phalanx
go-sstables
phalanx | go-sstables | |
---|---|---|
13 | 4 | |
341 | 253 | |
- | - | |
0.0 | 4.0 | |
about 1 year ago | about 2 months ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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?
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
go-sstables
- GitHub - thomasjungblut/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.
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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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What's the big deal about key-value databases like FoundationDB ands RocksDB?
I highly recommend people comfortable with Go checkout the building blocks at https://github.com/thomasjungblut/go-sstables
This codebase shows how SSTables, WAL, memtables, skiplists, segment files, and plenty of other storage engine components work in a digestible way. Includes a demo database showing how it all comes together.
- Understanding LSM Trees: What Powers Write-Heavy Databases
What are some alternatives?
tantivy - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust
search-engines - Reviewing alternative search engines
ipfs-search - Search engine for the Interplanetary Filesystem.
pytai - Kaitai Struct: Visualizer and Hex Viewer GUI in Python
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
hse - HSE: Heterogeneous-memory storage engine
markov - Materials for book: "Markov Chains for programmers"
mitta-screenshot - Mitta's Chrome extension for saving the current view of a website.
search-lib - A library of classes which can be used to build a search engine.
grub-2.0 - Grub is an AI powered Web crawler.
jina - ☁️ Build multimodal AI applications with cloud-native stack