连接断开时龙卷风内存泄漏

Question

I've got a setup where Tornado is used as kind of a pass-through for workers. Request is received by Tornado, which sends this request to N workers, aggregates results and sends it back to client. Which works fine, except when for some reason timeout occurs — then I've got memory leak.

I've got a setup which similar to this pseudocode:

workers = ["http://worker1.example.com:1234/",
           "http://worker2.example.com:1234/", 
           "http://worker3.example.com:1234/" ...]

class MyHandler(tornado.web.RequestHandler):
    @tornado.web.asynchronous
    def post(self):
        responses = []

        def __callback(response):
            responses.append(response)
            if len(responses) == len(workers):
                self._finish_req(responses)

        for url in workers:
            async_client = tornado.httpclient.AsyncHTTPClient()
            request = tornado.httpclient.HTTPRequest(url, method=self.request.method, body=body)
            async_client.fetch(request, __callback) 

    def _finish_req(self, responses):
        good_responses = [r for r in responses if not r.error]
        if not good_responses:
            raise tornado.web.HTTPError(500, "\n".join(str(r.error) for r in responses))
        results = aggregate_results(good_responses)
        self.set_header("Content-Type", "application/json")
        self.write(json.dumps(results))
        self.finish()

application = tornado.web.Application([
    (r"/", MyHandler),
])

if __name__ == "__main__":
    ##.. some locking code 
    application.listen()
    tornado.ioloop.IOLoop.instance().start()

What am I doing wrong? Where does the memory leak come from?

Answer 1

I don't know the source of the problem, and it seems gc should be able to take care of it, but there's two things you can try.

The first method would be to simplify some of the references (it looks like there may still be references to responses when the RequestHandler completes):

class MyHandler(tornado.web.RequestHandler):
    @tornado.web.asynchronous
    def post(self):
        self.responses = []

        for url in workers:
            async_client = tornado.httpclient.AsyncHTTPClient()
            request = tornado.httpclient.HTTPRequest(url, method=self.request.method, body=body)
            async_client.fetch(request, self._handle_worker_response) 

    def _handle_worker_response(self, response):
        self.responses.append(response)
        if len(self.responses) == len(workers):
            self._finish_req()

    def _finish_req(self):
        ....

If that doesn't work, you can always invoke garbage collection manually:

import gc
class MyHandler(tornado.web.RequestHandler):
    @tornado.web.asynchronous
    def post(self):
        ....

    def _finish_req(self):
        ....

    def on_connection_close(self):
        gc.collect()

Answer 2

The code looks good. The leak is probably inside Tornado.

I only stumbled over this line:

async_client = tornado.httpclient.AsyncHTTPClient()

Are you aware of the instantiation magic in this constructor? From the docs:

"""
The constructor for this class is magic in several respects:  It actually
creates an instance of an implementation-specific subclass, and instances
are reused as a kind of pseudo-singleton (one per IOLoop).  The keyword
argument force_instance=True can be used to suppress this singleton
behavior.  Constructor arguments other than io_loop and force_instance
are deprecated.  The implementation subclass as well as arguments to
its constructor can be set with the static method configure()
"""

So actually, you don't need to do this inside the loop. (On the other hand, it should not do any harm.) But which implementation are you using CurlAsyncHTTPClient or SimpleAsyncHTTPClient?

If it is SimpleAsyncHTTPClient, be aware of this comment in the code:

"""
This class has not been tested extensively in production and
should be considered somewhat experimental as of the release of
tornado 1.2. 
"""

You can try switching to CurlAsyncHTTPClient. Or follow Nikolay Fominyh's suggestion and trace the calls to __callback().