hash-db VS cr-sqlite

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.

I'm a happy user of https://github.com/vlcn-io/cr-sqlite/

If you're interested in this, here are some related projects that all take slightly different approaches:

- LiteSync directly competes with Marmot and supports DDL sync, but is closed source commercial (similar to SQLite EE): https://litesync.io

- dqlite is Canonical's distributed SQLite that depends on c-raft and kernel-level async I/O: https://dqlite.io

- cr-sqlite is a Rust-based loadable extension that adds CRDT changeset generation and reconciliation to SQLite: https://github.com/vlcn-io/cr-sqlite

Slightly related but not really (no multi writer, no C-level SQLite API or other restrictions):

- comdb2 (Bloombergs multi-homed RDMS using SQLite as the frontend)

- rqlite: RDMS with HTTP API and SQLite as the storage engine, used for replication and strong consistency (does not scale writes)

- litestream/LiteFS: disaster recovery replication

- liteserver: active read-only replication (predecessor of LiteSync)

Theory is great, but how can we apply this in practice? Instead of starting from 0, and writing a CRDT, let's try and leverage an existing project to do the heavy lifting. My choice is crSQLITE, an extension for SQLite to support CRDT merging of databases. Under the hood, the extension creates tables to track changes and allow inserting into an event log for merging states of separated peers.

Also https://github.com/vlcn-io/cr-sqlite/ which is SQLite + CRDTs

Runs/syncs to the browser too which is just lovely.

If you need multiple writers and can handle eventual correctness, you should really be using cr-sqlite[1]. It'll allow you to have any number of workers/clients that can write locally within the same process (so no network overhead) but still guarantee converge to the same state.

[1] https://github.com/vlcn-io/cr-sqlite

I am fully on the offline-first bandwagon after starting to use cr-sqlite (https://vlcn.io), which works similar to ElectricSQL.

I thought the bundle size of wasm-sqlite would be prohibitive, but it's surprisingly quick to download and boot. Reducing network reliance solves so many problems and corner-cases in my web app. Having access to local data makes everything very snappy too - the user experience is much better. Even if the user's offline data is wiped by the browser (offline storage limits are a bit of a minefield), it is straightforward to get all synced changes back from the server.

I didn't know that. Especially the first approach sounds interesting to me, because as far as I know the transactions of Yjs seem to be a problem on heavily changing documents. https://github.com/vlcn-io/cr-sqlite#approach-1-history-free... Thanks!

I wonder how this compares to https://vlcn.io?

The short ask: Anyone know of any projects that bring incremental view maintenance to SQLite?

The why:

Applications are usually read heavy. It is a sad state of affairs that, for these kinds of apps, we don't put more work on the write path to allow reads to benefit.

Would the whole No-SQL movement ever even have been a thing if relational databases had great support for materialized views that updated incrementally? I'd like to think not.

And more context:

I'm working to push the state of "functional relational programming" [1], [2] further forward. Materialized views with incremental updates are key to this. Bringing them to SQLite so they can be leveraged one the frontend would solve this whole quagmire of "state management libraries." I've been solving the data-sync problem in SQLite (https://vlcn.io/) and this piece is one of the next logical steps.

If nobody knows of an existing solution, would love to collaborate with someone on creating it.

[1] - https://github.com/papers-we-love/papers-we-love/blob/main/design/out-of-the-tar-pit.pdf