uip VS rupy

As the other commenter pointed out, uIP seems to be from 9 years ago. From the issues in the project, I found it is part of Contiki. Are you developing using Contiki?

The CPU architecture is actually the least of your concerns there—I'm pretty sure qemu-user can run httpdito on ARM with less than an order of magnitude performance overhead. There are a lot of embedded systems where an HTTP transaction per second per MHz would be more than sufficient.

The bigger problem is that the Raspberry Pico is a dual-core Cortex-M0+, which doesn't have an MMU, so it can't run Linux and especially can't handle fork(). But httpdito is basically scripting the Linux system call interface in assembly language—it needs to run on top of a filesystem, an implementation of multitasking that provides allocation of different memory to different tasks, and a TCP/IP stack. Any one of these is probably a larger amount of complexity than the 296 CPU instructions in httpdito.

The smallest TCP/IP stack I know of is Adam Dunkels's uIP. Running `sloccount .` in uip/uip cloned from https://github.com/adamdunkels/uip gives a count of 2796 lines of source code ("generated using David A. Wheeler's 'SLOCCount'."). uIP can run successfully on systems with as little as 2KiB of RAM, as long as you have somewhere else to put the code, but for most uses lwIP is a better choice; it minimally needs 10KiB or so. uIP is part of Dunkels's Contiki, which includes a fairly full-featured web server and a somewhat less-full-featured browser. I think he got both the server and the browser to run in 16KiB of RAM on a Commodore PET, but not at the same time.

(twIP http://dunkels.com/adam/twip.html is only 139 bytes of C source but doesn't support TCP or any physical-layer protocol such as Ethernet, PPP,or SLIP.)

However, Adam Dunkels has also written Miniweb http://dunkels.com/adam/miniweb/, which implements HTTP and enough of TCP and IP to support it, in 400 lines of C. It needs at least 30 bytes of RAM. Like twIP, it doesn't provide a physical layer. But that's solvable.

I am currently reviewing existing TCP/IP stacks for microcontrollers, and in particular uIP.

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.