Python线程池源码分析

liuxiaohua 2019-06-25

对Python线程池的研究是之前对Apshceduler分析的附加工作。

在之前对Apshceduler源码分析的文章中,写到调度器将任务放入线程池的函数

def _do_submit_job(self, job, run_times):
        def callback(f):
            exc, tb = (f.exception_info() if hasattr(f, 'exception_info') else
                       (f.exception(), getattr(f.exception(), '__traceback__', None)))
            if exc:
                self._run_job_error(job.id, exc, tb)
            else:
                self._run_job_success(job.id, f.result())

        f = self._pool.submit(_run_job, job, job._jobstore_alias, run_times, self._logger.name)
        f.add_done_callback(callback)

这里分析的线程池类是concurrent.futures.ThreadPoolExecutor,也就是上述代码中self._pool所使用的类。先上self._pool.submit函数的代码,再做详细分析

def submit(self, fn, *args, **kwargs):
        with self._shutdown_lock:
            if self._shutdown:
                raise RuntimeError('cannot schedule new futures after shutdown')

            f = _base.Future()
            w = _WorkItem(f, fn, args, kwargs)

            self._work_queue.put(w)
            self._adjust_thread_count()
            return f

f和w是两个非常重要的变量,f作为submit返回的对象,submit函数的调用者可以对其添加回调,待fn执行完成后,会在当前线程执行,具体是如何实现的,这里先不说,下面再详细分析;w则是封装了线程需要执行的方法和参数,通过self._work_queue.put(w)方法放入一个队列当中。

self._adjust_thread_count()方法则是检查当前线程池的线程数量,如果小于设定的最大值,就开辟一个线程,代码就不上了,直接看这些个线程都是干嘛的

def _worker(executor_reference, work_queue):
    try:
        while True:
            work_item = work_queue.get(block=True)
            if work_item is not None:
                work_item.run()
                # Delete references to object. See issue16284
                del work_item
                continue
            executor = executor_reference()
            # Exit if:
            #   - The interpreter is shutting down OR
            #   - The executor that owns the worker has been collected OR
            #   - The executor that owns the worker has been shutdown.
            if _shutdown or executor is None or executor._shutdown:
                # Notice other workers
                work_queue.put(None)
                return
            del executor
    except BaseException:
        _base.LOGGER.critical('Exception in worker', exc_info=True)

这些线程就是一个死循环,不断的从任务队列中获取到_WorkItem,然后通过其封装方法,执行我们需要的任务。如果取到的任务为None,就往队列中再放入一个None,以通知其它线程结束,然后结束当前循环。

def run(self):
        if not self.future.set_running_or_notify_cancel():
            return

        try:
            result = self.fn(*self.args, **self.kwargs)
        except BaseException as e:
            self.future.set_exception(e)
        else:
            self.future.set_result(result)

如果没有异常,执行结束后,会执行之前我们说的回调。在self.future.set_result(result)方法中会执行任务回调,当然了,是在当前线程中。如果需要写入数据库之类的操作,不建议在回调中直接写入。

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