natscli VS marmot

Nats provides a nice CLI that can help with debugging: https://github.com/nats-io/natscli

Besides that I'm also working on a UI solution, that will help to get better overview of your cluster: https://qaze.app/

You should just use the `nats` CLI tool (https://github.com/nats-io/natscli/) instead.

This cli is used by NATS cli which itself needs to use json schema to drive it and so shows the concept of using data to drive itself at https://github.com/nats-io/natscli

In this article, we’ll take a look at NATS CLI and learn some basics commands. In my opinion NATS CLI is quite underrated, it offers many features and can help eliminate most of the manual scripts used to manage a NATS server.

Now let’s use the NATS CLI and do a simple benchmark to generate some publish/subscribe events.

Next, we will need the NATS CLI. We can simply install it for Linux from the GitHub releases page.

NATS CLI

While running nats server using cli or docker container is usually the preferred way but in some instances, it can be unnecessary, one such example is testing. While testing, it’s often cumbersome to spin up new instances for external services, this can be completely avoided by using an in-memory server. Luckily, NATS server package provides this functionality out of the box!

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.