ideas4 VS PyO3

https://github.com/samsquire/ideas4#334-knowledgegraph-progr...

How would you do control flow and scheduling and parallelism and async efficiently with this code?

`db.save()`, `download()` are IO intensive whereas `document.query("a")` and `parse` is CPU intensive.

I think its work diagram looks like this: https://github.com/samsquire/dream-programming-language/blob...

I've tried to design a multithreaded architecture that is scalable which combines lightweight threads + thread pools for work + control threads for IO epoll or liburing loops:

Here's the high level diagram:

https://github.com/samsquire/ideas5/blob/main/NonblockingRun...

The secret is modelling control flow as a data flow problem and having a simple but efficient scheduler.

I wrote about schedulers here and binpacking work into time:

https://github.com/samsquire/ideas4#196-binpacking-work-into...

I also have a 1:M:N lightweight thread scheduler/multiplexer:

https://github.com/samsquire/preemptible-thread

My blogging/journalling setup is simple.

I just use GitHub. I just rely on the default repository view on GitHub.com

I create a README.md and add markdown headings to the bottom or to the top (bottom if its a journal, top if it's a blog) and then when I get to 100-800 I create a new repository and repeat.

https://github.com/samsquire/ideas (2013)

https://github.com/samsquire/ideas4

https://github.com/samsquire/ideas3

https://github.com/samsquire/ideas2

Thanks for posting this Ask HN question.

I journal ideas and thoughts about computers and software. I am interested in software architecture, parallelism, async, coroutines, database internals, programming language implementation, software design and the web.

https://github.com/samsquire/ideas (2013)

https://github.com/samsquire/ideas2

https://github.com/samsquire/ideas3

https://github.com/samsquire/ideas4 <-- this is recent but needs editing

https://github.com/samsquire/ideas5 <-- this is what I'm working on now

https://github.com/samsquire/startups

https://github.com/samsquire/blog <-- thoughts I want to write about, but incomplete

I use README.md on GitHub and create a heading at the bottom for each entry. I use Typora on Windows or the GitHub web interface to edit.

My deep interest is multithreaded code. For a software engineer working on business software, I'm not sure if they should be spending too much time debugging multithreaded bugs because they are operating at the wrong level of abstraction from my perspective for business operations.

I'm looking for an approach to writing concurrent code with parallelism that is elegant and easy to understand and hard to introduce bugs. This requires alternative programming approaches and in my perspective, alternative notations.

One such design uses monotonic state machines which can only move in one direction. I've designed a syntax and written a parser and very toy runtime for the notation.

https://github.com/samsquire/ideas5#56-stateful-circle-progr...

https://github.com/samsquire/ideas4#558-state-machine-formul...

The idea is inspired by LMAX Disruptor and queuing systems.

This is really good. Thank you!

I've been studying how to create an asynchronous runtime that works across threads. My goal: neither CPU and IO bound work slow down event loops.

I've only written two Rust programs but in Rust you presumably you can use Rayon (CPU scheduling) and Tokio (IO scheduling)

I wrote about using the LMAX Disruptor ringbuffer pattern between threads.

https://github.com/samsquire/ideas4#51-rewrite-synchronous-c...

I am designing a state machine formulation syntax that is thread safe and parallelises effectively. It looks like EBNF syntax or a bash pipeline. Parallel steps go in curly brackets. There is an implied interthread ringbuffer between pipes.

  states = state1 | {state1a state1b state1c} {state2a state2b state2d} | state3

This is very interesting and could lead to some futuristic programming technology.

I kind of want to plot the state space of a program to see all available states.

In my exploration of distributed systems, microservices and multithreaded systems, it is extremely helpful to try and see what potential states the system can be in. Global and local reasoning of these kinds of software is rather difficult.

I've written about value tracing but I've not heard of treating values as types. I would love to be able to see the trajectory of a value through different states.

https://github.com/samsquire/ideas4#571-value-calculus-varia...

I've never written a TLA+ specification and I'm a complete beginner to this space but I've been trying to understand the dining philosophers one. TLA+ Toolbox is aware of discrete states in the state space, which is absolutely awesome. Types can inform us about future possible valid states.

I began writing a visualisation of memory and animated the movement of memory around to try reveal patterns.

https://replit.com/@Chronological/ProgrammingRTS#index.html

If we see types or values as positions, we can create animations of the state space unfolding in front of us. This is the dream.

Great comment!

>I think late architecture is orthogonal to functional, imperative

Absolutely. From a truly architectural view, procedural, functional, and method-oriented (current OO) are really only variations on the call/return architectural style. Good and sometimes important distinctions, but not really that far apart. They are very much about computing, results from inputs. That is an appropriate architecture for fewer and fewer programs.

See Why Architecture Oriented Programming matters

https://blog.metaobject.com/2019/02/why-architecture-oriente...

and

Can Programmers Escape the Gentle Tyranny of call/return?

https://2020.programming-conference.org/details/salon-2020-p...

> its solution is higher level than even functional programming

Yes. Well, functional actually gets most of its utility from being lower level as far as paradigms go (less powerful). But yes.

> and more abstract

No. Well, yes, if expressed with current programming languages. But that's part of the problem set, not part of the solution set. We should be able to express our architectures less abstractly, more concretely, but for that we need linguistic support. Which is why I am working on that:

http://objective.st

> I want software architecture to be cheap and easy to change without breaking any existing behaviours. I don't know much research on this subject.

There was quite a bit of research at CMU, for example on packaging mismatch. Famous paper Architectural Mismatch, Why Reuse is so hard, and the 10 year follow up in 2009: Architectural Mismatch: Why Reuse is Still So Hard

https://repository.upenn.edu/cgi/viewcontent.cgi?article=107...*

Not much has changed since.

> https://github.com/samsquire/ideas4

> https://devops-pipeline.com

Will check those out. Dataflow is definitely a big part of it, with the extension of dataflow constraints (make, spreadsheets, "FRP"/"Rx"). But so is in-process REST with Storage Combinators!

And breaking down barriers between scripting and "real" programming.

Thanks for this.

I have never deployed a server mesh or used one but I am designing something similar at the code layer. It is designed to route between server components. That is, at the architecture between threads in a multithreaded system.

The problem I want to solve is that I want architecture to be trivially easy to change with minimal code changes. This is the promise and allure of enterprise service buses and messaging queues.

I have managed RabbitMQ and I didn't enjoy it.

If I want a system that can scale up and down and that multiples of any system object can be introduced or removed without drastic rewrites.

I would like to decouple bottleneck from code and turn it into runtime configuration.

My understanding of things such as Traefik and istio is that they are frustrating to set up.

Specifically I am working on designing interthread communication patterns for multithreaded software.

How do you design an architecture that is easy to change, scales and is flexible?

I am thinking of a message routing definition format that is extremely flexible and allows any topology to be created.

https://github.com/samsquire/ideas4#526-multiplexing-setting...

I think there is application of the same pattern to the network layer too.

Each communication event has associated with it an environment of keyvalues that look similar to this:

  petsserver1

Thanks for remembering me :-)

I would like things to run locally by default and then deployed to the cloud where they run.

Should be easier to debug problems if I can get the code to my machine and investigate issues with tools that my computer has such as "strace", "perf" and debug logging that I liberally apply to the build script.

In production we would have log aggregation and log search (such as ELK stack) and it is a good habit to get into the perspective of debugging production via tooling.

But CICD feels before that tooling in the pipeline. You could wire up your CICD to log to ELK but I would prefer local deployable software.

I think my focus on automating things means I want to be capable of seeing how the thing works without relying on a deployed black box in the cloud and using assumptions of how it works rather than direct investigation.

One of my journal entries is almost a lamentation of all the things that need to be done to release and use software.

This is that entry:

https://github.com/samsquire/ideas4#5-permanent-softwareplat...

I wonder if software could be deployed more like a URL that has all the information to configure a virtual machine. Docker over URL or something.

Integrating Rust into Python, Edward Wright, 2021-04-12 Examples for making rustpython run actual python code Calling Rust from Python using PyO3 Writing Python inside your Rust code — Part 1, 2020-04-17 RustPython, RustPython Rust for Python developers: Using Rust to optimize your Python code PyO3 (Rust bindings for Python) Musing About Pythonic Design Patterns In Rust, Teddy Rendahl, 2023-07-14

This story unfolds as a captivating journey where the agile Flounder, representing the Python programming language, navigates the vast seas of coding under the wise guidance of Sebastian, symbolizing Rust. Central to their adventure are three powerful tridents: cargo, PyO3, and maturin.

I would not recommend FFI + ctypes. Maintaining the bindings is tedious and error-prone. Also, Rust FFI/unsafe can be tricky even for experienced Rust devs.

Instead PyO3 [1] lets you "write a native Python module in Rust", and it works great. A much better choice IMO.

[1] https://github.com/PyO3/pyo3

Same w/ Rust and Python, this is really neat because now each thread could have a GIL without doing exactly what you said. The pyO3 commit to allow subinterpreters was merged 21 days ago, so this might "just work" today: https://github.com/PyO3/pyo3/pull/3446

I expected someone to write a rust-based scripting language which tightly integrated with rust itself.

In reality, it seems like the python developers and toolchain are embracing rust enough to reduce the benefits to a new alternative.

https://github.com/PyO3/pyo3

Hey HN! I am one of the people working on Bytewax. Bytewax came out of our experience working with ML infrastructure at GitHub. We wanted to use Python because we could move fast, the team was very fluent in it, and the rest of our tooling was Python-native already. We didn't want to introduce JVM-based solutions into our stack because of the lack of experience and the friction we had trying to get Python-centric tooling working with existing solutions like Flink.

In our research, we found Timely Dataflow (https://timelydataflow.github.io/timely-dataflow/, https://news.ycombinator.com/item?id=24837031) and the Naiad project (https://www.microsoft.com/en-us/research/project/naiad/) as well as PyO3 (https://github.com/PyO3/pyo3) and we thought we found a match made in heaven :). Bytewax leverages both of these projects and builds on them to provide a clean API (at least we think so) and table stakes features like connectors, state recovery, and cloud-native scaling. It has been really cool to learn about the dataflow computation model, Rust, and how to wrangle the GIL with Rust and Python :P.

Would love to get your feedback :).

`pip install bytewax` to get started. We have a page of guides (https://www.bytewax.io/guides) with ready-to-run examples.

You can practice your Rust skills by writing performant and/or gluey extensions for higher-level language such as NodeJS (checkout napi-rs) and Python or complementing JS in the browser if you target Webassembly.

For instance, checkout Llama-node https://github.com/Atome-FE/llama-node for an involved Rust-based NodeJS extension. Python has PyO3, a Rust-Python extension toolset: https://github.com/PyO3/pyo3.

They can help you leverage your Rust for writing cool new stuff.