Caio's Stuff

The fastest WebSocket implementation

Introduction — Adapted photo of William Warby on Unsplash.

This post measures the performance of wtx and other projects to figure out which one is faster. If any metrics or procedures described here are flawed, feel free to point them.

Metrics

Differently from autobahn, which is the standard automated test suite that verifies client and server implementations, there isn't an easy and comprehensive benchmark suite for the WebSocket Protocol (well at least I couldn't find any) so let's create one.

Enter ws-bench! Three parameters that result in reasonable combinations trying to replicate what could possibly happen in a production environment are applied to listening servers by the program.

	Low	Medium	High
Number of connections	1	128	256
Number of messages	1	64	128
Transfer memory (KiB)	1	64	128

Number of connections

Tells how well a server can handle multiple connections concurrently. For example, there are single-thread, concurrent single-thread or multi-thread implementations.

In some cases this metric is also influenced by the underlying mechanism responsible for scheduling the execution of workers/tasks.

Number of messages

When a payload is very large, it is possible to send it using several sequential frames where each frame holds a portion of the original payload. This frame formed by different smaller frames is called here "message" and the number of "messages" can measure the implementation's ability of handling their encoding or decoding as well as the network latency (round trip time).

Transfer memory

It is not rare to hear that the cost of a round trip is higher than the cost of allocating memory, which is generally true. Unfortunately, based on this concept some individuals prefer to indiscriminantly call the heap allocator without investigating whether such a thing might incur a negative performance impact.

Frames tend to be small but there are applications using WebSocket to transfer different types of real-time blobs. That said, let's investigate the impact of larger payload sizes.

Investigation

Project	Language	Fork	Application
uWebSockets	C++	https://github.com/c410-f3r/uWebSockets	examples/EchoServer.cpp
fastwebsockets	Rust	https://github.com/c410-f3r/fastwebsockets	examples/echo_server.rs
gorilla/websocket	Go	https://github.com/c410-f3r/websocket	examples/echo/server.go
tokio-tungstenite	Rust	https://github.com/c410-f3r/tokio-tungstenite	examples/echo-server.rs
websockets	Python	https://github.com/c410-f3r/regular-crates/blob/wtx-0.5.2/ws-bench/_websockets.py	_websockets.py
wtx	Rust	https://github.com/c410-f3r/wtx	wtx-instances/generic-examples/web-socket-server.rs

In order to try to ensure some level of fairness, all six projects had their files modified to remove writes to stdout, impose optimized builds where applicable and remove SSL or compression configurations.

The benchmark procedure is quite simple: servers listen to incoming requests on different ports, the ws-bench binary is called with all uris and the resulting chart is generated. In fact, everything is declared in this bash script.

Chart	Connections	Messages	Memory	fastwebsockets	gorilla/websockets	tokio_tungstenite	uWebsockets	websockets	wtx_hyper	wtx-_raw_async_std	wtx_raw_tokio
Chart	low	mid	high	104	273	102	88	232	64❗	67	65
Chart	low	high	low	5759	5783	5784	5760	5728❗	5802	5764	5736
Chart	low	high	mid	336	546	235	192	526	160	163	159❗
Chart	low	high	high	331	960	360	325	725	250	282	249❗
Chart	mid	low	high	18	22	18	15	31	14	12❗	13
Chart	mid	mid	high	4503	5724	3959	4816	9754	3514	3474❗	3498
Chart	mid	high	low	5684❗	5800	5721	5687	6681	5689	5764	5684❗
Chart	mid	high	mid	11020	13735	8365	9072	19874	6937	6895❗	6933
Chart	mid	high	high	19808	23178	15471	19821	38327	13759	13693❗	13749
Chart	high	low	low	52	71	98	46	1053	52	41❗	88
Chart	high	low	mid	84	86	74	51	1043	60	50	48❗
Chart	high	low	high	124	82	78	57	1059	55	54❗	58
Chart	high	mid	low	2987	3051	3027	2955	5071	2981	3000	2942❗
Chart	high	mid	mid	20150	21475	14593	18931	41368	11172	10987❗	11268
Chart	high	mid	high	41846	43514	20706	21779	41091	16118	15555	15524❗
Chart	high	high	low	5828	5941	5830	5790	9400	5778❗	5877	5808
Chart	high	high	mid	53756	55063	44829	47312	107758	36628	34333❗	37000

Tested with a notebook composed by i5-1135G7, 256GB SSD and 32GB RAM. Combinations of low and mid were discarded for showing almost zero values in all instances.

soketto and ws-tools were initially tested but eventually abandoned at a later stage due to frequent shutdowns. I didn't dive into the root causes but they can return back once the underlying problems are fixed by the authors.

Result

wtx as a whole scored an average amount of 6350.31 ms, followed by tokio-tungstenite with 7602.94 ms, uWebSockets with 8393.94 ms, fastwebsockets with 10140.58 ms, gorilla/websockets with 10900.23 ms and finally websockets with 17042.41 ms.

websockets performed the worst in several tests but it is unknown whether such behavior could be improved. Perhaps some modification to the _weboskcets.py file? Let me know if it is the case.

Among the three metrics, the number of messages was the most impactful because the client always verifies the content sent back from a server leading a sequential-like behavior. Perhaps the number of messages is not a good parameter for benchmarking purposes.

To finish, wtx was faster in all tests and can indeed be rotulated as the fastest WebSocket implementation at least according to the presented projects and methodology.