Go vs. Python for low-latency websocket servers with 50 concurrent users

This review evaluates Go and Python as backend technologies for a TLS-enabled websocket server, focusing on low latency, parsing capabilities, and handling up to 50 concurrent connections on Rocky Linux 9.

TL;DR

Best for: Go is best for applications prioritizing raw performance, minimal server load, and explicit concurrency control for websocket streaming and parsing, especially when starting a new codebase. Skip if: Go is not ideal if your team's primary expertise is exclusively in Python and rapid iteration on existing Python code is a higher priority than absolute performance gains for this specific scale. Bottom line: For a new, performance-sensitive websocket server handling 50 concurrent users, Go offers a superior foundation due to its concurrency model and compiled nature.

METHODOLOGY

This v0 review draws on the user lcnielsen's stated requirements and concerns posted on Reddit, combined with public knowledge of the Go and Python language ecosystems for backend development. The signal describes a need for a TLS-enabled websocket server that performs message parsing (chopping into chunks, passing over TLS-enabled TCP) with low latency and minimal server load for approximately 50 concurrent users, running on Rocky Linux 9. The user also requires basic crypto support, with optional frontend templating and OIDC. The current implementation uses Bun, which the user intends to replace. This review covers the suitability of Go and Python for these specific technical constraints, addressing lcnielsen's questions regarding Go's standard library websocket support and Python's concurrency/latency for data wrangling. Independent benchmarks of specific libraries or long-term workflow implications are not covered in this initial assessment. Update cadence: re-tested when claims diverge from observed behavior or new performance data becomes available.

WHAT IT DOES

Go's Concurrency and Performance for WebSockets

Go is a compiled, statically typed language known for its strong concurrency primitives: goroutines and channels. Goroutines are lightweight, independently executing functions that are multiplexed onto a smaller number of OS threads, making them highly efficient for I/O-bound tasks like network communication. Channels provide a safe way for goroutines to communicate. For websockets, while the Go standard library (net/http) includes basic HTTP server capabilities, it does not offer direct websocket protocol implementation. This is where community libraries like gorilla/websocket or nhooyr/websocket become essential. gorilla/websocket is a mature, widely used library that provides a robust and performant implementation of the WebSocket protocol. nhooyr/websocket is a more modern alternative, emphasizing simplicity and adherence to the standard library's io.Reader/io.Writer interfaces. Both are stable and simple to work with, addressing lcnielsen's concern about coder/websocket (which is less common; gorilla/websocket is the de-facto standard). Go's compiled nature ensures a small binary size and fast startup times, beneficial for server load on Rocky Linux 9.

Python's Asynchronous Capabilities and Parsing Strengths

Python, an interpreted language, offers a rich ecosystem and high developer productivity. For concurrency, Python 3.5+ introduced asyncio, a framework for writing concurrent code using the async/await syntax. This allows for cooperative multitasking, where tasks yield control to the event loop, enabling a single thread to handle many concurrent I/O operations. For websockets, the websockets library is a popular and well-maintained choice, built on asyncio. It provides a clean API for both client and server implementations. Python's strength in parsing and data manipulation, stemming from its extensive standard library and third-party packages, makes it an attractive option for the message chopping and processing required by lcnielsen's application. However, Python's Global Interpreter Lock (GIL) means that CPU-bound tasks within a single process cannot truly run in parallel, which could be a concern for high-throughput parsing if not carefully managed with asyncio or by offloading work to separate processes/threads.

WHAT'S INTERESTING / WHAT'S NOT

Go's explicit concurrency model with goroutines and channels is a significant advantage for a websocket server. This design inherently handles many concurrent connections efficiently, making it well-suited for the low-latency, low-server-load requirement for 50 users. The compiled binary means fewer runtime dependencies and easier deployment on Rocky Linux 9 compared to an interpreted language. The gorilla/websocket library is battle-tested and provides a solid foundation, directly addressing lcnielsen's question about library stability. The performance profile of Go often translates to lower resource consumption, which is valuable for operational costs and stability.

What's less interesting about Go, particularly for lcnielsen who has expert Python proficiency, is the initial learning curve. While Go is designed for simplicity, it's a different paradigm from Python. The lack of a standard library websocket implementation is a minor inconvenience, but the robust third-party options effectively fill this gap. The bonus features like templating and OIDC are well-supported in Go's ecosystem, though perhaps with less out-of-the-box, batteries-included frameworks compared to Python or Java.

Python's appeal lies in lcnielsen's expert-level proficiency, which promises faster initial development. For 50 concurrent users, asyncio and the websockets library can certainly handle the load. Python's parsing capabilities are excellent, and for the specific task of chopping messages, it would be straightforward to implement. The concern about latency for parsing is valid; while asyncio handles I/O concurrency well, heavy CPU-bound parsing might introduce latency if not designed to yield control frequently or if the parsing logic itself is inefficient. This would necessitate careful profiling and potentially offloading CPU-intensive parts. Python's deployment on Rocky Linux 9 is standard, but managing virtual environments and dependencies can be more involved than a single Go binary. The