sql.js VS SQLite

Hi HN! A friend and I were inspired by projects like https://github.com/sql-js/sql.js and the idea of querying files served over CDN with HTTP range requests. We started thinking: what would a database that was specifically designed for this type of use case look like? So we started building one, and we landed on a functional prototype that we're pretty proud of!

With our prototype, Appendable, we're able to serve and query large (GB+) datasets by hosting them on a static file host like Amazon S3 or Cloudflare R2 without running a separate server and worrying about things like tail latency, replication, and connection pooling -- all that is handled for us by the file hoster.

Additionally, one tenet that we have been following is Appendable won't touch your underlying data, so your jsonl file is preserved and we point at that data instead of consuming it into an Appendable-specific file format. This keeps your data yours and makes it easy to introspect the data: just open it up with your favorite editor aka vim.

We're curious what you think, we're excited to build this out further to get the performance even better and add features like pubsub. Everything is open source at https://github.com/kevmo314/appendable.

Kevin and Matthew

My project is powered by sql.js, I recommend checking that out if you're interested - https://github.com/sql-js/sql.js/

Maybe you can try to use some SQL database compiled as Web Assembly Modules? Like this one for example: https://github.com/sql-js/sql.js

If you want to have persistence, then I would go with a database like Dexie, as it uses IndexedDB and has transactions. If you just want something that's in memory, you could look at Sql.js or something simple like lowdb.

not necessarily. you can host an html/js/sqlite site on github pages for free. json -> sqlite3 js -> sql

Link: https://sql.js.org

Does it work with really large files? Like, >100mb or so. I was considering making something similar but with sqlite.js [1], but the problem with it is that it loads everything in memory, so I wasn't entirely sure how it will deal with larger workloads.

[1]: https://sql.js.org/#/

If you are into alternative storage engines for SQLite, there is also an LSM (Log-Structured Merge-tree) extension in the main repository that is not announced nor documented but seems to work. It’s based on the SQLite 4 project.

https://github.com/sqlite/sqlite/tree/master/ext/lsm1

https://www.charlesleifer.com/blog/lsm-key-value-storage-in-...

The sqlite code base is really well done. Lots of documentation.

https://github.com/sqlite/sqlite

Especially the VM part: https://github.com/spandanb/learndb-py/blob/master/learndb/v...

Compare it with this: https://github.com/sqlite/sqlite/blob/master/src/vdbe.c

That's said, I'm curious how complete this LearnDB is. SQLite is hard to read not only it's old but also it covers a lot of SQL and following SQL spec makes hings complicated. SQLite has great test suite so it's nice if you run the suit against this implementation.

Correct, see the github mirror[1]. I don't know how well supported that feature is compared to main branch. If it was completely stable, then it would have already landed in the main stable branch. Clarity about the roadmap of that branch would be nice.

1. https://github.com/sqlite/sqlite/tree/begin-concurrent

Including comprehensive comments, documentation and tests in a codebase takes time and effort.

Failing to do so creates code that is very difficult to maintain or for someone new to the codebase to understand.

However, time and effort may not be what the organization wants to pay for, and individuals may view their own incomprehensible code as something like job security, as they can't be replaced by someone else easily.

As an example of complicated code that's still well-documented, the open-source sqlite code is a good example, about 1/4 of the B-tree file is comments, every time a variable is defined there's a short note explaining what it's used for, every function has a comment header that's comprehensive, such that someone new to the codebase could construct a map of how it all works fairly quickly. It's a good model for how to avoid the problem:

https://github.com/sqlite/sqlite/blob/master/src/btree.c