This is a basic HTTP server written from scratch in Python which can respond to GET requests for webpages stored in www directory. The motivation behind this project is this coding challenge.
Beyond learning to deal with sockets, in this project, multiple server architechures are implemented along with a testing framework to understand how they compare against one another.
To start the HTTP server in multi-processing mode, you need to run:
./start_server -p
To start the HTTP server in multi-threading mode, you need to run:
./start_server -t
To start the HTTP server in async(coroutine) mode, you need to run:
./start_async_server
Multi-processing(threading) modes are implemented by spinning up a number of worker processes(threads) dedicated to listening and processing client requests. Default number of workers is 5, and can be configured using:
$:~/path_to_your_repo/webserver (main)$./start_server -p -w 3
Server listening on 127.0.0.1:2000
Starting connection worker for ('127.0.0.1', 2000), pid: 26417, tid: 140647853301760
Main thread tid: 140647853301760
Starting connection worker for ('127.0.0.1', 2000), pid: 26418, tid: 140647853301760
Starting connection worker for ('127.0.0.1', 2000), pid: 26419, tid: 140647853301760
To talk to this HTTP server, you can use curl:
$:~/path_to_your_repo/webserver (main)$curl -i 127.0.0.1:2000
HTTP/1.1 200 OK
<!DOCTYPE html>
<html lang="en">
<head>
<title>Simple Web Page</title>
</head>
<body>
<h1>Test Web Page</h1>
<p>My web server served this page!</p>
</body>
</html>
or you can use send_to_server:
$:~/path_to_your_repo/webserver (main)$./send_to_server 127.0.0.1 2000 "GET / HTTP/1.1"
HTTP/1.1 200 OK
...
...
or you can type below into your browser as well:
http://127.0.0.1:2000/
To compare against different architectures, you can start the HTTP server in the mode you want and use ./test_my_server to send concurrent connections to the server, and get back the overall response time. Example usage:
$:~/path_to_your_repo/webserver (main)$./test_my_server -i -nc 4 127.0.0.1 2000
http://127.0.0.1:2000/io - Status Code: 200, took 1072.60 ms
http://127.0.0.1:2000/io - Status Code: 200, took 1102.88 ms
http://127.0.0.1:2000/io - Status Code: 200, took 2155.36 ms
http://127.0.0.1:2000/io - Status Code: 200, took 2211.57 ms
Total time taken 2216.14 ms
Use -c to test cpu bound task, and -i for i/o bound task. Number of concurrent connection the test scripts tries to make is configurable by -nc.
Preliminary stats are available in the file Performance Stats.xlsx.
Following graphs show response time for different architectures (in ms) as number of clients increase. (These were generated with 5 workers)
Couple of things evident from above data:
- Asyncio is really good for I/O bound task as all coroutines can wait in parallel
- Multi-threaded server in Python is can not beat other archs as GIL prevents true parallel processing
- If your request is CPU heavy, multi-processing continues to be the most performant
Looks like a server implementing multi-processing architecture, where each process (worker) can handle incoming request asynchronously can be best of both world for a generic set of requests.