pgx VS quickwit

It uses pgx (https://github.com/tcdi/pgx) which is our more generalized framework for developing Postgres extensions with Rust.

It's a great question, considering I've been using Rust for a number of years now, and I generally advocate its use for its rich ecosystem, safety and tooling. I actively contribute to pgx, a library for building Postgres extensions in Rust. Yet, Omnigres appears to be all done in C.

Our release today is a framework which extends this functionality to other databases/systems. If you’re familiar with Multicorn[1] or Steampipe[2], then it’s very similar. The framework is written in Rust, using the excellent pgx[3].

We have developed FDWs for Stripe, Firebase, BigQuery, Clickhouse, and Airtable (all in various “pre-release” states). The plan is to focus on the tools we’re using internally while we stabalize the framework.

There’s a lot in the blog post into our goals for this release. It’s early, but one of the things I’m most excited about.

[0] Postgres FDW: [https://www.postgresql.org/docs/current/sql-createforeigndat...

[1] Multicorn: https://multicorn.org/

[2] Steampipe: https://steampipe.io/

[2] pgx: [https://github.com/tcdi/pgx](https://github.com/tcdi/pgx)

BTW one nice easter egg is that with pgx there is actually no reason that we can't build even better search solutions inside the database itself.

> That thread led me to a project/product idea where you take an existing Postgres instance used for normal products or whatever, replicate it to various read only clusters with a custom search extension loaded and some orchestrator sitting on top (I’ve written most of one in rust that uses 0mq to communicate with it’s nodes) and create drop in search from existing databases with a nice guided web gui for automatic tuning suitable for most business use cases.

Very interesting idea -- just want to add one thing, write it in rust (with pgx?[0]) :)

[0]: https://github.com/tcdi/pgx

Hey HN,

It turns out choosing a good database-optimized UUID (and deciding whether to use serial, etc) isn't quite so simple, and I finally got a chance to do some exploration, write about it[0].

One of the reasons Postgres is the best open source database out there is it's extensibility -- so I hacked up a small extension for generating some of the more exotic (but crucially, lexicograhically sortable) UUID generation mechanisms:

https://github.com/t3hmrman/pg_idkit

This idea has been bumbling around my head for a while, but I finally got a chance to build it while working with Supabase on a post about IDs[0]!

Most of the heavy lifting is done by pgx[1] which is an amazing framework for building Postgres extensions in Rust. I think we are very early to the trend of amazing postgres extensions built in Rust, and it's yet another reason that it's an exciting time to be all-in on Postgres.

[0]: https://supabase.com/blog/choosing-a-postgres-primary-key

[1]: https://github.com/tcdi/pgx

[0]: https://supabase.com/blog/choosing-a-postgres-primary-key

The extension is still WIP but for those of ya'll that like Rust it's built on pgx which has excellent DX. The rust involved isn't complicated -- I'm basically laundering the functionality from other crates that are listed in the README.md.

Seems to be using this: https://github.com/tcdi/pgx

- https://github.com/furstenheim/is_jsonb_valid

pgx[0] is going to be pretty revolutionary for the postgres ecosystem I think -- there is so much functionality that can be utilized at the database level and I can't think of a language I want to do it with more than Rust.

[0]: https://github.com/tcdi/pgx

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