sonic VS quickwit

Sonic - a project written in Rust, uses custom network communication protocol for fast communication between the client and the server.

This is uncanny, I just discovered ArchiveBox earlier today and set up a self-hosted instance on some home hardware for a collection of bookmarks of useful guides, tutorials, and references I've collected over the years.

Setting it up on K8s with sonic [1] as the search backend and importing a few hundred URLs only took ~an hour or so, and the cached pages look great for the most part.

[1] https://github.com/valeriansaliou/sonic

If you don't need advanced search features, you can use Sonic (https://github.com/valeriansaliou/sonic). It's blazing fast and you can save lot of money on servers.

What about Sonic? Maybe it requires a bit of integration, but it's simple and blazing fast.

git clone https://github.com/valeriansaliou/sonic.git

- Sonic (https://github.com/valeriansaliou/sonic)

There isn't enough out there comparing all these for the simple typical fuzzy search/search box usecase -- which I think is 80% of people doing search today.

Like other people are pointing out, most of these engines won't have all the features of ES (or more accurately Lucene) but I am pretty convinced that most of the time it doesn't actually matter and if someone is searching on your site excessively maybe there's a problem with your UX (unless you're a search engine or repository of information).

[0]: https://supabase.com/blog/postgres-full-text-search-vs-the-r...

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.

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

- Another nice comment seen on HN « it seems to be very easy to run, not very IO intensive, and running fine on a single node with modest hardware with >2 billion log rows. It has a really cool dynamic schema feature too.» [9]

Fun fact: at least 4 users are using Garage[10] as the object storage, this OSS project looks really promising and made the HN front page a few months ago[11], we really cherish the OSS for this kind of unexpected combination.

Any feedback positive/negative always greatly appreciated here!

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

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

[2] Chitchat gossip library: https://github.com/quickwit-oss/chitchat

[3] Columnar format: https://github.com/quickwit-oss/tantivy/tree/main/columnar

[4] Tantivy library: https://github.com/quickwit-oss/tantivy/

[5] Whichlang library: https://github.com/quickwit-oss/whichlang

[6] GitHub Archive demo in terminal: https://www.youtube.com/watch?v=SNq3bARRlDI

[7] Indexing performance: https://twitter.com/fulmicoton/status/1638016949459488768

[8] https://twitter.com/arnonrgo/status/1645429632303235073?s=20

[9] https://news.ycombinator.com/item?id=35742544

[10] Garage object storage: https://garagehq.deuxfleurs.fr/

[11] https://news.ycombinator.com/item?id=33853539