My first experience with Lemmy was thinking that the UI was beautiful, and lemmy.ml (the first instance I looked at) was asking people not to join because they already had 1500 users and were struggling to scale.
1500 users just doesn’t seem like much, it seems like the type of load you could handle with a Raspberry Pi in a dusty corner.
Are the Lemmy servers struggling to scale because of the federation process / protocols?
Maybe I underestimate how much compute goes into hosting user generated content? Users generate very little text, but uploading pictures takes more space. Users are generating millions of bytes of content and it’s overloading computers that can handle billions of bytes with ease, what happened? Am I missing something here?
Or maybe the code is just inefficient?
Which brings me to the title’s question: Does Lemmy benefit from using Rust? None of the problems I can imagine are related to code execution speed.
If the federation process and protocols are inefficient, then everything is being built on sand. Popular protocols are hard to change. How often does the HTTP protocol change? Never. The language used for the code doesn’t matter in this case.
If the code is just inefficient, well, inefficient Rust is probably slower than efficient Python or JavaScript. Could the complexity of Rust have pushed the devs towards a simpler but less efficient solution that ends up being slower than garbage collected languages? I’m sure this has happened before, but I don’t know anything about the Lemmy code.
Or, again, maybe I’m just underestimating the amount of compute required to support 1500 users sharing a little bit of text and a few images?
Great link, thanks!
Looks like Discord was using 177 nodes each with 4TB disk space running Cassandra (Java), and then in 2022 migrated to 72 nodes of 9TB disk space running ScyllaDB (C++). Switching to a C++ database and writing their services in Rust allowed them to finally end latency spikes from Java garbage collection. The messages are stored in buckets assigned by channel and time window. Buckets are replicated across 3 nodes, and are accessed using “quorum consistency”. They were still having difficulties with “hot partitions” where many users at once all want to access the same bucket, leading to increased latencies. They solved it by putting a data service in front of the database that would detect multiple identical incoming queries and pool them together into a single database request. The nodes are still spending a lot of time periodically “compacting” their tables for better disk read performance.