PlanetScale Blog

You forgot the read the original text above my response.

> I looked Neon recently, and it appears that it's designed as a SaaS product from the outset; while it is technically possible to self-host the individual components of the architecture, it does not look trivial, in large part because the control plane is closed source (and probably extremely specific to Neon's SaaS operations).

This is a good reason to go with multigres vs Neki (assuming Neki gets integrated into planetscale vs a standalone multigres ).

The two announcement's regarding Neki smells like its going to be proprietary or heavily tied into planetscale. See the gauging interest two months ago with sign ups. The current signing up ... Feel very marketing focused.

https://planetscale.com/blog/planetscale-for-postgres#nova-vitess-for-postgres

> If your company runs Postgres at a significant scale and this is something that interests you, reach out.

> Sign up for the private preview of PlanetScale for Postgres waitlist here.

https://planetscale.com/blog/announcing-neki

> To stay up to date with the latest developments on Neki you can signup at neki.dev.

Where as the multigres via Supabase points to github repo's, the license, etc...

https://supabase.com/blog/multigres-vitess-for-postgres

> Like Vitess, Multigres will be open source using the same license: Apache 2. You can follow the repo here.

We shall see, but one is running and acting like pure open source project, and another is being announced…

Hi, thank you for your work on this and being willing to answer questions on it.

"We guarantee durability via replication". I've starting noticing this pattern more where distributed systems provide durability by replicating data rather than writing it to disk and achieving the best of both worlds. I'm curious

1. Is there a name for this technique?

2. How do you calculate your availability? This blog post[1] has some rough details but I'd love to see the math.

3. I'm guessing a key part of this is putting the replicas in different AZs and assuming failures aren't correlated so you can multiply the probabilities directly. How do you validate that failures across AZs are statistically independent?

Thanks!

[1] https://planetscale.com/blog/planetscale-metal-theres-no-replacement-for-displacement

Check out PlanetScale [0] (if you use MySQL). Their hobby tier is free and very generous. If you get to the point of needing more, $30/mo isn't bad for what you get. It's way better than Aurora Serverless V1 or V2 which I was super excited for but turned out to not be worth it all. It failed scaling so much that I had to crank it up before an event or it would never find a good "migration point" or whatever they called it. Aurora Serverless' marketing is straight lies and their pricing is opposite of what I think of when I hear "serverless", $45/mo per DB if you want a single instance in V1 or 0.5 of one in V2. At least V1 allowed you to scale to 0, they removed that in V2.

[0] https://planetscale.com/

PlanetScale is an awesome DB that handles sharding and size-balancing — plus they have an incredible free tier. https://planetscale.com/

Rust is a memory-safe systems programming language that has earned the title of most beloved language on StackOverflow’s annual developer survey for the past six years running. https://www.rust-lang.org/

These two technologies seemed like a match made in heaven. The best in database technology with an unbeatably fast and secure access layer. There was just one problem…

…no one had ever done it before. Until now! Let us know if you have any questions :)

PlanetScale | https://planetscale.com/ | Software Engineer - PlanetScale Postgres | Remote (AMER, LATAM & EMEA) | Base range: $120,000 - $290,000 USD

I'm the hiring manager for this position.

Come build the best Postgres product on the planet with super talented folks, very high autonomy, tier-zero databases for some of the biggest and fastest growing data sets.

Apply at https://job-boards.greenhouse.io/planetscale/jobs/4251150009.

Feel free to contact me on LinkedIn or Bluesky if you'd like to talk more about the role. Tell me why Postgres extensions are both great and terrible.

(I'm on the team that made this)

The raw numbers are one thing, but the overall performance of pg is another. If you check out https://planetscale.com/blog/benchmarking-postgres-17-vs-18 for example, in the average QPS chart, you can see that there isn't a very large difference in QPS between GP3 at 10k iops and NVMe at 300k iops.

So currently I wouldn't recommend this new storage for the highest end workloads, but it's also a beta project that's still got a lot of room for growth! I'm very enthusiastic about how far we can take this!