Python logging模块为何晚些时候才检查logger.disabled？

Great question—this gets into some of the practical tradeoffs in the design of Python's built-in logging module, balancing code clarity, responsibility separation, and performance. Let's break this down:

Why the Disabled Check Lives in handle() Instead of _log() #

The logging module's original design splits core responsibilities between methods to keep each function focused:

• _log() is meant to handle record creation and message formatting: it constructs the LogRecord instance, processes message arguments, and prepares metadata like timestamps or caller information.
• handle() is tasked with runtime validation and routing: it checks if the logger is disabled, manages propagation to parent loggers, and passes valid records to registered handlers.

At the time this code was written, the maintainers prioritized separating "record preparation" logic from "record processing" logic. The assumption was that most logging calls would be enabled in production, so the overhead of creating a LogRecord was acceptable for the sake of clean, maintainable code.

The Performance Cost of Disabled Loggers #

Your performance analysis is spot-on: when a logger is marked disabled=True, all the work in _log()—formatting arguments, building the LogRecord, and collecting metadata—gets done entirely for nothing before handle() finally exits early. For code paths that make thousands of logging calls to a disabled logger, this wasted work can add up to measurable performance overhead.

Is This Design Rational? And Should You Optimize It? #

Design Rationale #

Looking at the module's evolution, this choice makes sense in context:

1. Separation of concerns: Keeping record creation and processing distinct made the code easier to extend (e.g., adding custom record factories later) and debug.
2. Historical usage patterns: Early logging use cases didn't emphasize high-throughput scenarios with disabled loggers, so the performance tradeoff wasn't a top priority.

Optimization Worthiness? #

It depends entirely on your use case:

• If your code makes frequent logging calls to disabled loggers, optimizing this can yield meaningful gains. Here are two practical approaches:
  • Wrap logging calls in an explicit check before invoking the log method (avoids even entering _log()):

    if not logger.disabled and logger.isEnabledFor(logging.DEBUG):
        logger.debug("Expensive message: %s", expensive_calculation())
    

  • Monkey-patch the _log() method for your logger to add an early disabled check (use cautiously, as it modifies built-in behavior):

    original_log = logger._log
    def optimized_log(self, level, msg, args, exc_info=None, extra=None):
        if self.disabled:
            return
        original_log(level, msg, args, exc_info, extra)
    logger._log = optimized_log.__get__(logger, type(logger))
    

• For most typical applications, the overhead of wasted LogRecord creation is negligible compared to other application work, so optimizing this might not be worth the effort.

It's also worth noting that later Python versions (3.7+) have added subtle optimizations to logging, including early checks for disabled loggers in critical paths to reduce this exact overhead.

内容的提问来源于stack exchange，提问作者gabhijit