@BenjDicken I’ve always thought systems performance was more of a reference than a book to read cover to cover? Is that the case? Or am I horribly mistaken and I should read it 😭
@massimo_san@Adriksh It’s not just files though, it’s anything that has a file descriptor associated with it which can include sockets too. Generally databases especially high frequency analytics databases can easily run in to this.
@TrisH0x2A Also if you’re doing direct io you should open files with the O_DIRECT and O_SYNC flag. Since you’re not fsyncing something like this is not guaranteed to write to disk.
@TrisH0x2A Generally syscalls are expensive due to the user to kernel space context switch. This is why something like io_uring tends to perform well, you batch syscalls before switching from user to kernel space. Not only that it has zero copy buffers.
@sudo_goreng If you want to deep dive in to LSM compaction (definitely relevant to RocksDB). This is an excellent survey paper on the topic: https://t.co/eMFldAImuM
@johncrickett Database Internals and Designing Data Intensive Applications are two of my favorites I’ve read in the past. Right now I’m reading through Understanding Deep Learning.
@GeoffreyHuntley I’ve been thinking about checking out ocaml for some time, I noticed that Jane Street has a “learn ocaml” repository, but it’s several years old: https://t.co/rM5TIALsRN I was thinking about going through it but I wonder if there are better options.
@claudeai Why wouldn’t I just have claude code run ‘gh pr diff <pull request #>’ locally? This seems like a dud idea when I already have the tooling to do it.
@lcamtuf I tend to use https://t.co/IcfdJempiW when I want follow up questions from Claude. Most recently used it while learning about gradient descent. I also will often go out of my way to ensure Claude does not explicitly solve problems for me but instead act like a fancy rubber ducky.
@KatanaLarp Many people are highlighting that as a follow up you can use the models to improve performance. This is true. But also, I think that once a code base gets to a specific complexity it is very tedious to drive performance gains via targeted prompts and profiling. Great read!