beir VS quickwit

The BEIR project might be what you're looking for: https://github.com/beir-cellar/beir/wiki/Leaderboard

Custom datasets can also be evaluated using this method as specified in this link. This article and the associated benchmarks script can be reused to evaluate what method works best on your data.

> Meilisearch focuses on simplicity, relevancy, and performance.

> excellent relevance out of the box

> if ease of use, performance, and relevancy are important to you, Meilisearch was made for you

Is there a benchmark that shows Meilisearch outperforming Elasticsearch in terms of relevance score? I couldn't find Meilisearch listed on https://github.com/beir-cellar/beir.

There are actually benchmarks that allow measuring search relevancy objectively, e.g. BEIR[1]. Manticore Search team did an effort to make a PR to include it to the list. The results are here [2]. Unfortunately the BEIR team seems to be too busy to review a whole pile of PRs including about Vespa. Nevertheless it would be nice to have both Meilisearch and Typesense there too since it's interesting what performance those non-tf-idf based search engines would show compared to BM25-based and vector search engines.

[1] https://github.com/beir-cellar/beir

True! Here's a pull request to BEIR to compare Manticore with Elasticsearch in terms of relevance https://github.com/beir-cellar/beir/pull/92. Spoiler: in this test Manticore provides better relevance than Elasticsearch in average. Of course you can tune both further and Elasticsearch now has KNN which when combined with BM25 can give even better relevance. In general I would say for most users the results quality in terms of full-text relevance is about the same in Elasticseach and Manticore.

But there's for example BEIR that compared BM25 vs state of the art ML language models and it turned out BM25 is in average better than all of them unless you rerank top 100 results from Elasticsearch using the language models. With Manticore you can get even better relevance than with Elasticsearch. We made a pull-request to BEIR to demonstrate that https://docs.google.com/spreadsheets/d/1_ZyYkPJ_K0st9FJBrjbZqX14nmCCPVlE_y3a_y5KkYI/edit#gid=0

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