go-sstables
pytai
go-sstables | pytai | |
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4 | 2 | |
255 | 122 | |
- | - | |
4.0 | 4.8 | |
2 months ago | 4 months ago | |
Go | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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
pytai
What are some alternatives?
search-engines - Reviewing alternative search engines
phalanx - Phalanx is a cloud-native distributed search engine that provides endpoints through gRPC and traditional RESTful API.
hse - HSE: Heterogeneous-memory storage engine
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.
jina - ☁️ Build multimodal AI applications with cloud-native stack
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
now - 🧞 No-code tool for creating a neural search solution in minutes
Milvus - A cloud-native vector database, storage for next generation AI applications
Yacy - Distributed Peer-to-Peer Web Search Engine and Intranet Search Appliance