.
from sop_deutils.datalake.y4a_minio import MinioUtils
minio_utils = MinioUtils()3.2 data_exist
To check whether data exists in a storage directory, using the data_exist method, it has the following parameters:
mode (required): The data storage mode. The value must be either 'prod' or 'stag'. (str)
file_path (required): The data directory to check. (str)
bucket_name (optional): The name of the bucket to check. The default value is 'sop-bucket'. (str)
The method will return True if data exists; otherwise, it returns False.
minio_utils.data_exist(
mode='stag',
file_path='your-data-path',
)Output:
True3.3 get_data_value_exist
To get the distinct values of a specified column of data in a data directory, using the get_data_value_exist method, it has the following parameters:
mode (required): The data storage mode. The value must be either 'prod' or 'stag'. (str)
file_path (required): The data directory to get distinct values. (str)
column_key (required): The column name to get distinct values. (str)
bucket_name (optional): The name of the bucket to get distinct values. The default value is 'sop-bucket'. (str)
The method will return a list of distinct values.
minio_utils.get_data_value_exist(
mode='stag',
file_path='your-data-path',
column_key='your-chosen-column',
)Output:
['value_1', 'value_2']3.4 load_data
To load data from a dataframe to storage, using the load_data method, it has the following parameters:
data (required): Dataframe containing data to load. (pd.DataFrame)
mode (required): The data storage mode. The value must be either 'prod' or 'stag'. (str)
file_path (required): The directory to load the data. (str)
bucket_name (optional): The name of the bucket to load the data. The default value is 'sop-bucket'. (str)
minio_utils.load_data(
data=df,
mode='stag',
file_path='your-data-path',
)3.5 get_data
To get data from a single file of a storage directory, using the get_data method, it has the following parameters:
mode (required): The data storage mode. The value must be either 'prod' or 'stag'. (str)
file_path (required): The data directory to get data. (str)
bucket_name (optional): The name of the bucket to get data. The default value is 'sop-bucket'. (str)
The method will return a dataframe containing the data to get.
df = minio_utils.get_data(
mode='stag',
file_path='your-data-path',
)
print(df)Output:
| Column1 Header | Column2 Header | Column3 Header |
| ---------------| ---------------| ---------------|
| Row1 Value1 | Row1 Value2 | Row1 Value3 |
| Row2 Value1 | Row2 Value2 | Row2 Value3 |
| Row3 Value1 | Row3 Value2 | Row3 Value3 |3.6 get_data_wildcard
To get data from multiple files in storage directories, using the get_data_wildcard method, it has the following parameters:
mode (required): The data storage mode. The value must be either 'prod' or 'stag'. (str)
file_path (required): The parent data directory to get the data. (str)
bucket_name (optional): The name of the bucket to get data. The default value is 'sop-bucket'. (str)
The method will return a dataframe containing the data to get.
df = minio_utils.get_data_wildcard(
mode='stag',
file_path='your-parent-data-path',
)
print(df)Output:
| Column1 Header | Column2 Header | Column3 Header |
| ---------------| ---------------| ---------------|
| Row1 Value1 | Row1 Value2 | Row1 Value3 |
| Row2 Value1 | Row2 Value2 | Row2 Value3 |
| Row3 Value1 | Row3 Value2 | Row3 Value3 |PostgreSQL
Use case: when interacting with Postgres database.
Functional:
4.1 initialize
Firstly, import PostgreSQL utils module class. This class has four parameters:
account_name: The shortcode of client account name to connect to PostgreSQL. The value can be used as DA member name. The default value is None. If not provide, must use params pg_account and pg_password. List of account name can be found here. (str)
pg_name: PostgreSQL db name to connect. Accepted values are 'raw_master', 'raw_repl', 'serving_master', 'serving_repl'. (str)
pg_account: The client account to connect to PostgreSQL. The default value is None. (str)
pg_password: The client password to connect to PostgreSQL. The default value is None. (str)
from sop_deutils.sql.y4a_postgresql import PostgreSQLUtils
pg_utils = PostgreSQLUtils(
pg_name='serving_master',
account_name='user1',
)
# or
pg_utils = PostgreSQLUtils(
pg_name='serving_master',
pg_account='y4a_sop_user1',
pg_password='password-of-user1',
)4.2 read_sql_file
To get the SQL query from an SQL file, using the read_sql_file method, it has the following parameter:
sql_file_path (required): The located path of the SQL file. (str)
The method will return the string representation of the SQL query.
sql = pg_utils.read_sql_file(
sql_file_path='your-path/select_all.sql',
)
print(sql)Output:
SELECT * FROM your_schema.your_table4.3 insert_data
To insert data into a PostgreSQL table, using the insert_data method, it has the following parameters:
data (required): A dataframe containing the data to insert. (pd.DataFrame)
schema (required): The schema containing the table to insert. (str)
table (required): The name of the table to insert the data into. (str)
ignore_errors (optional): Whether to ignore errors when inserting data. The default value is False. (bool)
commit_every (optional): The number of rows of data to commit each time. The default value is 5000. (int)
db_pool_conn (optional): The connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.insert_data(
data=your_df,
schema='your-schema',
table='your-table',
)4.4 bulk_insert_data
To insert a large amount of data into a PostgreSQL table and need high performance, using the bulk_insert_data method, it has the following parameters:
data (required): A dataframe containing the data to insert. (pd.DataFrame)
schema (required): The schema containing the table to insert. (str)
table (required): The name of the table to insert the data into. (str)
commit_every (optional): The number of rows of data to commit each time. The default value is 5000. (int)
db_pool_conn (optional): The connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.bulk_insert_data(
data=your_df,
schema='your-schema',
table='your-table',
)4.5 upsert_data
To upsert data in a PostgreSQL table, using the upsert_data method, it has the following parameters:
data (required): A dataframe containing the data to upsert. Note that if the dataframe contains duplicated rows, they will be dropped. (pd.DataFrame)
schema (required): The schema containing the table to upsert. (str)
table (required): The name of the table to upsert the data into. (str)
where_conditions (optional): A string of a query that uses conditions to update. The default value is None. (str)
ignore_existence (optional): Whether to insert only new transactions and ignore existing transactions. The default value is False. (bool)
commit_every (optional): The number of rows of data to commit each time. The default value is 5000. (int)
db_pool_conn (optional): The connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.upsert_data(
data=your_df,
schema='your-schema',
table='your-table',
)4.6 bulk_upsert_data
To upsert large data to a PostgreSQL table and need high performance, using the bulk_upsert_data method, it has the following parameters:
data (required): A DataFrame containing data to upsert. If the DataFrame contains duplicated rows, they will be dropped. (pd.DataFrame)
schema (required): The schema containing the table to upsert. (str)
table (required): The name of the table to upsert the data into. (str)
where_conditions (optional): A string of a query that uses conditions to update. The default value is None. (str)
ignore_existence (optional): Whether to insert only new transactions and ignore existing transactions. The default value is False. (bool)
commit_every (optional): The number of rows of data to commit each time. The default value is 5000. (int)
db_pool_conn (optional): The connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.bulk_upsert_data(
data=your_df,
schema='your-schema',
table='your-table',
)4.7 update_table
To update new data of specific columns in a table based on primary keys, using the update_table method, it has the following parameters:
data (required): A DataFrame containing data to update, including primary keys and columns to update. (pd.DataFrame)
schema (required): The schema containing the table to update data. (str)
table (required): The table to update data. (str)
columns (required): A list of column names to update data. (list)
commit_every (optional): The number of rows of data to commit each time. The default value is 5000. (int)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.update_table(
data=your_df,
schema='your-schema',
table='your-table',
columns=['col1', 'col2'],
)4.8 get_data
To get data from a PostgreSQL database using a SQL query, use the get_data method. This method has the following parameters:
sql (required): SQL query to get data. (str)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
The method will return a dataframe that contains data extracted by the given SQL query.
Here’s how to use the get_data method in Python:
df = pg_utils.get_data(
sql='your-query',
)
print(df)Output:
| Column1 Header | Column2 Header | Column3 Header |
| ---------------| ---------------| ---------------|
| Row1 Value1 | Row1 Value2 | Row1 Value3 |
| Row2 Value1 | Row2 Value2 | Row2 Value3 |
| Row3 Value1 | Row3 Value2 | Row3 Value3 |4.9 select_distinct
To retrieve the distinct values of a specified column in a PostgreSQL table, use the select_distinct method, it has the following parameters:
col (required): Column name to get the distinct data.. (str)
schema (required): Schema contains table to get data. (str)
table (required): Table to get data. (str)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
The method will return a list of distinct values from the specified column.
distinct_values = pg_utils.select_distinct(
col='chosen-column',
schema='your-schema',
table='your-table',
)
print(distinct_values)Output:
['val1', 'val2', 'val3']4.10 show_columns
To retrieve a list of column names for a specific PostgreSQL table, use the show_columns method. It has the following parameters:
schema (required): The schema that contains the table from which to retrieve columns. (str)
table (required): The name of the table from which to retrieve columns. (str)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
The method will return a list of column names for the specified table.
col_names = pg_utils.show_columns(
schema='your-schema',
table='your-table',
)
print(col_names)Output:
['col1', 'col2', 'col3']4.11 execute
To execute a given SQL query, use the execute method. It has the following parameters:
sql (required): The SQL query to execute. (str)
fetch_output (optional): Whether to fetch the results of the query. The default value is False. (bool)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
The method will return a list of query output if fetch_output is True, otherwise None.
sql = """
UPDATE
sales_order_avc_di o,
(
SELECT
DISTINCT po_name,
asin,
CASE
WHEN o.status LIKE '%cancel%' AND a.status IS NULL THEN ''
WHEN o.status LIKE '%cancel%' THEN CONCAT(a.status,' ',cancel_date)
ELSE o.status END po_asin_amazon_status
FROM
sales_order_avc_order_status o
LEFT JOIN
sales_order_avc_order_asin_status a USING (updated_at, po_name)
WHERE updated_at > DATE_SUB(NOW(), INTERVAL 1 DAY)
) s
SET
o.po_asin_amazon_status = s.po_asin_amazon_status
WHERE
o.po_name = s.po_name
AND o.asin = s.asin
"""
pg_utils.execute(
sql=sql,
)4.12 add_column
To add a new column to a specific PostgreSQL table, use the add_column method. It has the following parameters:
schema (required): The schema containing the table to create the column. (str)
table (required): The name of the table to create the column. (str)
column_name (optional): The name of the column to create (available when creating a single column). The default value is None. (str)
dtype (optional): The data type of the column to create (available when creating a single column). The default value is None. (str)
multiple_columns (optional): A dictionary containing column names as keys and their corresponding data types as values (available when creating multiple columns). The default value is an empty dictionary. (dict)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.add_column(
schema='your-schema',
table='your-table',
multiple_columns={
'col1': 'int',
'col2': 'varchar(50)',
},
)4.13 create_table
To create a new table in a PostgreSQL database, use the create_table method. It has the following parameters:
schema (required): The schema containing the table to create. (str)
table (required): The name of the table to create. (str)
columns_with_dtype (required): A dictionary containing column names as keys and their corresponding data types as values. (dict)
columns_primary_key (optional): A list of columns to set as primary keys. The default value is []. (list)
columns_not_null (optional): A list of columns to set as “not null” constraints. The default value is []. (list)
columns_with_default (optional): A dictionary containing column names as keys and their default values as values. The default value is an empty dictionary. (dict)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
Notes that table will be automatically granted privileges following the rules after creating.
pg_utils.create_table(
schema='your-schema',
table='your-new-table',
columns_with_dtype={
'col1': 'int',
'col2': 'varchar(50)',
'col3': 'varchar(10)',
},
columns_primary_key=[
'col1',
],
columns_not_null=[
'col2',
],
columns_with_default={
'col3': 'USA',
},
)4.14 auto_grant
To grant table privileges to users in PostgreSQL, use the auto_grant method. It has the following parameters:
schema (required): The schema containing the table to grant. (str)
list_tables (required): A list of tables name to grant. (list)
list_users (optional): A list of users to grant access. The default value is None. If None, the table will be granted for all the predefined users. (list)
privileges (optional): A list of privileges to grant. The default value is ['SELECT']. Accepted values in the privileges list are: 'SELECT', 'INSERT', 'UPDATE', 'DELETE', 'TRUNCATE', 'REFERENCES', 'TRIGGER'. (list)
all_privileges (optional): Whether to grant all privileges. The default value is False. (bool)
pg_utils.auto_grant(
schema='your-schema',
list_tables=['your-new-table'],
list_users=[
'linhvk',
'trieuna',
],
privileges=[
'SELECT',
'INSERT',
'UPDATE',
],
)4.15 truncate_table
To remove all the data from a PostgreSQL table, use the truncate_table method. It has the following parameters:
schema (required): The schema containing the table to truncate. (str)
table (required): The table name to truncate. (str)
reset_identity (optional): Whether to reset the identity of the table. The default value is False. (bool)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
pg_utils.truncate_table(
schema='your-schema',
table='your-table',
)4.16 table_exists
To check if the PostgreSQL table exists in the database, use the table_exists method. It has the following parameters:
schema (required): The schema containing the table to check. (str)
table (required): The table name to check. (str)
db_pool_conn (optional): A connection pool to connect to the database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)
The method will return True if the table exists and False if it does not.
pg_utils.table_exists(
schema='your-schema',
table='your-exists-table',
)Output:
True4.17 coalesce
To coalesce missing values in a DataFrame based on a specified order of columns, use the coalesce method. It has the following parameters:
data (required): The input DataFrame. (pd.DataFrame)
columns_order (required): The order of columns for coalescing. (list)
The method will return a series representing the coalesced column.
df = pd.DataFrame(
{
'col1': [1, 2, None, 4, None],
'col2': [None, 20, 30, None, 50],
'col3': [10, 20, 30, 40, 50],
}
)
df['coalesce'] = pg_utils.coalesce(
data=df,
columns_order=['col1', 'col2', 'col3'],
)
print(df[['coalesce']])Output:
| coalesce |
| ---------------|
| 1.0 |
| 2.0 |
| 30.0 |
| 4.0 |
| 50.0 |4.18 create_table_from_df
To create a new table in a PostgreSQL database which has predefined attributes based on Pandas dataframe, use the create_table_from_df method. It has the following parameters:
data (required): Reference dataframe for table creation. (pd.DataFrame)
schema (required): The schema containing the table to create. (str)
table (required): The name of the table to create. (str)
Notes that table will be automatically granted privileges following the rules after creating.
df = pd.DataFrame(
{
'col1': [1, 2, None, 4, None],
'col2': [None, 20, 30, None, 50],
'col3': [10, 20, 30, 40, 50],
}
)
pg_utils.create_table_from_df(
data=df,
schema='your-schema',
table='your-new-table',
)Telegram
Use case: When need to send messages to Telegram by using bot
Functional:
5.1 send_message
To send messages to Telegram, using send_message method, it has the following parameters:
text (required): Message to send. (str)
bot_token (optional): Token of the bot which send the message. The default value is None. If the value is None, the bot sleep at 9pm will be used to send messages. (str)
chat_id (optional): ID of group chat where the message is sent. The default value is None. If the value is None, the group chat Airflow Status Alert will be used. (str)
parse_mode (optional): Sending mode, the accepted value is Markdown or HTML. The default value is Markdown. (str)
from sop_deutils.y4a_telegram import send_message
send_message(
text='Hello liuliukiki'
)5.2 send_data
To send data to Telegram, using send_data method, it has the following parameters:
data (required): Data to send. (pd.DataFrame)
title (optional): The title of the message. The default value is None. (str)
bot_token (optional): Token of the bot which send the data. The default value is None. If the value is None, the bot sleep at 9pm will be used to send data. (str)
chat_id (optional): ID of group chat where the data is sent. The default value is None. If the value is None, the group chat Airflow Status Alert will be used. (str)
parse_mode (optional): Sending mode, the accepted value is Markdown or HTML. The default value is Markdown. (str)
from sop_deutils.y4a_telegram import send_data
send_data(
data=my_df,
title='Sample Data',
)GoogleChat
Use case: When need to send messages to Google Chat space by using bot
Functional:
6.1 send_message
To send messages to chat space of Google, using send_message method, it has the following parameters:
webhook_url (required): Url of the webhook that is registered in the chat space. How to create webhook. (str)
message (required): The content to send to the chat space. (str)
from sop_deutils.gg_api.y4a_chat import send_message
send_message(
webhook_url=f'https://chat.googleapis.com/v1/spaces/{SPACE_ID}/messages?key={KEY}&token={TOKEN}'
message='Hello liuliukiki',
)6.2 send_data
To send data to chat space of Google, using send_data method, it has the following parameters:
webhook_url (required): Url of the webhook that is registered in the chat space. How to create webhook. (str)
data (required): Data to send. (pd.DataFrame)
title (optional): The title of the message. The default value is None. (str)
from sop_deutils.gg_api.y4a_chat import send_data
send_data(
webhook_url=f'https://chat.googleapis.com/v1/spaces/{SPACE_ID}/messages?key={KEY}&token={TOKEN}'
data=my_df,
title='Sample Data',
)GoogleMail
Use case: when need to send email to group of people.
Functional:
7.1 initialize
Firstly, import GGMail utils module class. This class has two parameters:
sender_email: The email of sender. The default value is None. If not provide, the email of DA team will be used. (str)
sender_password: The password email of sender. The default value is None. If not provide, the email of DA team will be used. (str)
from sop_deutils.gg_api.y4a_mail import GGMailUtils
mail_utils = GGMailUtils() # This utils will use email of DA team
# or
mail_utils = GGMailUtils(
sender_email='user@domain.abc',
sender_password='something',
)7.2 send_mail
To send plain text email, using the send_mail method, it has the following parameter:
receiver_email (required): List of people to receive email. (list)
content (required): The content of email. (str)
cc_email (optional): List of people to receive CC. The default value is None. (list)
subject (optional): The subject of email. The default value is None. (str)
mail_utils.send_mail(
receiver_email=['user1@domain.abc', 'user2@domain.abc'],
content='j ai biec',
)7.3 send_mail_w_attachments
To send email with attachments, using the send_mail_w_attachments method, it has the following parameters:
receiver_email (required): List of people to receive email. (list)
content (required): The content of email. (str)
attachment_path (required): List of file path to attach. (list)
cc_email (optional): List of people to receive CC. The default value is None. (list)
subject (optional): The subject of email. The default value is None. (str)
mail_utils.send_mail_w_attachments(
receiver_email=['user1@domain.abc', 'user2@domain.abc'],
content='j ai biec',
attachment_path=['parent_dir/file1.xlsx', 'parent_dir/file2.xlsx'],
)7.4 send_mail_w_pandas_df
To send email with pandas dataframe as Excel file to group of people, using the send_mail_w_pandas_df method, it has the following parameters:
receiver_email (required): List of people to receive email. (list)
content (required): The content of email. (str)
data_list (required): List of dataframe to attach. (list)
file_name (required): List of file name respectively to list of dataframe. Notes that each file name must contain .xlsx. (list)
cc_email (optional): List of people to receive CC. The default value is None. (list)
subject (optional): The subject of email. The default value is None. (str)
df1 = pd.DataFrame([1, 2, 3], columns=['d1'])
df2 = pd.DataFrame([4, 5, 6], columns=['d2'])
mail_utils.send_mail_w_pandas_df(
receiver_email=['user1@domain.abc', 'user2@domain.abc'],
content='j ai biec',
data_list=[df1, df2],
file_name=['data1.xlsx', 'data2.xlsx'],
)All in one (DAConfig)
Use case: So far, there are a lot of platforms that needs to access frequently, in order not to import lots of modules, users can inherit all of above modules as simplest way.
Functional:
Firstly, import DAConfig class. This class has the following parameter:
account_name: The client account name to access platforms. The value can be used as DA member name. List of account name can be found here. (str)
from sop_deutils.base.y4a_da_cfg import DAConfig
da_cfg = DAConfig(
account_name='your-account-name',This class will have its attributes as all above modules (PostgreSQL, MinIO, Google API, Airflow, Telegram) that users don’t need to import and config to connect individually to each platform, each platform attributes will have the its own methods that listed above. List of attributes are:
minio_utils
pg_raw_r_utils (connected to PostgreSQL raw read - repl)
pg_raw_w_utils (connected to PostgreSQL raw write - master)
pg_serving_r_utils (connected to PostgreSQL serving read - repl)
pg_serving_w_utils (connected to PostgreSQL serving write - master)
sheet_utils
print(da_cfg.minio_utils)
print(da_cfg.pg_raw_r_utils)
print(da_cfg.pg_raw_w_utils)
print(da_cfg.pg_serving_r_utils)
print(da_cfg.pg_serving_w_utils)
print(da_cfg.sheet_utils)Output:
<sop_deutils.datalake.y4a_minio.MinioUtils object at 0x7fe6e704d6f0>
<sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704d9f0>
<sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704dae0>
<sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704e170>
<sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704e0b0>
<sop_deutils.gg_api.y4a_sheet.GGSheetUtils object at 0x7fe72c65e1d0>Workflow example
from datetime import datetime, timedelta
from airflow import DAG
from airflow.decorators import task
import pandas as pd
from sop_deutils.y4a_airflow import auto_dag_id, telegram_alert
from sop_deutils.base.y4a_da_cfg import DAConfig
owner = 'linhvu'
cfg = DAConfig(owner)
default_args = {
"retries": 20, # number times to retry when the task is failed
"retry_delay": timedelta(minutes=7), # time delay among retries
"start_date": datetime(2023, 7, 14, 0, 0, 0), # date that the DAG start to run
"owner": owner, # account name of DAG owner
"on_failure_callback": telegram_alert, # this contains function to alert to Telegram when the DAG/task is failed
"execution_timeout": timedelta(hours=4), # limit time the DAG run
}
dag = DAG(
dag_id=auto_dag_id(), # this contains function to name the DAG based on the file directory
description='Sample DAG', # description about the DAG
schedule_interval="1 6 * * *", # schedule for the DAG run
default_args=default_args, # default arguments contains dictionary of predefined params above
catchup=False, # If True, the DAG will backfill tasks from the start_date to current date
)
with dag:
@task
def create_spreadsheet():
spread_sheet_id = cfg.sheet_utils.create_spread_sheet(
sheet_name='test_sheet_231020',
share_to=['longnc@yes4all.com'],
)
return spread_sheet_id
@task
def insert_data_spreadsheet(spread_sheet_id):
df = pd.DataFrame(
[[1, 2, 3, 4]]*20,
columns=['col1', 'col2', 'col3', 'col4']
)
cfg.sheet_utils.insert_data(
data=df,
sheet_id=spread_sheet_id,
from_row_index=1,
insert_column_names=True,
)
@task
def process_data_spreadsheet(spread_sheet_id):
cfg.sheet_utils.remove_data(
sheet_id=spread_sheet_id,
list_range=[
'A3:D3',
'A15:D15',
],
)
@task
def etl_from_sheet_to_db(spread_sheet_id):
df_from_sheet = cfg.sheet_utils.get_data(
sheet_id=spread_sheet_id,
columns_first_row=True,
)
df_from_sheet['total'] = df_from_sheet['col1'] + df_from_sheet['col2']\
+ df_from_sheet['col3'] + df_from_sheet['col4']
df_from_sheet.dropna(inplace=True)
for col in df_from_sheet.columns:
df_from_sheet[col] = df_from_sheet[col].astype('int')
cfg.pg_serving_w_utils.create_table(
schema='y4a_sop_analyst',
table='test_231020',
columns_with_dtype={
'col1': 'int',
'col2': 'int',
'col3': 'int',
'col4': 'int',
'total': 'int',
},
)
cfg.pg_serving_w_utils.insert_data(
data=df_from_sheet,
schema='y4a_sop_analyst',
table='test_231020',
)
@task
def execute_query():
df_from_db = cfg.pg_serving_r_utils.get_data(
sql='SELECT * FROM y4a_sop_analyst.test_231020',
)
print(df_from_db)
cfg.pg_serving_w_utils.execute(
sql='TRUNCATE TABLE y4a_sop_analyst.test_231020',
)
spread_sheet_id = create_spreadsheet()
insert_data_spreadsheet(spread_sheet_id) \
>> process_data_spreadsheet(spread_sheet_id) \
>> etl_from_sheet_to_db(spread_sheet_id) \
>> execute_query()provided by liuliukiki
hinh anh users feedback thu vien kieu
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for sop_deutils-1.0.2.1-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 4aa2699c985b16c1de4a9c733c00f716d264ec50c7c8b5c69d74f28cc8bc6f89 |
|
| MD5 | 3ae904229f854bed2cc627f681d57272 |
|
| BLAKE2b-256 | 75de6008cc42b7a4428700f4c833ff4336b1240e318b2ea9436013215394f27c |
