监控数据管道

使用 Python 的 ETL 和 ELT

Jake Roach

Data Engineer

监控数据管道

应监控数据管道中的数据变化和执行失败

缺失数据
数据类型漂移
包弃用或功能变更

数据管道的日志记录工作流。

记录数据管道性能

记录执行时的性能
失败时提供排查起点

import logging
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.DEBUG)

# Create different types of logs
logging.debug(f"Variable has value {path}")
logging.info("Data has been transformed and will now be loaded.")

DEBUG: Variable has value raw_file.csv
INFO: Data has been transformed and will now be loaded.

记录警告与错误

import logging
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.DEBUG)

# Create different types of logs
logging.warning("Unexpected number of rows detected.")
logging.error("{ke} arose in execution.")

WARNING: Unexpected number of rows detected.
ERROR: KeyError arose in execution.

使用 try-except 处理异常

try:
    # Execute some code here
    ...

except:
    # Logging about failures that occured
    # Logic to execute upon exception
    ...

出错时可执行应对代码

用 try-except 处理特定异常

在 except 子句中传入具体异常

try:
    # Try to filter by price_change
    clean_stock_data = transform(raw_stock_data)
    logging.info("Successfully filtered DataFrame by 'price_change'")

except KeyError as ke:
    # Handle the error, create new column, transform
    logging.warning(f"{ke}: Cannot filter DataFrame by 'price_change'")
    raw_stock_data["price_change"] = raw_stock_data["close"] - raw_stock_data["open"]
    clean_stock_data = transform(raw_stock_data)

Passons à la pratique !

使用 Python 的 ETL 和 ELT