electric VS hash-db

I'm interested in this problem also!

I think there is a large overlap with projects that market/focus on offline-first experiences.

AFAIK this problem can be solved by:

1) Considering a client-side copy of the database that gets synced with the remote DB. This is an approach [PowerSync](https://www.powersync.com/) and [ElectricSql](https://electric-sql.com/) and [rxdb](https://rxdb.info/) take!

Yeah I agree with it being an exaggeration. They are certainly riding the admittedly dated perception that realtime is so hard it's only available to the Googles and Figmas. But there's now some amazing open source solutions available like Y.js and ElectricSQL[1]. The barrier has certainly come down.

[1] https://github.com/electric-sql/electric

https://github.com/electric-sql/electric :

> ElectricSQL is a local-first software platform that makes it easy to develop high-quality, modern apps with instant reactivity, realtime multi-user collaboration and conflict-free offline support.

> Local-first is a new development paradigm where your app code talks directly to an embedded local database and data syncs in the background via active-active database replication. Because the app code talks directly to a local database, apps feel instant. Because data syncs in the background via active-active replication it naturally supports multi-user collaboration and conflict-free offline

"SQLedge: Replicate Postgres to SQLite on the Edge" (2023)

Yup, this is correct.

We have had some requests / discussions around adding hooks to the sync service that will support custom logic on the write path (as per https://github.com/electric-sql/electric/discussions/565). This seems like a good idea but they don't exist yet.

https://github.com/samsquire/hash-db

I need to combine the ideas in each of these projects into a cohesive solution.

I did some work on trying to implement the YATA algorithm, poorly.

From an SQL database implementation perspective, in my toy Python barebones SQL database that barely supports inner joins (https://github.com/samsquire/hash-db) I tested by testing on postgresql and seeing if my query with two joins produces the same results.

I ought to produce unit tests that prove that tuples from each join operation produces the correct dataset.

For a user perspective, I guess you could write some tooling that loads example data into a database and does an incremental join with each part of the join statement added.

I wrote a toy dynamodb, SQL, Cypher graph and document storage database engine in Python for the learning.

https://github.com/samsquire/hash-db

I am interested in database internals. Btrees come up with regard to designing database systems that are efficient to query on disk. Postgres uses them for its indexes. Radix trees are memory efficient tries which are useful for answering prefix queries. They're also called prefix trees. I use them to get a list of prefixes of a string. Useful for simple intellisense style forms or dynamodb style querying. I've also been studying LSM trees which are used in Leveldb and RocksDB.

I experiment with database technology in my experimental project hash-db https://github.com/samsquire/hash-db The code should be readable.

I need to change my search tree to be self balancing currently it grows to the left or right without balancing. I think I need to use tree rotation depending on which branch has the highest height.