litestack VS marmot

The benchmarks for LiteCache are impressive, with a small caveat. While LiteCache outperforms a local Redis installation for every read operation, it seems like there's still room for improvement, especially for large write payloads.

Luckily, the official LiteStack benchmarks include measurements for LiteCable against Redis, which I am going to quote here.

Let's quickly look into how LiteJob uses SQLite to implement a job queueing system. In essence, the class Litequeue interfaces with the SQLite queue table. This table's columns, like id, name, fire_at, value, and created_at, store and manage job details.

Next, we install LiteStack using the shipped generator:

Related: I wrote a piece last week on deploying Rails apps to production on Fly.io at https://fly.io/ruby-dispatch/sqlite-and-rails-in-production/

The work that’s made this possible is:

1. Litestack - https://github.com/oldmoe/litestack

2. Fly.io’s work on the dockerfile-rails generator detecting Sqlite and Litestack in a Rails project, then setting up sane defaults for where that data is stored and persisted in production. This is all done behind the scenes with no intervention required from the person deploying.

3. Servers are overall faster and more powerful

I hope more Rails hosts make it easier and safer to deploy Sqlite to production. It will lower costs and reduce complexity for folks deploying apps.

Didn't know that one! The litestack.gemspec shows it's a wrapper around the sqlite3 gem. So, not really comparable...

I’m working on this for Rails apps at https://github.com/oldmoe/litestack/pull/12

The idea is that people with small-to-medium size Rails Turbo apps should be able to deploy them without needing Redis or Postgres.

I’ve gotten as far as deploying this stack _without_ LiteFS and it works great. The only downside is the application queues requests on deploy, but for some smaller apps it’s acceptable to have the client wait for a few seconds while the app restarts.

When I get that PR merged I’ll write about how it works on Fly and publish it to https://fly.io/ruby-dispatch/.

Rails is the way to go. The productivity of the Ruby language is insane. It's battle tested for decades and you can easily scale your prototype.

If you want a simple app served on a single host you can try LiteStack [0] so you don't need a Redis/Postgres/Sidekiq instance, just SQLite.

Laravel is also good if you like PHP language.

[0] https://github.com/oldmoe/litestack

If you're willing to accept eventual consistency (a big ask, but acceptable in some scenarios) then there are options like marmot [1] that replicate cdc over nats.

[1]: https://github.com/maxpert/marmot

I've written a whole SQLite replication system that works on top of RAFT ( https://github.com/maxpert/marmot ). Best part is RAFT has a well understood and strong library ecosystem as well. I started of with libraries and when I noticed I am reimplementing distributed streams, I just took off the shelf implementation (https://docs.nats.io/nats-concepts/jetstream) and embedded it in system. I love the simplicity and reasoning that comes with RAFT. However I am playing with epaxos these days (https://www.cs.cmu.edu/~dga/papers/epaxos-sosp2013.pdf), because then I can truly decentralize the implementation for truly masterless implementation. Right now I've added sharding mechanism on various streams so that in high load cases masters can be distributed across nodes too.

Very interesting! I have question ( out of my experience in https://github.com/maxpert/marmot ) how do get around the boot time, specially when a change log of table is pretty large in Postgres? I've implemented snapshotting mechanism in Marmot as part of quickly getting up to speed. At some level I wonder if we can just feed this PG replication log into NATS cluster and Marmot can just replicate it across the board.

One of the reasons I started writing Marmot (https://maxpert.github.io/marmot/) was for replicating bunch of tables across regions that were read heavy. I even used it for cache replication (because who cares if it’s a cache miss, but a hit will save me time and money). It’s hard to make such blue prints in early days of product, and by the time you hit a true growth almost everyone builds a custom solution for multi-region IAM.

Amazing I was just looking for a good mail server to configure for my demo. Which reminds me since you folks have mentioned LiteStream, have you tried Marmot (https://github.com/maxpert/marmot); I recently configured Isso with Marmot to scale it out horizontally (https://maxpert.github.io/marmot/demo). I am super curious what kind of write workload on a sub thousand people organization will have and if Marmot can help scale it horizontally without Foundation DB. I always find the the convenience of SQLite amazing.

Great that you brought it up. I will fill in the perspective of what I am doing for solving this in Marmot (https://github.com/maxpert/marmot). Today Marmot already records changes via installing triggers to record changes of a table, hence all the offline changes (while Marmot is not running) are never lost. Today when Marmot comes up after a long offline (depending upon max_log_size configuration), it realizes that and tries to catch up changes via restoring a snapshot and then applying rest of logs from NATS (JetStream) change logs. I am working on change that will be publishing those change logs to NATS before it restores snapshots, and once it reapplies those changes after restoring snapshot everyone will have your changes + your DB will be up to date. Now in this case one of the things that bothers people is the fact that if two nodes coming up with conflicting rows the last writer wins.

For that I am also exploring on SQLite-Y-CRDT (https://github.com/maxpert/sqlite-y-crdt) which can help me treat each row as document, and then try to merge them. I personally think CRDT gets harder to reason sometimes, and might not be explainable to an entry level developers. Usually when something is hard to reason and explain, I prefer sticking to simplicity. People IMO will be much more comfortable knowing they can't use auto incrementing IDs for particular tables (because two independent nodes can increment counter to same values) vs here is a magical way to merge that will mess up your data.