tigerbeetle VS Co-dfns

Indeed!

I was so glad to see you cite not only the Rebello paper but also Protocol-Aware Recovery for Consensus-Based Storage. When I read your first comment, I was about to reply to mention PAR, and then saw you had saved me the trouble!

UW-Madison are truly the vanguard where consensus hits the disk.

We implemented Protocol-Aware Recovery for TigerBeetle [1], and I did a talk recently at the Recurse Center diving into PAR, talking about the intersection of global consensus protocol and local storage engine. It's called Let's Remix Distributed Database Design! [2] and owes the big ideas to UW-Madison.

[1] https://github.com/coilhq/tigerbeetle

[2] https://www.youtube.com/watch?v=rNmZZLant9o

> It sounds like payments might be part of the larger concept of declarative programming (DP)

Yes, exactly! The idea with TigerBeetle's state machine [1] is to expose double-entry accounting as higher level financial primitives, so that developers can think in terms of declaring transfers from one account to another. The business logic behind the scenes is detailed, but the interfaces and data structures are simple.

[1] https://github.com/coilhq/tigerbeetle/blob/main/src/state_ma...

> Maybe TigerBeetle could be generalized to support any multi-step distributed process?

That's part of the plan, that the distributed database framework of TigerBeetle can be used as a ”distributed Iron Man suit” to support any kind of state machine.

It's a pleasure. Let me know if you have any more questions about TigerBeetle. Our design doc is also here: https://github.com/coilhq/tigerbeetle/blob/main/docs/DESIGN....

Surprisingly, some of the most powerful distributed systems algorithms or tools are actually deterministic. They're powerful because they can "load the dice" and so make the distributed system more intuitive for humans to reason about, more resilient to real world network faults, and do all this with more performance.

For example, Barbara Liskov and James Cowling's deterministic view change [1], which isn't plagued by the latency issues of RAFT's randomized dueling leader problem. Viewstamped Replication Revisited's deterministic view change can react to a failed primary much quicker than RAFT (heartbeat timeouts don't require randomized "padding" as they do in RAFT), commence the leader election, and also ensure that the leader election succeeds without a split vote.

Determinism makes all that possible.

Deterministic testing [2][3] is also your best friend when it comes to testing distributed systems.

[1] I did a talk on VSR, including the benefits of the view change — https://www.youtube.com/watch?v=Wii1LX_ltIs

[2] FoundationDB are pioneers of deterministic testing — https://www.youtube.com/watch?v=OJb8A6h9jQQ

[3] TigerBeetle's deterministic simulation tests — https://github.com/coilhq/tigerbeetle#simulation-tests

This is the chasm problem, where people don't use a technology because people aren't using that technology, thus the technology has difficulty gaining adoption. I did see that Zig does have it's own killer app and startup that's using Zig: TigerBeattle.

Control flow statements should always be on their own lines, then it's easy to find all of them by visually scanning top-down, without needing to look all the way down each line.

[1]: https://github.com/coilhq/tigerbeetle/blob/main/src/vsr/repl...

> In hindsight, data logic should be in the database itself.

This is the reason we are creating TigerBeetle [1] at Coil, as an open source distributed financial accounting database, with the double entry logic and financial invariants enforced through financial primitives within the database itself.

This is all the more critical for financial data, because raw data consistency is not enough for financial transactions, you also need financial consistency, not to mention immutability.

The performance of doing it this way is also easier. For example, around a million financial transactions per second on commodity hardware, with p100 latency around 10-20ms.

[1] https://github.com/coilhq/tigerbeetle

You probably already know this — because we’ve mentioned it a few times — but Coil champions and supports open-source projects and is privacy-first, by default. Over the years, Developer Relations at Coil has championed and sponsored teams that write Open Web Documentations and projects that empower open-source developers to get paid. Coil has also incubated many open-source projects like Tigerbeetle and Rafiki.

[6] Partial logical sector reads/writes even when using O_DIRECT — https://github.com/coilhq/tigerbeetle/blob/main/src/storage....

And if anyone wants an absolute masterclass in tacit programming, have a look at Aaron's Co-dfns compiler. The README has extensive reference material. https://github.com/Co-dfns/Co-dfns/

I don't put a lot of stock in the "write-only" accusation. I think it's mostly used by those who don't know APL because, first, it's clever, and second, they can't read the code. However, if I remember I implemented something in J 10 years ago, I will definitely dig out the code because that's the fastest way by far for me to remember how it works.

This project specifically looks to be done in a flat array style similar to Co-dfns[0]. It's not a very common way to use APL. However, I've maintained an array-based compiler [1] for several years, and don't find that reading is a particular difficulty. Debugging is significantly easier than a scalar compiler, because the computation works on arrays drawn from the entire source code, and it's easy to inspect these and figure out what doesn't match expectations. I wrote most of [2] using a more traditional compiler architecture and it's easier to write and extend but feels about the same for reading and small tweaks. See also my review [3] of the denser compiler and precursor Co-dfns.

As for being read by others, short snippets are definitely fine. Taking some from the last week or so in the APL Farm, {⍵÷⍨+/|-/¯9 ¯11+.○?2⍵2⍴0} and {(⍸⍣¯1+\⎕IO,⍺)⊂[⎕IO]⍵} seemed to be easily understood. Forum links at [4]; the APL Orchard is viewable without signup and tends to have a lot of code discussion. There are APL codebases with many programmers, but they tend to be very verbose with long names. Something like the YAML parser here with no comments and single-letter names would be hard to get into. I can recognize, say, that c⌿¨⍨←(∨⍀∧∨⍀U⊖)∘(~⊢∊LF⍪WS⍨)¨c trims leading and trailing whitespace from each string in a few seconds, but in other places there are a lot of magic numbers so I get the "what" but not the "why". Eh, as I look over it things are starting to make sense, could probably get through this in an hour or so. But a lot of APLers don't have experience with the patterns used here.

[0] https://github.com/Co-dfns/Co-dfns

[1] https://github.com/mlochbaum/BQN/blob/master/src/c.bqn

[2] https://github.com/mlochbaum/Singeli/blob/master/singeli.bqn

[3] https://mlochbaum.github.io/BQN/implementation/codfns.html

[4] https://aplwiki.com/wiki/Chat_rooms_and_forums

This always seemed like a very interesting project; we need to get to the point where, if things can run in parallel, they must run in parallel to make software more efficient on modern cpu/gpu.

It won't attract funds, I guess, but it would be far more trivial to make this work with an APL or a Lisp/Scheme. There already is great research for APL[0] and looking at the syntax of HVM-core it seems it is rather easy to knock up a CL DSL. If only there were more hours in a day.

[0] https://github.com/Co-dfns/Co-dfns

There are many styles of APL, not just due to its long history, but also because APL is somewhat agnostic to architecture paradigms. You can see heavily imperative code with explicit branching all over the place, strongly functional-style with lots of small functions, even object-oriented style.

However, given the aesthetic that you express, I think you might like https://github.com/Co-dfns/Co-dfns/. This is hands-down my favorite kind of APL, in which the data flow literally follows the linear code flow.

Which is, as far as I know, unused. (Similarly the gpu compiler.)

You might be familiar with iKe (grahics), SpecialK (GLSL) and Co-dfns. Also, I am working on bastardized APL for GPU – Fluent. Fluent 1 had backend implemented through Apple Metal Performance Shaders Graph and Fluent 2 has TensorFlowJS backend for now. I care more about having auto differentiation in the lang than running on GPU and do graphics, to be honest.

Not that I am aware of. I think the closest project is co-dfns[1] which is being developed by Aaron Hsu (he did a presentation as well). It aims to compile a subset of APL so that it can be executed on GPUs for instance, possibly with other backends. I imagine an XLA backend could be possible there.

[1] https://github.com/Co-dfns/Co-dfns

Aaron hsu did his dissertation on this topic (compiler, thesis), at indiana university in the us.