Bossy Lobster

A blog by Danny Hermes; musing on tech, mathematics, etc.

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A Threadsafe Connection Pool for Requests

I've been load testing quite a bit recently. So far I've been impressed by the wrk project's use of sockets to pour on a ridiculously high amount of load. When I had stretched the system under load past its limits, finding the fortio project was a breath of fresh air, in particular because of the inclusion of a -qps flag to control the rate (and duration) of a load test.

So what does this have to do with requests? These load testing tools (and many others) are great if you know what to expect of your failures, but I was load testing to debug a problem. I wanted a way to keep all failed responses and inspect them after the fact; I wanted an escape hatch into the event loop firing off requests.

Motivated by wrk, I wanted to utilize a large-ish thread pool all sharing a connection pool. A shared conneciton pool enables testing of re-used TCP sockets and can generate more load since TLS negotiation can be re-used. Using the select package and low-level socket objects to accomplish this task likely would've given maximal throughput but the sheer amount of work required was not worth it, especially because a modest few hundred requests per second was all I was looking for (i.e. a controlled but steady flow of traffic). So I sought to put the über-popular requests1 package to the task.

Contents

Zero Modification Approach

Based on a very popular StackOverflow question, I knew requests.Session() wasn't "really" threadsafe, but I figured I'd give it a try anyhow. By using a requests adapter, a connection pool size can be specified

import requests


def connection_pool(size):
    session = requests.Session()
    adapter = requests.adapters.HTTPAdapter(
        pool_connections=size, pool_maxsize=size
    )
    session.mount("http://", adapter)
    session.mount("https://", adapter)
    return session

To take it for a test drive, I spun up 4 threads to share a connection pool of size 2 and at first things looked just fine. Running

import threading


URL = "https://www.google.com/"


def make_request(i, pool):
    response = pool.get(URL)
    print(f"{i} Status Code: {response.status_code}")


def spawn_threads(pool_size, thread_count):
    pool = connection_pool(pool_size)
    threads = []
    for i in range(thread_count):
        thread = threading.Thread(target=make_request, args=(i, pool))
        thread.start()
        threads.append(thread)
    return threads


def main():
    threads = spawn_threads(2, 4)
    for thread in threads:
        thread.join()


if __name__ == "__main__":
    main()

all 4 threads seem to do their work without interfering with one another

1 Status Code: 200
2 Status Code: 200
0 Status Code: 200
3 Status Code: 200

Zero Correctness

To be sure, I added a response hook to actually verify the socket being used on each request

import socket


def make_response_hook(i):
    def response_hook(response, **unused_kwargs):
        sock = socket.fromfd(
            response.raw.fileno(), socket.AF_INET, socket.SOCK_STREAM
        )
        client_ip, port = sock.getsockname()
        print(f"{i} Socket client address: {client_ip}:{port}")

    return response_hook


def make_request(i, pool):
    response = pool.get(URL, hooks={"response": make_response_hook(i)})
    print(f"{i} Status Code: {response.status_code}")

After running this I confirmed that the threads were not sharing a pool of (at most) 2 open TCP sockets:

0 Socket client address: 192.168.7.31:50784
0 Status Code: 200
3 Socket client address: 192.168.7.31:50786
3 Status Code: 200
2 Socket client address: 192.168.7.31:50787
1 Socket client address: 192.168.7.31:50785
1 Status Code: 200
2 Status Code: 200

To me, this indicates either requests.Session directly or some component (e.g. the underlying urllib3 package) is using thread local storage for parts of the connection pool2. However, using a global lock on usage of the connection pool

LOCK = threading.Lock()


def make_request(i, pool):
    with LOCK:
        response = pool.get(URL, hooks={"response": make_response_hook(i)})
    print(f"{i} Status Code: {response.status_code}")

it is clear that the same socket is used for all requests

0 Socket client address: 192.168.7.31:51143
0 Status Code: 200
1 Socket client address: 192.168.7.31:51143
1 Status Code: 200
2 Socket client address: 192.168.7.31:51143
2 Status Code: 200
3 Socket client address: 192.168.7.31:51143
3 Status Code: 200

so there is some level of sharing across threads.

Queue and Per-thread Pool

Since I couldn't directly rely on requests.Session() as a multithreaded pool I sought to create one. I briefly looked into the requests_toolbelt.threaded.pool module but it also lacks the escape hatch I was looking for.

Using a global lock as above completely defeats the point of concurrent workers, so this is not an option. Requiring each thread to maintain its own pool may be unnecessarily restrictive, but due to the state sharing issue each of the N distinct connections will need an exclusive lock.

In order to simulate N locks while maintaining some modicum of throughput, a queue.Queue() can be used:

import queue


def threadsafe_pool(size):
    id_queue = queue.Queue(maxsize=size)
    connections = {}
    for i in range(size):
        id_queue.put(i)
        connections[i] = connection_pool(1)
    return connections, id_queue

Rather than putting the requests.Session() objects directly into the queue, a read-only dictionary can be shared across all threads. Putting this to use, the connections and the queue can be passed to the make_request() thread target. The worker can make a (blocking) get() for a connection ID from the queue, keep it until the request has completed and place the connection ID back on the queue for re-use:

def make_request(i, connections, id_queue):
    connection_id = id_queue.get()
    connection = connections[connection_id]

    response = connection.get(URL, hooks={"response": make_response_hook(i)})

    id_queue.task_done()
    id_queue.put(connection_id)
    print(f"{i} Status Code: {response.status_code}")

Verification

To see that this works as expected, the spawn_threads() helper can be updated to pass along the connections and queue

def spawn_threads(pool_size, thread_count):
    connections, id_queue = threadsafe_pool(pool_size)
    threads = []
    for i in range(thread_count):
        thread = threading.Thread(
            target=make_request, args=(i, connections, id_queue)
        )
        thread.start()
        threads.append(thread)
    return threads

and running the code shows exactly 2 sockets were used (and re-used)

1 Socket client address: 192.168.7.31:51426
1 Status Code: 200
0 Socket client address: 192.168.7.31:51425
0 Status Code: 200
2 Socket client address: 192.168.7.31:51426
2 Status Code: 200
3 Socket client address: 192.168.7.31:51425
3 Status Code: 200

Turning up the difficulty level a bit, the sockets used can be tracked (utilizing the atomicity of list.append() in Python)

SOCKET_PAIRS = []


def make_response_hook(i):
    def response_hook(response, **unused_kwargs):
        sock = socket.fromfd(
            response.raw.fileno(), socket.AF_INET, socket.SOCK_STREAM
        )
        SOCKET_PAIRS.append(sock.getsockname())

    return response_hook

Using this, a histogram of the sockets

import collections


def main():
    threads = spawn_threads(5, 256)
    for thread in threads:
        thread.join()

    histogram = collections.Counter(SOCKET_PAIRS)
    for (ip_, port), count in histogram.most_common(len(histogram)):
        print(f"{ip_}:{port} -> {count}")

shows that only 5 sockets were used to service all 256 requests

4 Status Code: 200
0 Status Code: 200
...
255 Status Code: 200
192.168.7.31:51625 -> 52
192.168.7.31:51624 -> 52
192.168.7.31:51621 -> 51
192.168.7.31:51623 -> 51
192.168.7.31:51622 -> 50
  1. Version 2.23.0, running Python 3.8.2 on macOS 10.15.4 as of this writing
  2. I'd love to know more but was more focused on getting a working multithreaded connection pool.

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