rupy VS Flyway

I have been running a Raspberry 2 cluster for 10 years: http://host.rupy.se

A few weeks back the first SD card to fail got so corrupted it failed to reboot!

My key learning is use oversized cards, because then the bitcycle will wear slower!

I'm going from 32GB to 256/512/1024!

How this article does not mention SSE, comet or chunking escapes me.

What does their definition of event-driven really look like in practice.

Nobody has a clue.

Here is the ideal event driven system, it's async-to-async: https://github.com/tinspin/rupy/wiki/Fuse

The example is not working because I had to shut down the services for multiple reasons, but the high level of it is that you use 4 (potentially different) threads to do one request/response middle man transaction.

That way you have _zero_ io-wait or idling. I'm surprised nobody has copied this approach since I invented it 10 years ago. I understand why though you need your entire chain to be async and that means rewriting everything and that is a big risk when it's hard to debug.

But if you succeed you can build something that is 10x perf/watt than all other implementations. Which is going to be important when interest rates go higher and crash our entire industry.

The hardware is peaking.

So software is where you can make the difference: http://host.rupy.se

I think we're all confused over the definition. Also one might understand what all the proponents are talking about better if they think about this more as a process and not some technological solution:

https://github.com/tinspin/rupy/wiki/Process

All input I have is you want your code to run on many machines, in fact you want it to run the same on all machines you need to deliver and preferably more. Vertically and horizontally at the same time, so your services only call localhost but in many separate places.

This in turn mandates a distributed database. And later you discover it has to be capable of async-to-async = no blocking ever anywhere in the whole solution.

The way I do this is I hot-deploy my applications async. to all servers in the cluster, this is what a cluster node looks like in practice (the name next to Host: is the node): http://host.rupy.se if you click "api & metrics" you'll see the services.

With this not only do you get scalability, but also redundancy and development is maintained at live coding levels.

I have hosted my own web server both physically and codevise since 2014.

It's on a Raspberry 2 cluster:

http://host.rupy.se

Since 2016 i have my own database also coded from scratch:

http://root.rupy.se

We need to implement HTTP/1.1 with less bloat, a C non-blocking web server that can share memory between threads is probably the most interesting project for humans right now, is anyone working on that?

I have one in Java: https://github.com/tinspin/rupy

Here is the 2000 lines of code of the entire database: http://root.rupy.se/code?path=/Root.java

And here you can try it out: http://root.rupy.se

The smallest PaaS you have ever seen is one order of magnitude larger than mine: https://github.com/tinspin/rupy

And I bet you the same goes for performance, if not two!

The data is here: http://fuse.rupy.se/about.html

Under Performance. Per watt the fuse/rupy platform completely crushes all competition because of 2 reasons:

- Event driven protocol design, averages at about 4 messages/player/second (means you cannot do spraying or headshots f.ex. which is another feature in my game design opinion).

- Java's memory model with atomic concurrency which needs a VM and GC (C++ copied that memory model in C++11, but it failed completely because they lack both VM and GC, but that model is still to this day the one C++ uses), you can read more about this here: https://github.com/tinspin/rupy/wiki

You can argue those points are bad arguments, but if you look at performance per watt with some consideration for developer friendlyness, I'm pretty sure in 100 years we will still be coding minimalist JavaSE on the server and vanilla C (compiled with C++ compiler) on the client.

Also RedGate, but Flyway has some reasons to recommend it over RedGate Deploy depending on your DBAs/workflows: https://flywaydb.org/

(Though I don't think it is "complete" or "perfect", either.)

EF Migrations are in a really good place now if you like/don't mind C# as a language (and you can easily embed SQL inside the C#, too, but there are benefits to being able to also run high level C# code). With today's tooling you can package your migration "runner application" as a single deployable executable for most platforms. You can build the executable once and run it in all your environments. (The same tool that updates your QA and Staging updates your Prod, testably running the same migrations.) Given the single executable deployable I might even consider using it for projects not themselves written in C#.

There is a bit of tooling needed but is already around. For Java for example I had very good experience with a combination of flyway [1] for migrations, testcontainers [2] for making integration tests as easy as unit tests and querydsl [3] for a query and mapping layer.

[1] https://github.com/flyway/flyway

[2] https://java.testcontainers.org/modules/databases/postgres/

[3] https://github.com/querydsl/querydsl

When software starts using a database, it's advisable to have version control, just as we have Github to control our source code. This is all to be sure about what was executed for that specific version. For Java and Spring boot, we have the Flyway framework that aims to resolve this situation, free of charge.

If you're looking for tools, like https://www.liquibase.com/ or https://flywaydb.org/, which are database-state-based schema migration toolkits - it might be relatively straightforward to build similar ones using Databricks SQL drivers.

Honestly I kind of wish there was a Lukas Eder database migration library. Call it whatever jooq-migration. At least I would have more insight of what is going on (<-- seriously look at the commit history).

This is a 4th option, which should play nice with ArgoCD. The following example runs flyway as a k8s job. The desired migration changes are recorded as files within the chart. This helm chart can be integrated with your application (Using hooks to determine when the migration job is run) or run manually.

By using an opinionated framework within the app/service (like Flyway, Migrate, Diesel, etc). Schema migrations happen on app/service start-up.

Depending on the language or platform there are libraries you can use to manage this, such as Prisma on node and Flyway for Java/JVM.

It's probably overkill but I've used github plus flyway at a couple places in the past which is pretty nice tool for tracking changes to a variety of db's, it's also very helpful if you ever need to replicate a db in a new region/environment.

Flyway