agavra

If you do want a native CLI in-terminal for this, try out github.com/agavra/tuicr

Save you the read, here's the interesting part:

For $90/mo, we ran a benchmark pushing ~30 MiB/s of metrics data via Buffer. Using public pricing calculators, the equivalent Kafka service cost roughly $1,300/month for managed WarpStream and $8,500/month for self-hosted Apache Kafka.

This is the biggest gap in the 0.2.1 release. We have a pretty naive query execution engine because we've spent most of the time on core data structures and ingestion.

I have some prototypes of vectorized compute that takes that same query from 2s -> ~800ms, and it's just early days. If you want to contribute to help make it better, the query engine part of it is begging for help!

also super cool to see you on here valyala! we took a bunch of inspiration from your work at VM. kudos to all you've done :)

The other solution is to aggressively size your disk cache and keep effectively the full working set on disk, using object storage just as a durability layer. Then the main benefit is operational simplicity because you have a true shared-nothing architecture between the read replicas (there's no quorum or hash ring to maintain and no deduplication on read). Obviously you'll have a more expensive deployment topology if you do so, but it's still compelling IMO because you have the knobs to tune whether you want to cache on disk or not.

anecdotally I've heard confirmations of the challenge of running VictoriaMetrics clusters at scale. they're way better than Cortex/Thanos and they've built a pretty awesome product but still are a pretty significant operational burden.

Good point, a tl;dr is probably worthwhile.

It's definitely not quite turn key just yet but we've been dogfooding it in production against a moderate metrics use case (~30k samples/s) and have it hooked up to grafana (you just configure a prometheus source and point to your deployed URL). We run it on a single node with no replicas ;)

Checkout https://github.com/agavra/tuicr - it's built exactly for this purpose (reviewing code in your terminal and then adding comments and exporting it to an agent to fix).

I just built a version of this a month ago that also allows you to add review comments so you can export them back to an Agent to fix: https://github.com/agavra/tuicr

Great work on deff, would love to brainstorm here :)

the compression algorithm you select for your data is quite dependent on the dataset you have. the equations in this blog post don't help you choose which compression to use, but rather "how much" and when to compress. I would be curious to formalize the math for different compression algorithms though... might be a good follow up post!

This is spot on, I understand very little about how terminal rendering works and was able to build github.com/agavra/tuicr (Terminal UI for Code Review) in an evening. The initial TUI design was done via Claude.

RocksDB actually does something somewhat similar with its prefix compression. It prefix-compresses texts and then "resets"the prefix compression every N records so it stores a mapping of reset point -> offset so you can skip across compressed records. It's pretty neat

this is pretty different but reminds me of https://en.wikipedia.org/wiki/Bitcask - if you're storing it all in memory why not just use a hash index?

So cool to see this make the front page of hacker news! I'm the author, I'll be online here throughout the weekend to answer any questions you might have :) excited for the next post which is in the works about LSM trees.

We haven't even started to discuss Object Storage, but it ends up looking very very similar if you're building data systems that use that instead of raw filesystems (not so much for physics reasons, but because of the way object storage require immutable objects and penalize you for many API calls)

Thanks! I use https://monodraw.helftone.com/ which is my favorite one-time-purchase software of all time. I definitely agree the buttons on the top left are unnecessary but ... it's cute and it makes me happy so I can't help it. Maybe I'll come up with a different style for the next blog

Sounds like a perfect fit for https://slatedb.io/ -- it's just that (an embedded, rust, KV store that supports transactions).

It's built specifically to run on object storage, currently relies on the `object_store` crate but we're consdering OpenDAL instead so if Garage works with those crates (I assume it does if its S3 compatible) it should just work OOTB.

There's a whole class of interesting problems related to query evolution - and it varies greatly depending on the "environment" that you're interested in (see mjdrogalis' docs on updating a running query). Generally, the strategy that ksqlDB takes at the moment is to validate what upgrades are possible to do in-place and which are not - for the former, ksqlDB "just does it" and for the latter, we are designing a mechanism to deploy topologies side-by-side and then atomically cut over when the new topology is caught up to the old one.

There's an in-progress blog post that describes exactly this class of problems - keep an eye out for it!