phalanx
Toshi
phalanx | Toshi | |
---|---|---|
13 | 12 | |
341 | 4,117 | |
- | 0.4% | |
0.0 | 6.1 | |
about 1 year ago | 3 months ago | |
Go | Rust | |
Apache License 2.0 | MIT License |
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phalanx
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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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What is the coolest Go open source projects you have seen?
Donβt forget about Phalanx if you like Bleve/Bluge.
- Cloud-native distributed search engine written in Go
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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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Show HN: I built a self hosted recommendation feed to escape Google's algorithm
Is there a tool that automatically forwards every URL + HTML of the page you visit to a webhook so you could write an endpoint that would index everything?
If not, I would love to see this add a "forward to webhook" option. I would be happy to write up a real backend that parsed the content and indexed it.
Actually, there are lots of OS projects for this: https://github.com/quickwit-oss/tantivy, https://github.com/valeriansaliou/sonic, https://github.com/mosuka/phalanx, https://github.com/meilisearch/MeiliSearch, etc...
- Phalanx is a cloud-native distributed search engine with REST API written in Go
- Phalanx v0.3.0, a distributed search engine written in Go, has been released
- Phalanx 0.2.0, a distributed search engine written in Go, has been released
- Phalanx - A cloud-native full-text search and indexing server written in Go built on top of Bluge
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?
tantivy - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust
elasticsearch-rs - Official Elasticsearch Rust Client
ipfs-search - Search engine for the Interplanetary Filesystem.
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
narg - A tool to generate LC/AP formulas for a given seed in Noita.
markov - Materials for book: "Markov Chains for programmers"
sonic - π¦ Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
go-sstables - Go library for protobuf compatible sstables, a skiplist, a recordio format and other database building blocks like a write-ahead log. Ships now with an embedded key-value store.
lnx - β‘ Insanely fast, π Feature-rich searching. lnx is the adaptable, typo tollerant deployment of the tantivy search engine.
search-engines - Reviewing alternative search engines
OpenSearch - π Open source distributed and RESTful search engine.