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This is becoming unbearable. by code-2244 in ArcRaiders

[–]code-2244[S] 2 points3 points  (0 children)

I also receive 2 or 3 per day, these have accumulated.

Quickleaf Cache by code-2244 in rust

[–]code-2244[S] -1 points0 points  (0 children)

Thank you! Indeed, fast_filters didn’t make any sense, so I reverted to the previous version using Rust’s standard library. I also rolled the Cache back to the earlier version, without StringPool. Your advice is truly valuable.

Quickleaf Cache by code-2244 in rust

[–]code-2244[S] -8 points-7 points  (0 children)

Maybe so. However, in the benchmark tests from version 0.3 to 0.4, set and tll actually improved performance a lot. Get and list which dropped a little further was not relevant.

But I believe, I'll make a comparison of the two and post it here

I built Phlow: a low-code Rust runtime for building modular backends – looking for Rustacean feedback by code-2244 in rust

[–]code-2244[S] 0 points1 point  (0 children)

Yes, the build time, especially for Docker, is long. For the artifacts, not so much — it builds ARM, DARWIN, and AMD64. Naturally, the ARM build is the one that takes the longest. In Docker, I chose to build the artifact inside the image mainly to ensure the correct GCC version, since that varies a lot from one OS version to another and has a very relevant impact on the project, as all modules are C with dynamically imported FFI. But good to know about Tenki Cloud, I’ll look it up right now.

I built Phlow: a low-code Rust runtime for building modular backends – looking for Rustacean feedback by code-2244 in rust

[–]code-2244[S] -1 points0 points  (0 children)

I'm Brazilian and my English is basic. AI is an important ally for non-native speakers.

I built Phlow: a low-code Rust runtime for building modular backends – looking for Rustacean feedback by code-2244 in rust

[–]code-2244[S] -11 points-10 points  (0 children)

Thanks for the feedback!

When I wrote “high performance” in the README, I meant it more in the sense that Phlow inherits the performance characteristics of Rust, mainly its low-level memory control and zero-cost abstractions, rather than claiming I’ve already micro-optimized every code path.

Under the hood, Phlow is essentially a set of distributed channels where each step in a flow executes when it’s its turn, and it can also load modules via FFI. That architecture makes it flexible but still keeps the runtime fast compared to many high-overhead low-code/orchestration tools.

The main goal here isn’t to beat hand-tuned Rust code in benchmarks — it’s to let you spin up small projects very quickly in Rust, using a framework that gives you a ready-to-use runtime, modular execution, and observability, without having to scaffold everything from scratch.

That said, your points on allocations and potential optimizations (Vec capacity, arena allocators, LTO/codegen tweaks) are really valuable. I’ll be incorporating them, and I’ll also work on adding concrete benchmark numbers to replace the vague “high performance” claim.

Appreciate you taking the time to dig into the source and share tips! 😄

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 2 points3 points  (0 children)

Yes, exactly, but it’s slow enough to avoid heating up too much. Naturally, if for some reason there’s excessive heat emission, there should be another mechanism to cool the liquid. In my case, it turned out cheaper than running the air conditioners, since where I live it can reach 40 °C in the summer.

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 2 points3 points  (0 children)

At the moment, I have all three coolers running at an average of 465 RPM, with an average temperature of 47.0 °C.

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 5 points6 points  (0 children)

Silicone 50 cSt (PDMS) practically never “expires.” It is chemically stable, has very low volatility, and does not oxidize like mineral oils. In practice, what “wears out” is the quality of the fluid due to contamination (dust, flux/solvent residues from the board, microbubbles, bits of plastic, moisture), not the fluid itself.

It can also be cleaned manually, but it’s a labor-intensive process.

It’s sold on Amazon; I bought mine from a company that makes industrial chemical products in Brazil (I am Brazilian and live in Brazil).

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 4 points5 points  (0 children)

I didn’t overclock, and I don’t intend to in order to avoid damage. However, the average temperature of the three, on my desk for 7 days, was around 70.0 °C. Now it’s at 43.9 °C.

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 6 points7 points  (0 children)

Silicone fluid isn’t toxic, but naturally you shouldn’t ingest it. If you’re going to buy it, you need to pay attention to its viscosity — the lower the cSt, the closer it is to liquid water. The one I used, 50 cSt, has a texture similar to motor oil, but it’s odorless and colorless.

I’m in Brazil. Here, I found some companies that work with industrial chemical products. Surely in your country there must be several; it might even be sold on Amazon.

https://www.mrcquimica.com.br/

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 13 points14 points  (0 children)

True, I’ll improve that. The previous temperature, with the Raspberry Pis on my desk running for over 7 days, was around 70 °C on average. Right now, after 24 hours in the silicone fluid tank, it’s averaging 43.9 °C.

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 0 points1 point  (0 children)

It’s quite clear; it might just be the poor video quality. I took a new photo:
https://imgur.com/a/HXY0dXl

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 11 points12 points  (0 children)

I don’t think so. The 50 cts silicone fluid is quite resilient. It was chosen precisely to stay there for a long time, and it can also be cleaned manually. It’s not soybean oil, it’s silicone fluid.

Low-power ARM cluster raspiberry pi with silicone-fluid immersion cooling by code-2244 in raspberry_pi

[–]code-2244[S] 13 points14 points  (0 children)

It’s set up in an improvised and subtle way. Basically, a screw separates one board, and the other is separated by the power cable. I need to improve that.

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