go-sstables VS RethinkDB

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.

I've been poking around, testing and breaking database servers for a long time (more than 20 years today). But a few years ago I came across a jewel, the grail, one of the best solutions available. Under the radar, shunned for whatever reason, RethinkDB is nonetheless one of the finest database server projects I've ever tested.

RethinkDB[0] looks like a "too good to be true" type of database. Anyone using it in production? What is your experience like? What are the pros and cons?

[0] https://rethinkdb.com

Since you’re not new to the field you might want to peek https://rethinkdb.com/ since it got picked up as an open source project.

No I don't think that's relevant. They implement their own btree it seems [0].

They don't use a key-value store library.

I know it's a bit of a fine line. But I'm talking about standalone libraries people embed across different applications/databases. That's what RocksDB/LevelDB/Pebble are.

[0] https://github.com/rethinkdb/rethinkdb/tree/v2.4.x/src/btree

A Data Objects represents data which can be saved inside a database. This concept is in the heart of SQLAlchemy, but as the name should be obvious: it's for SQL Database (in general). Today, there are now document databases too (like MongoDB, ArangoDB, RethinkDB that I love so much, or even PostgreSQL). So, a "data" is like a "structured and typed document" that you save "as is". That's not the same paradigm, not the same controls. There are advantages and disadvantages, but we won't debate that here.

There are multiple ways to share data between two (or more) instances of your app: - you can use a shared storage, such as Cloud Firestore, - you can build your own backend (based on a real-time database, such as RethinkDB), - or you can rely on local communication using the Multipeer framework you linked.