gh-ost VS litestream

I’ve been through multiple incidents where everything worked fine in the testing environment but ended up locking the production database for minutes when deployed. A category of open-source tools called OSC (Online Schema Change) exists to mitigate such pain, like gh-ost used by GitHub and OSC used by Meta. They work by creating a set of "ghost tables" to apply the migrations, copy over old data from the original tables, and catch up with new writes simultaneously. When all old data is migrated, you can trigger a cutover to make the "ghost tables" production. Check the post below for a great introduction and comparison:

Sounds like it's basically explained in the gh-ost readme https://github.com/github/gh-ost#how

I think it amounts to "use views to decouple access to the table with a fixed interface" and "use triggers for migrating data between tables"

Gh-ost is the new hotness. Simple to use and lots of great features: https://github.com/github/gh-ost

If the performance of the db is a concern during migrations (locking, high cpu consumption for large writes) there are tools that can help and do similiar to what your describing but with the benefit that they are battle tested tools. This one spring to mind https://github.com/github/gh-ost there are other options as well and its worth reading the trade off docs

Not sure which version of MySQL you're using, but one approach would be to use a tool like pt-online-schema-change (from Percona) or g-host -- which will create a duplicate table and then swap it in place of the original table. It's a safer approach when operating in production environments. Here's a good comparison of the tools many people use https://planetscale.com/docs/learn/online-schema-change-tools-comparison

No, especially on large tables with billions of records. They make online schema changes impossible. More details: https://github.com/github/gh-ost/issues/331#issuecomment-266...

Tip #4: Consider slow-running migrations. Some tables can be so large that the traditional migration way is simply not a viable option for them. In such cases, you can consider embedding the data migration code right into your application, or use a special utility like GitHub's online schema migration for MySQL. A slow-running migration can work in production for days or even weeks. It gradually converts the data by small chunks, so you can carefully balance the load on the database while making sure that it doesn't cause slowness or downtime.

GitHub gh-ost

Actually I never tried but I was scared by the small print of GH not using RDS themselves [1] and Ghost relying on lower-level features that might be not easily available in RDS. Also I had the impression you have to setup a normal non-RDS replica attached to your RDS master?

[1] https://github.com/github/gh-ost/blob/master/doc/rds.md

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.