It's hard to write something that is both accessible and well-motivated. The best uses of category theory is when the morphisms are far more exotic than "regular functions". E.g. It would be nice to describe a circuit of live queries (like https://materialize.com/ stuff) with proper caching, joins, etc. Figuring this out is a bit of an open problem. Haskell's standard library's Monad and stuff are watered down to... - Source: Hacker News / 5 months ago

> [...] `https://materialize.com/` to solve their memory issues [...] Disclaimer: I work at Materialize Recently there have been major improvements in Materialize's memory usage as well as using disk to swap out some data. I find it pretty easy to hook up to Postgres/MySQL/Kafka instances: https://materialize.com/blog/materialize-emulator/. - Source: Hacker News / 5 months ago

I agree. So many disparate solutions. The streaming sql primitives are by themselves good enough (e.g. `tumble`, `hop` or `session` windows), but the infrastructural components are always rough in real life use cases. Crossing fingers for solutions like `https://github.com/feldera/feldera` to solve their memory issues, or `https://clickhouse.com/docs/en/materialized-view` to solve reliable streaming consumption.... - Source: Hacker News / 5 months ago

Or the related Materialize stuff https://materialize.com/. - Source: Hacker News / 6 months ago

The original post makes so much more sense in this context! One of the "holy grails" in my mind is making CQRS and dataflow programming as easy to learn and maintain as existing imperative programming languages - and easy to weave into real-time UX. There are so many backend endpoints in the wild that do a bunch of things in a loop, many of which will require I/O or calls to slow external endpoints, transform the... - Source: Hacker News / 7 months ago

This looks extremely cool. This is basically incremental view maintenance in databases, a problem that almost everybody (I think) has when using SQL databases and wanting to do some derived views for more performant access patterns. Importantly, they seem to support a wide breath of SQL operators, and it's open-source! There's already a bunch of tools in this area: 1. Materialize[0], which afaik is more... - Source: Hacker News / 7 months ago

OctoSQL[0] or DuckDB[1] will most likely be much simpler, while going through 10 GB of JSON in a couple seconds at most. Disclaimer: author of OctoSQL [0]: https://github.com/cube2222/octosql. - Source: Hacker News / about 2 years ago

This is really cool! With their Postgres scanner[0] you can now easily query multiple datasources using SQL and join between them (i.e. Postgres table with JSON file). Something I strived to build with OctoSQL[1] before. It's amazing to see how quickly DuckDB is adding new features. Not a huge fan of C++, which is right now used for authoring extensions, it'd be really cool if somebody implemented a Rust extension... - Source: Hacker News / about 2 years ago

Congrats on the Show HN! It's great to see more tools in this area (querying data from various sources in-place) and the Lambda use case is a really cool idea! I've recently done a bunch of benchmarking, including ClickHouse Local and the usage was straightforward, with everything working as it's supposed to. Just to comment on the performance area though, one area I think ClickHouse could still possibly improve... - Source: Hacker News / over 2 years ago

SPyQL is really cool and its design is very smart, with it being able to leverage normal Python functions! As far as similar tools go, I recommend taking a look at DataFusion[0], dsq[1], and OctoSQL[2]. DataFusion is a very (very very) fast command-line SQL engine but with limited support for data formats. Dsq is based on SQLite which means it has to load data into SQLite first, but then gives you the whole breath... - Source: Hacker News / over 2 years ago