rupy VS lowdefy

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.

I'm really enjoying reading through the docs and the tutorial. We've created Lowdefy, a config web-stack which makes it really simple to build quite advanced web apps. We're writing everything in YAML, but it has it's limitations, specifically when doing config type checking and IDE extensions that go beyond just YAML.

I've been looking for a way to have typed objects in the config to do config suggestions and type checking.. PKL looks like it can do this for us. And with the JSON output we might even be able to get there with minimal effort.

Is there anyone here with some PKL experience that would be willing to answer some technical questions re the use of PKL for more advanced, nested config?

See Lowdefy:

https://lowdefy.com/

https://github.com/lowdefy/lowdefy

Awsome! With Lowdefy we tried to build a low-code framework that works like code. We’ve developed a schema in which to define applications and we’ve built all kinds of apps for enterprise customers. Massive, advanced CRM systems, call centre solutions, ticketing systems, a light MRP, all kinds of survey apps and so many dashboards. Even our docs and our website are Lowdefy apps!

Give Lowdefy a try and reach out it you have any questions or want to see what is possible :) (We need to invest a lot more into content and examples, bootstapping is a grind!)

https://github.com/lowdefy/lowdefy

Also add Lowdefy onto the list https://github.com/lowdefy/lowdefy

co-founder here :)

100% this. And yes, good documentation takes a lot of investment but it pays off like compound interest. But with that done, it becomes even more important not to pull the carpet for no good reason, you are building a tower and documentation is at the foundation.

We’ve built Lowdefy [1] as an open source project and documented it with all effort, 200 pages of docs. I often forget why or how something works and then jump to the docs. This investment keeps on paying of as we use Lowdefy to build customer apps, new devs in the team typically take less than two week to get up to speed and start making contributions, the sharp ones, just a two or three days.

This year, we’re extended our documentation onto customer apps aswell, with flow diagrams, state machine definitions, detailed field level explication schema definitions, and end user test procedures. The key here for this documentation is detail. It should be easier to reach for the docs and the the answer, than to dive in the code and interpret it.

1 - https://github.com/lowdefy/lowdefy

https://github.com/lowdefy/lowdefy Co-Founder here.

Check out https://lowdefy.com/ they even have a sample survey app as one of their examples.

You can build an app that would do this

Lowdefy is a low code tool that uses AG Grid as a block component, allowing you to create apps which render data in AG Grid without a lot of coding knowledge. There is a Lowdefy example using AG Grid here.

Shameless plug, also check out Lowdefy - https://github.com/lowdefy/lowdefy

I’m also going to jump in here and say try Lowdefy https://github.com/lowdefy/lowdefy - co-founder here.

We take a different angle and believe that low code should still work like code. We focus on a developer first approach.