I have a networked process that looks somewhat like this in its most basic form

```
import asyncio

shutting_down = False

async def read_message(reader, timeout=30):
    async def _read(reader: asyncio.StreamReader):
        try:
            d = await reader.readexactly(24)
            # do something with the data
            print("I'm never called")
        except:
            return None

    try:
        return await asyncio.wait_for(_read(reader), timeout)
    except Exception:
        return None


async def my_service():
    reader, writer = await asyncio.open_connection('127.0.0.1', 20333)
    while not shutting_down:
        m = await read_message(reader, timeout=0)
        if m is None:
            continue
        # else process message


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.create_task(my_service())
    loop.run_forever()
```

read_message() has a default timeout of 30, but I thought setting it to 0 (instead of None) would be equal to blocking. This bleeds 16GB of memory in ~3 minutes. A minimal example is provided.

I manually applied the patch of https://bugs.python.org/issue36613 to a self compiled build of 3.7.3 (https://github.com/python/cpython/commit/ef4ec6ed12d6c6200a85068f60483723298b6ff4) on Ubuntu 18.04 and that did not solve the problem.

Thanks for the report.
If the problem  is in asyncio.wait_for() function the real network code can be stripped for the leakage example and replaced with `await asyncio.sleep()`.

Would you try to boil down the snippet by converting it into a code that I can execute on my laptop to reproduce the problem?

Hi Andrew, 
There is an attached minimal example (that differs from the code given in the first comment). I couldn't attach 2 files. So I pasted the code of one file to showcase how we could run into the issue, then a minimal reproducible example without network code in the attachment.

Nice, thanks!

This is the consumption I'm seeing.

Perhaps also worth mentioning is that when we supply None as timeout value in the `wait_for()` in the minimal sample, then it still reports 60MB memory usage. Seems pretty steep for doing basically nothing but looping around.