grub-2.0 VS go-sstables

Not finished, but the Selenium based crawler works pretty well to combat most blocks: https://github.com/kordless/grub-2.0

For IP blocks, try this: https://github.com/kordless/mitta-screenshot

I started https://mitta.us as this, but am pivoting to prompt management for GPT-3. I've Open Sourced the code for the crawler here: https://github.com/kordless/grub-2.0. The entire system uses Google Vision for extracting text. I dislike fiddling with the DOM...

If you are interested in using Solr for this, I can provide instructions to you. I'm kordless at the gmails ... com.

Depending on the use case you might try imaging the page, then send the image to an ML model for full text before indexing. If you need links extracted, Selenium also supports parsing the assembled DOM: https://github.com/kordless/grub-2.0/tree/main/aperture

I’ve found imaging the page and doing OCR on the image is quite good for text extraction. Many pages on the Internet render with JavaScript, which means BS may not see the text in the DOM.

Here is the code to do some of that: https://github.com/kordless/grub-2.0

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.

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.