bleve
Toshi
bleve | Toshi | |
---|---|---|
13 | 12 | |
9,674 | 4,118 | |
0.7% | 0.4% | |
8.0 | 6.1 | |
about 18 hours ago | 4 months ago | |
Go | Rust | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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bleve
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Hermes v1.7
I don't have the answer to that, but the project has been alive for many years. Seems maybe you should find the answer since you are developing a competing solution? Also it might be a good reference project for solving similar problems to yours. They do have bench tests you could play with https://github.com/blevesearch/bleve/blob/master/query_bench_test.go
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Seeking a free full text search solution for large data with progress display
I know of https://github.com/blevesearch/bleve and I think there was another project for full text search that I can't find now.
- Any Full Text Search library for json data?
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An alternative to Elasticsearch that runs on a few MBs of RAM
I would be interested in such a testbed. I would also like to know how Bleve Search (https://github.com/blevesearch/bleve) turns out.
I have for many years now a small search engine project in my free-time pipeline, but I'm before crawling even and I intend to sit for searching part after some of that.
- What is the coolest Go open source projects you have seen?
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BetterCache 2.0 (has full text search/remove, etc.)
Haha. Seriously I canβt tell the difference between these libraries https://github.com/blevesearch/bleve
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I want to dive into how to make search engines
I've never worked on a project that encompasses as many computer science algorithms as a search engine. There are a lot of topics you can lookup in "Information Storage and Retrieval":
- Tries (patricia, radix, etc...)
- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)
- Consensus (raft, paxos, etc..)
- Block storage (disk block size optimizations, mmap files, delta storage, etc..)
- Probabilistic filters (hyperloloog, bloom filters, etc...)
- Binary Search (sstables, sorted inverted indexes, roaring bitmaps)
- Ranking (pagerank, tf/idf, bm25, etc...)
- NLP (stemming, POS tagging, subject identification, sentiment analysis etc...)
- HTML (document parsing/lexing)
- Images (exif extraction, removal, resizing / proxying, etc...)
- Queues (SQS, NATS, Apollo, etc...)
- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)
- Rate limiting (leaky bucket, windowed, etc...)
- Compression
- Applied linear algebra
- Text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)
- etc...
I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.
- https://github.com/quickwit-oss/tantivy
- https://github.com/valeriansaliou/sonic
- https://github.com/mosuka/phalanx
- https://github.com/meilisearch/MeiliSearch
- https://github.com/blevesearch/bleve
- https://github.com/thomasjungblut/go-sstables
A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. The problem is that search with good rankings often requires custom storage so calculations can be sharded among multiple nodes and you can do layered ranking without passing huge blobs of results between systems.
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Why Writing Your Own Search Engine Is Hard (2004)
For those curious, I'm on my 3rd search engine as I keep discovering new methods of compactly and efficiently processing and querying results.
There isn't a one-size-fits all approach, but I've never worked on a project that encompasses as many computer science algorithms as a search engine.
- Tries (patricia, radix, etc...)
- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)
- Consensus (raft, paxos, etc..)
- Block storage (disk block size optimizations, mmap files, delta storage, etc..)
- Probabilistic filters (hyperloloog, bloom filters, etc...)
- Binary Search (sstables, sorted inverted indexes)
- Ranking (pagerank, tf/idf, bm25, etc...)
- NLP (stemming, POS tagging, subject identification, etc...)
- HTML (document parsing/lexing)
- Images (exif extraction, removal, resizing / proxying, etc...)
- Queues (SQS, NATS, Apollo, etc...)
- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)
- Rate limiting (leaky bucket, windowed, etc...)
- text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)
- etc...
I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.
- https://github.com/quickwit-oss/tantivy
- https://github.com/valeriansaliou/sonic
- https://github.com/mosuka/phalanx
- https://github.com/meilisearch/MeiliSearch
- https://github.com/blevesearch/bleve
A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. That might work for something small like a curated collection of a few hundred sites.
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Mattermost β open-source platform for secure collaboration
Search in SQL databases is a tough beast to get it right. And given that we support MySQL and Postgres both, it gets even harder to support quirks of both of them.
In enterprise editions, the only addition is Elasticsearch. But in our open-source version, we do have support for https://github.com/blevesearch/bleve. Although, it's in beta, we have a lot of customers using it.
I am wondering if you have tried using it and didn't like it?
- A Database for 2022
Toshi
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Tantivy 0.20 is released: Schemaless column store, Schemaless aggregations, Phrase prefix queries, Percentiles, and more...
I don't think you have an active project that addresses all those use cases. There was an attempt in Rust with Toshi that is built on top of tantivy, but the project seems to have stalled.
- An alternative to Elasticsearch that runs on a few MBs of RAM
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Postgres Full Text Search vs. the Rest
I wish we had an extension like ZomboDB but using a lighter search engine like https://github.com/quickwit-oss/quickwit, https://github.com/toshi-search/Toshi and https://github.com/mosuka/bayard
Here I'm listing engines based on https://github.com/quickwit-oss/tantivy - tantivy is comparable to Lucene in its scope - but I'm sure there are other engines that could tackle ElasticSearch.
Another thing that could happen is maybe directly embed tantivy in Postgres using an extension, perhaps this could be an option too.
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Ask HN: Does anybody still use bookmarking services?
I do something similar, though I index the page myself via a little browser extension I wrote. I click a button, the content gets POSTed to a server that throws it in Toshi[1]. I hacked it together on a Saturday, and it's been pretty handy; as you describe, much more useful than any bookmarking approach I've tried before.
[1] https://github.com/toshi-search/Toshi
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*set Edge as default browser*
There is some incredible work being done in the web department, frameworks like rocket.rs and actix.rs are amazing. To get the latest info on web development in Rust, check arewewebyet.org. It doesn't list Toshi though, which is weird.
- Zinc Search engine. A lightweight alternative to elasticsearch that requires minimal resources, written in Go.
- Zinc Search engine. A lightweight alternative to Elasticsearch written in Go
- AWS releases forked Elasticsearch code. Announces new name: OpenSearc
What are some alternatives?
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
elasticsearch-rs - Official Elasticsearch Rust Client
elastic - Deprecated: Use the official Elasticsearch client for Go at https://github.com/elastic/go-elasticsearch
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
goriak - goriak - Go language driver for Riak KV
narg - A tool to generate LC/AP formulas for a given seed in Noita.
elasticsql - convert sql to elasticsearch DSL in golang(go)
sonic - π¦ Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
goes
lnx - β‘ Insanely fast, π Feature-rich searching. lnx is the adaptable, typo tollerant deployment of the tantivy search engine.
elastigo - A Go (golang) based Elasticsearch client library.
OpenSearch - π Open source distributed and RESTful search engine.