bayard
bleve
bayard | bleve | |
---|---|---|
4 | 13 | |
1,839 | 9,674 | |
0.0% | 0.7% | |
0.0 | 8.0 | |
about 1 year ago | 5 days ago | |
Rust | Go | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
bayard
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An alternative to Elasticsearch that runs on a few MBs of RAM
Somewhat related, this guy: https://github.com/mosuka/ seems to be very passionate about search service.
He built two distributed search services:
- https://github.com/mosuka/phalanx, written in Go.
- https://github.com/mosuka/bayard, written in Rust.
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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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Meilisearch, the Rust search engine, just raised $5M
So there's more than one? The one I knew was https://github.com/quickwit-oss/tantivy and https://github.com/quickwit-oss/quickwit on top of it (there's a couple of other search engines built on top of tantivy, like https://github.com/bayard-search/bayard)
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
What are some alternatives?
quickwit - Cloud-native search engine for observability. An open-source alternative to Datadog, Elasticsearch, Loki, and Tempo.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
tantivy - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust
elastic - Deprecated: Use the official Elasticsearch client for Go at https://github.com/elastic/go-elasticsearch
sonic - π¦ Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
goriak - goriak - Go language driver for Riak KV
zincsearch - ZincSearch . A lightweight alternative to elasticsearch that requires minimal resources, written in Go.
elasticsql - convert sql to elasticsearch DSL in golang(go)
quickwit - Quickwit is a fast and cost-efficient distributed search engine for large-scale, immutable data. [Moved to: https://github.com/quickwit-oss/quickwit]
goes
lyra - π Fast, in-memory, typo-tolerant, full-text search engine written in TypeScript. [Moved to: https://github.com/LyraSearch/lyra]
elastigo - A Go (golang) based Elasticsearch client library.