phalanx VS search-engines

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.

Don’t forget about Phalanx if you like Bleve/Bluge.

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.

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.

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...

I'm in a similar bandwagon. I just started collecting search engines and analyzing them. I've listed some of them at https://github.com/Tintedfireglass/search-engines and what they le it is easy to look at search algorithms, queries and User interfaces I still don't understand how to create one

I had the same problem so started this project, currently it just lists 70search engines, planning to review them after summer https://github.com/Tintedfireglass/search-engines