nlz
Joined 5 years ago
Karma
9
Submissions
7
Member since
Mar 18
tkmx.odio.dev/u/nevzheng
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Comment · 5 months ago
Hi! I'm building a database for fun, and I wanted to share how I bootstrapped my sql engine catalog on top of my storage engine.
Comment · 8 months ago
Yes! This is a great idea. Thanks for mentioning it! GlueSQL could be another cool target too (https://github.com/gluesql/gluesql). I think there's a fun exploration in taking a storage engine and seeing if it's compatible with different SQL layers — GlueSQL as a simpler starting point, DataFusion as the more complete option (https://github.com/apache/datafusion). Plugging into a real SQL engine seems like a great strategy for uncovering bugs, fixing correctness issues, and discovering what use cases the storage layer actually needs to support. It's also interesting how certain SQL engines and storage engines tend to align well with each other — the right pairing can unlock a lot. Definitely a direction I want to explore.
Comment · 8 months ago
Great point — the causal vs mechanical distinction is a cleaner way to put it than what I wrote. Context chains preserve application-specific, causal information about what you were trying to do. Stack traces just tell you where code happened to be executing. That gap gets huge with async, where the mechanical stack is basically noise.
Comment · 8 months ago
Author here. I've been working with Rust more recently, and thinking through its error handling ecosystem led me down a rabbit hole comparing it to what I'd used before — C, Go, exceptions, and Google's Absl Status. The post ended up being a broader survey of error handling principles across languages and organizations than I originally planned.
Looking forward to input — I'd love to hear what your experiences with error handling have been like, especially across different languages or at scale.