Comment by andersmurphy
9 hours ago
> SQLite on the same machine is akin to calling fwrite.
Actually 35% faster than fwrite [1].
> This is also a system constraint as it forces a one-database-per-instance design
You can scale incredibly far on a single node and have much better up time than github or anthropic. At this rate maybe even AWS/cloudflare.
> you need to serve traffic beyond your local region
Postgres still has a single node that can write. So most of the time you end up region sharding anyway. Sharding SQLite is straight forward.
> This is fine if you're putting together a site for your neighborhood's mom and pop shop, but once you need to handle a request baseline beyond a few hundreds TPS
It's actually pretty good for running a real time multiplayer app with a billion datapoints on a 5$ VPS [2]. There's nothing clever going on here, all the state is on the server and the backend is fast.
> but you're now compelled to find "clever" strategies to sync state across nodes.
That's the neat part you don't. Because, for most things that are not uplink limited (being a CDN, Netflix, Dropbox) a single node is all you need.
May be an "out" there question, but any tech book suggestions you'd recommend that can teach an average dev on how to build highly performant software with minimal systems?
I feel like the advice from people with your experience is worth way way way way more than what you'd hear from big tech. Like what you said yourself, big tech tends to recommend extremely complicated systems that only seem worth maintaining if you have a trillion dollar monopoly behind it.
How do you manage HA?
Backups, litestream gives you streaming replication to the second.
Deployment, caddy holds open incoming connections whilst your app drains the current request queue and restarts. This is all sub second and imperceptible. You can do fancier things than this with two version of the app running on the same box if that's your thing. In my case I can also hot patch the running app as it's the JVM.
Server hard drive failing etc you have a few options:
1. Spin up a new server/VPS and litestream the backup (the application automatically does this on start).
2. If your data is truly colossal have a warm backup VPS with a snapshot of the data so litestream has to stream less data.
Pretty easy to have 3 to 4 9s of availability this way (which is more than github, anthropic etc).
My understanding is litestream can lose data if a crash occurs before the backup replication to object storage. This makes it an unfair comparison to a Postgres in RDS for example?
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> Backups, litestream gives you streaming replication to the second.
You seem terribly confused. Backups don't buy you high availability. At best, they buy you disaster recovery. If your node goes down in flames, your users don't continue to get service because you have an external HD with last week's db snapshots.
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No offense, you wait. Like everyone's been doing for years in the internet and still do
- When AWS/GCP goes down, how do most handle HA?
- When a database server goes down, how do most handle HA?
- When Cloudflare goes down, how do most handle HA?
The down time here is the server crashed, routing failed or some other issue with the host. You wait.
One may run pingdom or something to alert you.
> When AWS/GCP goes down, how do most handle HA?
This is a disingenuous scenario. SQLite doesn't buy you uptime if you deploy your app to AWS/GCP, and you can just as easily deploy a proper RDBMS such as postgres to a small provider/self-host.
Do you actually have any concrete scenario that supports your belief?
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> You can scale incredibly far on a single node
Nonsense. You can't outrun physics. The latency across the Atlantic is already ~100ms, and from the US to Asia Pacific can be ~300ms. If you are interested in performance and you need to shave off ~200ms in latency, you deploy an instance closer to your users. It makes absolutely no sense to frame the rationale around performance if your systems architecture imposes a massive performance penalty in networking just to shave a couple of ms in roundtrips to a data store. Absurd.
You need regional state, or you're still back hauling to the db with all the lag.
That only solves read latency not write latency. Unless you don't care about consistency.