go-sqlite
bleve
go-sqlite | bleve | |
---|---|---|
12 | 13 | |
678 | 9,674 | |
- | 0.7% | |
7.5 | 8.0 | |
2 days ago | 1 day ago | |
Go | Go | |
ISC 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.
go-sqlite
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JSON Canvas – An open file format for infinite canvas data
Check out https://github.com/zombiezen/go-sqlite if you're interested in trying out Sqlite in Go again. Nice interface, negligible compile time impact, fast, compiles without CGO. It's very comfortable.
I agree that going from text to sqlite is a bit of a hurdle, especially if you're not writing C :)
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Jsonfile: A Quick Hack for Tinkering
struggling figuring out how to make my cgo sqlite cross-compile to Windows
Plenty of people trying to fix that.
There's at least:
https://modernc.org/sqlite
Then there's https://github.com/zombiezen/go-sqlite that actually builds https://crawshaw.io/sqlite on top of modernc.
And there's mine that has both a low level and a database/sql driver builds and runs everywhere Go does: https://github.com/ncruces/go-sqlite3
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Any Full Text Search library for json data?
There are several different Go bindings for SQLite. I maintain https://pkg.go.dev/zombiezen.com/go/sqlite
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What’s your preferred setup to work with SQL DB (without ORM) ?
I like and use https://github.com/zombiezen/go-sqlite for CGo-free SQLite. It avoids some of the problems database/sql has, discussed here: https://crawshaw.io/blog/go-and-sqlite.
- SQLite in Go, with and Without Cgo
- A pure Go embedded SQL database
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Containerize Go and SQLite with Docker – 9MB Image Size
> C libraries are required to interact with SQLite
Or: modernc.org/sqlite (https://github.com/zombiezen/go-sqlite), "an automatically generated translation of the original C source code of SQLite into Go"
- Gokrazy – A Native Go Userland
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Library for sqlite3 recommendations?
https://pkg.go.dev/modernc.org/sqlite via https://pkg.go.dev/zombiezen.com/go/sqlite
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New advanced, CGo-free SQLite package
modernc.org/sqlite provides a database/sql driver, but does not (currently) provide an easy way to get at the more advanced functionality of SQLite, like streaming blob I/O or user-defined functions. David Crawshaw has argued that the database/sql API is not a good fit for SQLite, which is how crawshaw.io/sqlite came about.
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 can’t 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 – 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?
go-sqlite3 - sqlite3 driver for go using database/sql
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
bbolt - An embedded key/value database for Go.
elastic - Deprecated: Use the official Elasticsearch client for Go at https://github.com/elastic/go-elasticsearch
distroless - 🥑 Language focused docker images, minus the operating system.
goriak - goriak - Go language driver for Riak KV
bun - SQL-first Golang ORM
elasticsql - convert sql to elasticsearch DSL in golang(go)
bun - Incredibly fast JavaScript runtime, bundler, test runner, and package manager – all in one
goes
sqlite - Go SQLite3 driver
elastigo - A Go (golang) based Elasticsearch client library.