10 Tips for Mastering the Python Logging Module

1. Customize LogLevel

In addition to the default DEBUG, INFO, WARNING, ERROR and CRITICAL levels, you can create custom levels. This is useful for differentiating events of varying severity.

import logging

# 创建自定义日志级别
CUSTOM_LEVEL = logging.INFO + 5
logging.addLevelName(CUSTOM_LEVEL, "CUSTOM")

# 创建一个 Logger 并设置自定义日志级别
logger = logging.getLogger("my_logger")
logger.setLevel(CUSTOM_LEVEL)

2. Use different processors

The processor is responsible for sending log events to a specific destination, such as a file or console. You can customize the processor to meet your specific needs.

import logging

# 创建一个 FileHandler 并设置日志文件名
file_handler = logging.FileHandler("my_log.txt")

# 创建一个 StreamHandler 并输出到控制台
stream_handler = logging.StreamHandler()

# 将处理器添加到 Logger
logger = logging.getLogger("my_logger")
logger.addHandler(file_handler)
logger.addHandler(stream_handler)

3. Use filters

Filters allow you to filter log events based on specific criteria. This is useful for logging only events of interest.

import logging

# 创建一个过滤器以过滤 INFO 级别以上的事件
info_filter = logging.Filter()
info_filter.filter = lambda record: record.levelno >= logging.INFO

# 将过滤器添加到 Logger
logger = logging.getLogger("my_logger")
logger.addFilter(info_filter)

4. Format log output

You can customize the format of log events to provide the required information.

import logging

# 创建一个 FORMatter 并设置格式字符串
formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")

# 将 Formatter 添加到处理器
handler = logging.StreamHandler()
handler.setFormatter(formatter)

# 将处理器添加到 Logger
logger = logging.getLogger("my_logger")
logger.addHandler(handler)

5. Using context processor

Context processors allow you to add additional information when logging. This is useful for tracking context within a request or transaction.

import logging
from contextlib import contextmanager

# 创建一个上下文处理器以添加请求 ID
@contextmanager
def request_id_context(request_id):
previous_request_id = logging.currentframe().f_locals.get("request_id")
try:
logging.currentframe().f_locals["request_id"] = request_id
yield
finally:
logging.currentframe().f_locals["request_id"] = previous_request_id

# 使用上下文处理器
logger = logging.getLogger("my_logger")
with request_id_context("1234"):
logger.info("Received request")

6. Use dictionary configuration

You can easily configure the Logging module using a dictionary.

import logging

# 配置字典
logging_config = {
"version": 1,
"formatters": {
"default": {
"format": "%(asctime)s - %(levelname)s - %(message)s"
}
},
"handlers": {
"file": {
"class": "logging.FileHandler",
"filename": "my_log.txt",
"formatter": "default",
},
"console": {
"class": "logging.StreamHandler",
"formatter": "default",
}
},
"loggers": {
"my_logger": {
"handlers": ["file", "console"],
"level": "INFO",
}
}
}

# 从字典配置 Logging
logging.config.dictConfig(logging_config)

7. Integrate third-party packages

The Logging module can be integrated with third-party packages such as Sentry or Rollbar. This allows you to easily send log events to a remote service.

import logging
import sentry_sdk

# 初始化 Sentry 并与 Logging 集成
sentry_sdk.init()
logging.basicConfig(level=logging.INFO, handlers=[sentry_sdk.handler.SentryHandler()])

8. Use Multi-threadingSupport

Logging module supports multi-threaded applications. It uses thread local storage to ensure that each thread has its own independent log processor.

import logging
import threading

# 创建线程安全的 Logger
logger = logging.getLogger("my_logger")

# 创建一个线程并向 Logger 记录
def thread_function():
logger.info("Executing in a separate thread")

# 启动线程
thread = threading.Thread(target=thread_function)
thread.start()

9. Record exceptions

Logging module can automatically record exceptions that occur.

import logging

# 创建一个 Logger
logger = logging.getLogger("my_logger")

# 记录一个异常
try:
raise Exception("An error occurred")
except Exception as e:
logger.exception(e)

10. Using extended logging

python 3.8 introduces support for extended logging. This allows you to create custom logging functions and handlers.

import logging

# 创建一个自定义日志记录函数
def my_log_function(logger, level, msg, *args, **kwargs):
# 您的自定义日志记录逻辑

# 添加自定义日志记录函数到 Logger
logger = logging.getLogger("my_logger")
logger.addHandler(logging.NullHandler())
logger.addFilter(logging.Filter())
logger.log = my_log_function

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