pip install -i https://mirrors.tencent.com/pypi/simple/ tencentcloud-dlc-connector

import tdlc_connector
import datetime
from tdlc_connector import constants

conn = tdlc_connector.connect(region="<REGION>", 
    secret_id="<SECRET_ID>", 
    secret_key="<SECRET_KEY>",
    token=None,
    endpoint=None,
    catalog=constants.Catalog.DATALAKECATALOG,  
    engine="<ENGINE>",
    engine_type=constants.EngineType.AUTO, 
    result_style=constants.ResultStyles.LIST,
    download=False,
    mode=constants.Mode.LASY,
    database='',
    config={},
    callback=None,
    callback_events=None,
    )

# Basic Operations

cursor = conn.cursor()
count = cursor.execute("SELECT 1")
print(cursor.fetchone())                    # Read one line of data
for row in cursor.fetchall():               # Read the remaining multiple lines of data
    print(row)

# Use the pyformat format

cursor.execute("SELECT * FROM dummy WHERE date < %s", datetime.datetime.now())           
cursor.execute("SELECT * FROM dummy WHERE status in %s", (('SUCCESS', 'INIT', 'FAIL'),))
cursor.execute("SELECT * FROM dummy WHERE date < %(date)s AND status = %(status)s", {'date': datetime.datetime.now(), 'status': 'SUCCESS'})

# Use BULK method

cursor.executemany("INSERT INTO dummy VALUES(%s, %s)", [('Zhang San', 18), ('Li Si', 20)])

import tdlc_connector
import datetime
from tdlc_connector import constants


def tdlc_connector_callback(statement_id, state):
    '''
    parmas: statement_id Quest id
    params: state Task status. The enumeration value is constants.TaskStatus
    '''
    print(statement_id, state)


conn = tdlc_connector.connect(region="<REGION>", 
    secret_id="<SECRET_ID>", 
    secret_key="<SECRET_KEY>",
    engine="<ENGINE>",
    engine_type=constants.EngineType.SPARK, 
    result_style=constants.ResultStyles.LIST,
    callback=tdlc_connector_callback,
    callback_events=[constants.CallbackEvent.ON_INIT, constants.CallbackEvent.ON_SUCCESS]
    )

cursor = conn.cursor()
cursor.execute("SELECT 1")
cursor.fetchone()

# The callback function is called when the task is initialized and the task is successful


from tdlc_connector import constants

conn = tdlc_connector.connect(region="<REGION>", 
    secret_id="<SECRET_ID>", 
    secret_key="<SECRET_KEY>",
    engine="<ENGINE>",                            # Select the spark job engine
    result_style=constants.ResultStyles.LIST,
    driver_size=constants.PodSize.SMALL,          # Select Driver Specifications
    executor_size=constants.PodSize.SMALL,        # Select the Executor specification
    executor_num=1,                               # Set the number of Executors
    executor_max_num=1,                           # Set the maximum number of executors. If it is not equal to {executor_num}, enable dynamic resource allocation
)

from tdlc_connector import constants

conn = tdlc_connector.connect(region="<REGION>",
secret_id="<SECRET_ID>",
secret_key="<SECRET_KEY>",
engine="<ENGINE>",
engine_type=constants.EngineType.AUTO  # This parameter can be set to AUTO or not to drive automatic inference
)

from tdlc_connector import constants, formats

formats.FORMAT_STRING_NULL = '\\1'  

conn = tdlc_connector.connect(region="<REGION>", 
    secret_id="<SECRET_ID>", 
    secret_key="<SECRET_KEY>",
    engine="<ENGINE>",
    result_style=constants.ResultStyles.LIST
)