kine VS litestream

-- Kine (Kine is not etcd)

Looking at [1], is there any reason why this couldn’t run in something like DynamoDB? I feel like the operations required would fit

https://github.com/k3s-io/kine/blob/master/pkg/drivers/gener...

I don't like some of the decisions that k3s makes which is why I stay away from it, but it's a great project. I'm not using kine yet, but I fantasize about using it exclusively every time I deal with etcd-induced slowness/random CPU spikes on my group of controller nodes. k0s supports kine too of course but I just haven't switched it on yet.

Well in some situations you can also get away with CRDTs or Gossip (SWIM), but yeah in the general sense Paxos (Raft is a Paxos-family algo). There are lots of paxos family algos to choose from[0].

> I’m also currently building a distributed search engine using etcd for the service registry, broker peer announcements, and worker queue and it’s been a good experience so far.

As a random stranger on the internet, please build an abstraction layer around etcd. Even if there's only one implementation, I've found that so many distributed projects that just never reach the scale that etcd is built for would benefit from the option of writing their own drivers (you don't need a full plugin system just a regular abstraction layer).

One example is Kine[0] for k8s -- if k8s had a built in option for writing/reading from something like Postgres from the beginning it would have been a better project for it, IMO.

If you do build a plugin system though, you can pass off the work of maintaining the other implementations!

[0]: https://vadosware.io/post/paxosmon-gotta-concensus-them-all

[1]: https://github.com/k3s-io/kine

I could spent >0s looking into this, but I've been curious how kline implements Etcd's Watch API. For the matter, I don't have much idea in general how kline handles data. Kline would be a great candidate for a blog post or introductory technical article on.

I do quickly see one issue- evidently watches presently use polling. The issue requests use of postgres triggers: https://github.com/k3s-io/kine/issues/20

check out https://github.com/k3s-io/kine

Not to be mistaken for high-availability Dqlite[1], which is one of the options one can run the k3s kubernetes distribution on (instead of etcd), via the Kine etcd shim[2]. Ultimately though the K3s team replaced Dqlite with an embedded etcd to get high-availability[3].

[1] https://dqlite.io/

[2] https://github.com/k3s-io/kine

[3] https://rancher.com/docs/k3s/latest/en/installation/ha-embed...

I have not, but I keep meaning to collate everything I've learned into a set of useful defaults just to remind myself what settings I should be enabling and why.

Regarding Litestream, I learned pretty much all I know from their documentation: https://litestream.io/

This presentation is focused on the use-case of vertically scaling a single server and driving everything through that app server, which is running SQLite embedded within your application process.

This is the sweet-spot for SQLite applications, but there have been explorations and advances to running SQLite across a network of app servers. LiteFS (https://fly.io/docs/litefs/), the sibling to Litestream for backups (https://litestream.io), is aimed at precisely this use-case. Similarly, Turso (https://turso.tech) is a new-ish managed database company for running SQLite in a more traditional client-server distribution.

This post intends to help you setup replication for SQLite using Litestream.

I've been messing around with litestream. It is so cool. And, I either found a bug in the -timestamp switch or don't understand it correctly.

What I want to do is time travel into my sqlite database. I'm trying to do some forensics on why my web service returned the wrong data during a production event. Unfortunately, after the event, someone deleted records from the database and I'm unsure what the data looked like and am having trouble recreating the production issue.

Litestream has this great switch: -timestamp. If you use it (AFAICT) you can time travel into your database and go back to the database state at that moment. However, it does not seem to work as I expect it to:

https://github.com/benbjohnson/litestream/issues/564

I have the entirety of the sqlite database from the production event as well. Is there a way I could cycle through the WAL files and restore the database to the point in time before the records I need were deleted?

Will someone take sqlite and compile it into the browser using WASM so I can drag a sqlite database and WAL files into it and then using a timeline slider see all the states of the database over time? :)

We're using SQLite in production very heavily with millions of databases and fairly high operations throughput.

But we did run into some scariness around trying to use Litestream that put me off it for the time being. Litestream is really cool but it is also very much a cool hack and the risk of database corruption issues feels very real.

The scariness I ran into was related to this issue https://github.com/benbjohnson/litestream/issues/510

Litestream is a library that allows you to easily create backups. You can probably just do analytic queries on the backup data and reduce load on your server.

https://litestream.io/

One possible strategy is to have one directory/file per customer which is one SQLite file. But then as the user logs in, you have to look up first what database they should be connected to.

OR somehow derive it from the user ID/username. Keeping all the customer databases in a single directory/disk and then constantly "lite streaming" to S3.

Because each user is isolated, they'll be writing to their own database. But migrations would be a pain. They will have to be rolled out to each database separately.

One upside is, you can give users the ability to take their data with them, any time. It is just a single file.

[0]. https://litestream.io/

Upstream Kuma uses a local SQLite database to store account data, configuration for services to monitor, notification settings, and more. To make sure that our data is available across redeploys, we will bundle Uptime Kuma with Litestream, a project that implements streaming replication for SQLite databases to a remote object storage provider. Effectively, this allows us to treat the local SQLite database as if it were securely stored in a remote database.