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)
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)
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)
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)
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")
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)
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()])
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()
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)
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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