Table of contents
High Performance
Tantivy is designed for speed and efficiency, enabling fast indexing and retrieval of large datasets. It achieves high throughput and low latency through optimized data structures and algorithms.
Feature Rich
Tantivy supports a wide range of search features such as full-text search, faceting, and filtering, making it suitable for diverse search applications.
Rust Implementation
Being implemented in Rust, Tantivy benefits from Rust's safety guarantees, which reduce the likelihood of common programming errors. Rust's performance is comparable to C++, while offering better memory safety.
Open Source
As an open-source project, Tantivy is freely available for use and modification. It has an active community, which contributes to its development and support.
Scalability
Tantivy is designed for scalability, making it suitable for handling large volumes of data and supporting distributed search architectures.
We have collected here some useful links to help you find out if Tantivy is good.
Check the traffic stats of Tantivy on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Tantivy on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Tantivy's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Tantivy on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Tantivy on Reddit. This can help you find out how popualr the product is and what people think about it.
The search engine is Tantivy, a Rust-native Lucene-class full-text indexer. Search response times across 1,500 sessions stay under 50ms. - Source: dev.to / 4 months ago
Tantivy is Rust's answer to Lucene. I use it as the local search engine that indexes everything into a single queryable index. - Source: dev.to / 6 months ago
> Acts as both the primary storage for email content and the full-text search index. This unified approach ensures high performance and eliminates data redundancy. [1] https://github.com/quickwit-oss/tantivy. - Source: Hacker News / 7 months ago
Adds a ton of Lucene-like features, based on , a Rust-based Lucene alternative. - Source: dev.to / about 1 year ago
Meilisearch is great, used it for a quick demo However if you need a full-text search similar to Apache Lucene, my go-to options are based on Tantivy Tantivy https://github.com/quickwit-oss/tantivy Asian language, BM25 scoring, Natural query language, JSON fields indexing support are all must-have features for me Quickwit. - Source: Hacker News / over 1 year ago
Congratulations! The fact you and your team managed to built Tantivy is a huge contribution to open source. As someone who never managed to built a fond relationship with Apache Lucene based products (Solf, Elastic). I was extremely happy to see Tantivy in open source. BM25 scoring, proper asian language support, speed memory foot prints, etc - amazing job! Thank you so much! - Source: Hacker News / over 1 year ago
This comment deserve an article! Just finished a call a few mins, and we came to conclusion we do natural query language, BM25 scoring with Tantivy based code first https://github.com/quickwit-oss/tantivy In meanwhile we collect all questions to ask LLM so we can be more consious at Hybrid Search implementation phase. - Source: Hacker News / over 1 year ago
Our new text match capability leverages Tantivy's analyzers and indexing for precise natural language matching. This feature is particularly valuable when combining text matching with vector similarity search, enabling you to filter search results based on specific terms before performing vector similarity calculations. - Source: dev.to / over 1 year ago
Fun fact: We've implemented binary embedding search [1] without the need for a specialized vector database. Instead, we create dimensional tokens like 'embedding_0_0', 'embedding_1_0', and so on, and we harness the robust capabilities of Tantivy [2]. We're really satisfied with the exceptional quality and performance this approach yields. Moreover, Tabby remains a single binary, integrating all these components... - Source: Hacker News / about 2 years ago
| Hm, I am interested, but I would love to use it as a rust lib and just have rust types instead of some json config... Yes that's how you use tantivy normally, not sure which json config you mean. `tantivy-cli` is more like a showcase, https://github.com/quickwit-oss/tantivy is the actual project. - Source: Hacker News / about 2 years ago
Tantivy - a full-text indexing library written in Rust. Has a great Performance and featureset. - Source: dev.to / over 2 years ago
By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructured blobs of data. Another alternative is to use Tantivy (https://github.com/quickwit-oss/tantivy), a Rust version of Lucene, if building search indices via Java isn't your cup of tea... - Source: Hacker News / over 2 years ago
We also implemented our schemaless columnar storage optimized for object storage. The inverted index and columnar storage are part of tantivy [0], which is the fastest search library out there. We maintain it and we decided to build the distributed engine on top of it. [0] tantivy github repo: https://github.com/quickwit-oss/tantivy. - Source: Hacker News / over 2 years ago
Search index : Custom-built using tantivy. Source: almost 3 years ago
Hi /r/rust, Iโm a SWE on Etsyโs Retrieval Systems team where weโre building a platform based on rust and tantivy (https://github.com/quickwit-oss/tantivy). Weโre looking to bring two new engineers onto the team. Source: about 3 years ago
Quick Search: Easily find specific notes with Velo's fuzzy-search feature, powered by tantivy. Tantivy might have been a little overkill, but it was really easy to integrate. Source: about 3 years ago
Two years after, we are finally reaching a version that can deliver our promise. Two years is both very long for a startup and very short when building a distributed engine. And we decided to do it the hard way: we implemented our own OSS gossip library, our own {S3,JSON}-friendly columnar format for schemaless analytics, and of course, we maintain our own search library, tantivy. This is a lot of engineering... Source: about 3 years ago
- Another nice comment seen on HN ยซ 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.ยป [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... - Source: Hacker News / about 3 years ago
It's mostly Rust with a sprinkling of python for some of the ML stuff. The transcription is done via whisper (https://github.com/ggerganov/whisper.cpp) and the search is handle via standard lexical search (https://github.com/quickwit-oss/tantivy) combined with a vector database (https://qdrant.tech) to find relevant pieces of content. Source: about 3 years ago
Good project, also thanks for being open about how you make use of Tantivy. Source: over 3 years ago
From my little knowledge of Loki's internals. I think contrary to Loki, Quickwit uses a fully feature search engine library underneath called Tantivy (https://github.com/quickwit-oss/tantivy). - Source: Hacker News / over 3 years ago
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