pgrx VS quickwit

Consider also the companies and work behind pgrx [0] and pgzx [1]:

[0] https://github.com/pgcentralfoundation/pgrx

[1] https://github.com/xataio/pgzx

If you like this (I do very much), you might also like pg_idkit[0] which is a little extension with a bunch of other kinds of IDs that you can generate inside PG, thanks to the seriously awesome pgrx[1] and Rust.

[0]: https://github.com/VADOSWARE/pg_idkit

[1]: https://github.com/pgcentralfoundation/pgrx

(disclosure, i work at supabase and have been developing TLEs with the RDS team)

Trusted Language Extensions refer to an extension written in any trusted language. In this case Rust, but it also includes: plpgsql, plv8, etc. See [0]

> PL/Rust is a more performant and more feature-rich alternative to PL/pgSQL

This is only partially true. plpgsql has bindings to low-level Postgres APIs, so in some cases it is just as fast (or faster) than Rust.

> Building a vector index (or any index for that matter) inside Postgres is a more involved process and can not be done via the UDF interface, be it Rust, C or PL/pgSQL

Most PG Rust extensions are written with the excellent pgrx framework [1]. While it doesn't have index bindings right now, I can certainly imagine a future where this is possible[2].

All that said - I think there are a lot of hoops to jump through right now and I doubt it's worth it for the Latern team. I think they are right to focus on developing a separate C extension

[0] TLE: https://supabase.com/blog/pg-tle

[1] pgrx: https://github.com/pgcentralfoundation/pgrx

[2] https://github.com/pgcentralfoundation/pgrx/issues/190#issue...

I’m currently playing with PostgreSQL, foreign data wrappers, and pgrx rust extensions. My development experience has been surprisingly smooth and enjoyable.

My main issue is that joins will be processed locally, so all the foreign data will be fetched before the join happens. But otherwise basic CRUD is easy.

https://wiki.postgresql.org/wiki/Foreign_data_wrappers

https://github.com/pgcentralfoundation/pgrx

https://github.com/supabase/wrappers

I think maybe what you’re really looking for are the files here: https://github.com/pgcentralfoundation/pgrx/tree/c2eac033856...

Those are the internals we currently expose as unsafe “sys” bindings.

As we/contributors identify more that are desired we add them.

pgrx’ focus is on providing safe wrappers and general interfaces to the Postgres internals, which is the bulk of our work and is what will take many years.

As unsafe bindings go, we could just expose everything, and likely eventually will. There’s just some practical management concerns around doing that without a better namespace organization —- something we’ve been working.

The Postgres sources are not small. They are very complex, inconsistent in places, and often follow patterns that are specific to Postgres and not easy to generalize.

If you’ve never built an extension with pgrx, give it a shot one afternoon. It’s very exciting to see your own code running in your database.

pgrx is one of the greatest enabling innovations in the PG ecosystem in a long time.

Awesome to see so many high quality extensions come out of it.

https://github.com/pgcentralfoundation/pgrx

If you're interested in what my Threadripper 3970X does with it, there's some numbers in this PR: https://github.com/tcdi/pgrx/pull/1147

Hi folks, Quickwit cofounder here.

We started Quickwit 3 years ago with a POC, "Searching the web for under $1000/month" (see HN discussions [0]), with the goal of making a robust OSS alternative to Elasticsearch / Splunk / Datadog.

We have reached a significant milestone with our latest release (0.7) [1], as we have witnessed users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy considerable cost savings.

To give you a concrete example, one company is ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit. They divided their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased their durability, accuracy with exactly-once semantics thanks to the native Kafka support, and elasticity.

The 0.7 release also brings better integrations with the Observability ecosystem: improvements of the Elasticsearch-compatible API and better support of OpenTelemetry standards, Grafana, and Jaeger.

Of course, we still have a lot of work to be a fully-fledged observability engine, and we would love to get some feedback or suggestions.

To give you a glance at our 2024 roadmap, we planned to focus on Kibana/OpenDashboard integration, metrics support, and pipe-based query language.

[0] Searching the web for under $1000/month: https://news.ycombinator.com/item?id=27074481

[1] Release blog post: https://quickwit.io/blog/quickwit-0.7

[2] Open Source Repo: https://github.com/quickwit-oss/quickwit

[3] Home Page: https://quickwit.io

We tested S3 Express for our search engine quickwit[0] a couple of weeks ago.

While this was really satisfying on the performance side, we were a bit disappointed by the price, and I mostly agree with the article on this matter.

I can see some very specific use cases where the pricing should be OK but currently, I would say most of our users should just stay on the classic S3 and add some local SSD caching if they have a lot of requests.

[0] https://github.com/quickwit-oss/quickwit/

Hi HN, I’m one of the builders of Quickwit, a cloud-native OSS search engine for observability. As of 2023, we support logs and traces, metrics will come in 2024.

You know the pitch: while software like Datadog or Splunk are great, they often comes with hefty price tags. Our mission is to offer an affordable alternative. So we’ve built Quickwit, we’ve made it compatible with the observabilty ecosystem (OpenTelemetry, Jaeger, Grafana) and above all, we’ve made it cost-efficient / “easy” to scale (well it’s never easy to scale to petabytes..).

To give you a glance at the engine performance I made a benchmark on the GitHub Archive dataset, 23 TB of events, here are the main observations:

Indexing: costs $2 per ingested TB. With 4CPU, throughput is at 20MBs However, you'll observe > 30MB throughput on simpler datasets, like logs and traces.

Search: a typical query costs $0.0002 per TB (considering both CPU time and GET request costs). Using 8CPU, a simple query on 23TB is achieved in under a second.

Storage: on S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $5/ingested TB due to a 2x better compression ratio.

I'm eager to get your thoughts on this!

Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...

Github repo: https://github.com/quickwit-oss/quickwit/

Website: https://quickwit.io/

- On S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $4/ingested TB due to a 2x better compression ratio.

I'm eager to get your thoughts on this!

[0] Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...

Quickwit (https://quickwit.io/) | Paris, France | Onsite and remote (based in Europe) | Full-time

The company is fully remote but we also have a small office in Paris. We prefer candidates based in Europe but can make exceptions for the right profiles.

- Senior Software Engineer 80-110k€ + 0.25-1% equity based on experience.

    We’re looking for a senior software engineer to contribute to [Quickwit](https://github.com/quickwit-oss/quickwit), our open-source search and analytics engine. We have an ambitious roadmap for the next 18 months (performance optimization, distributed storage, support for SQL, query optimizer, revamp of our execution engine, etc.), and this is a great opportunity to shape the future of Quickwit while tackling fun and challenging problems in the field of distributed databases.

In our latest blog post, we walk you through the steps of instrumenting your Rust application and monitoring the performance on Grafana using Quickwit + Jaeger.

Link: https://github.com/quickwit-oss/quickwit