phalanx
orama
phalanx | orama | |
---|---|---|
13 | 12 | |
341 | 8,059 | |
- | 2.9% | |
0.0 | 9.4 | |
about 1 year ago | 7 days ago | |
Go | TypeScript | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
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?
Don’t 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
orama
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Vector Search is Eating the Web
Orama, an open-source, edge-first hybrid search engine highlights the industry's shift towards more efficient, accurate, and scalable solutions. Recent trends indicate a shift from traditional search solutions to more modern and efficient answering engines like Orama, evidenced by the search features on both Node.js and SolidJS that were formerly powered by Algolia, but are now powered by Orama.
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Sky's the Limit! Supercharging Your Astro Blog with Orama, the Ultimate Stargazing Search Engine!
Let's break into the steps to utilize Orama and analyze how it works. I won't dig into the technical stuff because, hey, it's an open-source project, which means you can easily peek at the source code, no problemo!
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OramaSearch, a full-text search in your React application
If you are interested in it, you can learn more about it in the official documentation. And don't forget to follow Orama on Twitter and Michere Riva its CTO.
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Why I love GitLens in my VsCode - Part 1
I'll use the Lyra repository for this article, so thanks to the Lyra contributors if this article has a great git history and awesome code.
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What is your go to client-side fuzzy searching library?
You can checkout lyra, its in-memory full text search engine for javascript
- An alternative to Elasticsearch that runs on a few MBs of RAM
- Lyra
- Lyra: Fast, in-memory, typo-tolerant, full-text search engine in TypeScript
What are some alternatives?
tantivy - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust
flexsearch - Next-Generation full text search library for Browser and Node.js
ipfs-search - Search engine for the Interplanetary Filesystem.
Lyra - A simple to use, composable, command line parser for C++ 11 and beyond
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
minisearch - Tiny and powerful JavaScript full-text search engine for browser and Node
markov - Materials for book: "Markov Chains for programmers"
regex-benchmark - It's just a simple regex benchmark of different programming languages.
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.
elasticsearch-py - Official Python client for Elasticsearch
search-engines - Reviewing alternative search engines
re.places - An in-cache, searchable database of 41,000 global cities. It’s designed as a light-weight polyfill for ‘cities’ in Algolia's places API, for when it sunsets in May 2022