> It captures the nondeterminism in the order of delivery of communications. The Subsequent transition captures fairness arising from the guarantee of delivery. We provide a denotational semantics for our minimal actor language in terms of the transition relations.
Juicy paper, not to mention the declassification. It really reminds me of asyncmachine.dev which has actors, relations, transitions, and embraces non-determinism.
Please change the title to the original, "Actors: A Model Of Concurrent Computation In Distributed Systems".
I'm not normally a stickler for HN's rule about title preservation, but in this case the "in distributed systems" part is crucial, because IMO the urge to use both the actor model (and its relative, CSP) in non-distributed systems solely in order to achieve concurrency has been a massive boondoggle and a huge dead end. Which is to say, if you're within a single process, what you want is structured concurrency ( https://vorpus.org/blog/notes-on-structured-concurrency-or-g... ), not the unstructured concurrency that is inherent to a distributed system.
I'm working on an rest API server backed by a git repo. Having an actor responsible for all git operations saved me from a lot of trouble as having all git operations serialised freed me from having to prevent concurrent git operations.
Using actors also simplified greatly other parts of the app.
Isn’t that just serializing through a queue, aka. the producer consumer pattern? Which I think the correct solution for most common concurrency problems.
For something to be an actor, it should be able to:
- Send and receive messages
- Create other actors
- Change how the next message is handled (becomes in Erlang)
I think the last one is what makes it different it from simple message passing, and what makes it genius: state machines consuming queues.
Agreed, (heirarchical if you must) state machines consuming queues and writing to queues via messages wins. If you are FP minded like me, then you are set up to cleanly separate IO to the edges and have a functional core imperative shell hexagonal architecture for less additional overhead thsn a standard java beans style OO logical design.
This might be a question of personal preference.
At the design stage I already find it more approachable to think in separated responsibilities, and it naturally translates to actors.
Thinking about the app, it's much reasier for me to thin "send the message to the actor" than call that function that uses the necessary mutex. With mutexes, I think the separation of concerns is not as strong, and you might end up with a function taking multiples mutexes that might interfere. With the actor model, I feel there is less risk (though I'm sure this would be questioned by seasoned mutex users).
In this simple case they're more or less equivalent if the only task is limiting concurrency, but in general usage of mutexes multiplies and soon enough someone else has created a deadlock situation.
Extending it however reveals some benefits, locking is often for stopping whilst waiting for something enqueued can be parallell with waiting for something else that is enqueued.
I think it very much comes down to history and philosophy, actors are philosophically cleaner (and have gained popularity with success stories) but back in the 90s when computers were physically mostly single-threaded and memory scarce, the mutex looked like a "cheap good choice" for "all" multithreading issues since it could be a simple lock word whilst actors would need mailbox buffering (allocations... brr),etc that felt "bloated" (in the end, it turned out that separate heavyweight OS supported threads was often the bottleneck once thread and core counts got larger).
Mutexes are quite often still the base primitive at the bottom of lower level implementations if compare-and-swap isn't enough, whilst actors generally are a higher level abstraction (better suited to "general" programming).
Atomic operations, memory barriers, condition variables, thread/"virtual processor" scheduling are philosophically cleaner since they're what the specified hardware/OS concurrency model actually provides, and can implement all of locks, mutexes, structured concurrency, arbitrary queues, actors etc. etc.
You are using mutexes, they are on the Actor message queues, amongst other places. "Just use mutexes" suggests a lack of experience of using them, they are very difficult to get both correct and scalable. By keeping them inside the Actor system, a lot of complexity is removed from the layers above. Actors are not always the right choice, but when they are they are a very useful and simplifying abstraction.
It is endemic to the JVM world that people try various forms of snake oil concurrency inside an address space like actors and the original synchronized model when java.util.concurrent and Executors are "all you need."
It was a theme in part of my career to pick up something written in Scala that used actors that (1) didn't always get the same answer and (2) didn't use all the CPU cores and struggling for days to get it working right with actors then taking 20 minutes to rewrite it using Executors and getting it to work the first time and always work thereafter.
Nurseries sound similar to run-till-completion schedulers [0].
> IMO the urge to use both the actor model (and its relative, CSP) in non-distributed systems solely in order to achieve concurrency has been a massive boondoggle
Can't you model any concurrent non-distributed system as a concurrent distributed system?
> Can't you model any concurrent non-distributed system as a concurrent distributed system?
Yes, in the same way that you can give up `for` loops and `while` loops and `if` statements and `switch` statements and instead write them all with `goto`, but you don't do this, and anyone advising you to do this would be written off as insane. The entire thrust of this thread is that you can have a more reliable system that is easier to reason about if you use specific constructs that each have less power, and non-distributed systems have the option to do this. Unstructured concurrency should be reserved exclusively for contexts where structured concurrency is impossible, which is what the actor model is for.
The point I was trying to make is that you can apply the actor model to any system of isolated processes. Whether the isolated processes live on a distributed system of networked computers or on the same computer is an implementation detail. The critical issue is that each actor should own and mutate its own state. Whether all actors run on the same thread or on separate threads is also an implementation detail. For instance, AtomVM is a lightweight implementation of the Beam (actor model) that runs on microcontrollers [0].
> The entire thrust of this thread is that you can have a more reliable system that is easier to reason about if you use specific constructs that each have less power
Easier to reason about, sure, fine. Your earlier comment claims the actor model is a dead end in non-distributed systems.
> Unstructured concurrency should be reserved exclusively for contexts where structured concurrency is impossible, which is what the actor model is for.
Results from my quick search on structured/unstructured concurrency were all references to Swift. Is this a Swift thing? In any case, the issue appears to be more about managing tasks that don't require a preemptive scheduler. As I see it, that issue appears orthogonal to distributed/non-distributed systems.
> Easier to reason about, sure, fine. Your earlier comment claims the actor model is a dead end in non-distributed systems.
If you have two ways of structuring something, and the worse way is so predominant that it obscures even the existence of the better way, that's a dead end in my book. In the pre-structured-programming days when people had to fight tooth and nail to get people to acknowledge the value of `if` and `while` over `goto`, we would have also called `goto` a dead end; it's plain to see that we would be in a worse place today if the structured programming advocates had not managed to convince everyone of its superiority.
> Results from my quick search on structured/unstructured concurrency were all references to Swift. Is this a Swift thing?
I have no idea whether Swift supports it, but no, it's not a Swift thing any more than `while` and `if` are a Python thing. I highly, highly encourage people to read the linked blog post, it will be the best use of your time today.
I read the article you referenced. It's based on, and described wrt, Python's `async with` (so quite a few layers of abstraction), so I can't say with certainty how it's implemented. But, as I noted earlier, it isn't really that different from a run-till-completion task scheduler and is not particularly novel or interesting, imo.
Don’t Erlang/Elixir model all concurrency as actors, to some level of success. I was under the impression that it allows for quite a bit of deployment flexibility. Actors are addressed in the same way whether they’re on the same machine or not.
yes, to huge levels of success. It's not clear what kibwen is going on about, but local + remote actor concurrency transparency, while not without its complications, comes with massive development and deployment wins.
I’m currently engineering a system that uses an actor framework to describe graphs of concurrent processing. We’re going to a lot of trouble to set up a system that can inflate a description into a running pipeline, along with nesting subgraphs inside a given node.
It’s all in-process though, so my ears are perking up at your comment. Would you relax your statement for cases where flexibility is important? E.g. we don’t want to write one particular arrangement of concurrent operations, but rather want to create a meta system that lets us string together arbitrary ones. Would you agree that the actor abstraction becomes useful again for such cases?
Data flow graphs could arguably be called structured concurrency (granted, of nodes that resemble actors).
FWIW, this has become a perfectly cromulent pattern over the decades.
It allows highly concurrent computation limited only by the size and shape of the graph while allowing all the payloads to be implemented in simple single-threaded code.
The flow graph pattern can also be extended to become a distributed system by having certain nodes have side-effects to transfer data to other systems running in other contexts. This extension does not need any particularly advanced design changes and most importantly, they are limited to just the "entrance" and "exit" nodes that communicate between contexts.
I am curious to learn more about your system. In particular, what language or mechanism you use for the description of the graph.
We’re using the C++ Actor Framework (CAF) to provide the actor system implementation, and then we ended up using a stupid old protobuf to describe the compute graph. Protobuf doubles as a messaging format and a schema with reflection, so it lets us receive pipeline jobs over gRPC and then inflate them with less boilerplate (by C++ standards, anyway).
Related to what you were saying, the protobuf schema has special dedicated entries for the entrance and exit nodes, so only the top level pipeline has them. Thus the recursive aspect (where nodes can themselves contain sub-graphs) applies only to the processor-y bit in the middle. That allowed us to encourage the side effects to stay at the periphery, although I think it’s still possible in principle. But at least the design gently guides you towards doing it that way.
After having created our system, I discovered the Reactor framework (e.g. Lingua Franca). If I could do it all over, I think I would have built using that formalism, because it is better suited for making composable dataflows. The issue with the actor model for this use case is that actors generally know about each other and refer to each other by name. Composable dataflows want the opposite assumption: you just want to push data into some named output ports, relying on the orchestration layer above you to decide who is hooked up to that port.
To solve the above problem, we elected to write a rather involved subsystem within the inflation layer that stitches the business actors together via “topic” actors. CAF also provides a purpose-built flows system that sits over top of the actors, which allows us to write the internals of a business actors in a functional reactive-x style. When all is said and done, our business actors don’t really look much like actors - they’re more like MIMO dataflow operators.
When you zoom out, it also becomes obvious that we are in many ways re-creating gstreamer. But if you’ve ever used gstreamer before, you may understand why “let’s rest our whole business on writing gstreamer elements” is too painful a notion to be entertained.
Since you still have C++ involved and if you are still looking for composable dataflow ideas, take a look at TBB's "flow_graph" module. It's graph execution is all in-process while what you describe sounds more distributed, but perhaps it is still interesting.
> we don’t want to write one particular arrangement of concurrent operations, but rather want to create a meta system that lets us string together arbitrary ones. Would you agree that the actor abstraction becomes useful again for such cases?
Actors are still just too general and uncontrolled, unless you absolutely can't express the thing you want to any other way. Based on your description, have you looked at iterate-style abstractions and/or something like Haskell's Conduit? In my experience those are powerful enough to express anything you want to (including, critically, being able to write a "middle piece of a pipeline" as a reusable value), but still controlled and safe in a way that actor-based systems aren't.
> both the actor model (and its relative, CSP) in non-distributed systems solely in order to achieve concurrency has been a massive boondoggle and a huge dead end.
Actors can be made to do structured concurrency as long as you allow actors to wait for responses from other actors, and implement hierarchy so if an actor dies , its children do as well. And that’s how I use them! So I have to say the OP is just ignorant of how actors are used in practice.
> Actors can be made to do structured concurrency as long as you allow actors to wait for responses from other actors
At which point they're very much not actors any more. You've lost the deadlock avoidance, you can't do the `become`-based stuff that looks so great in small demos. At that point what are you gaining from using actors at all?
To adapt the analogy from the link in the root comment, this is akin to saying "`goto` can be made to do structured programming as long as you strictly ensure that the control flow graph is reducible". Which is to say, it is a true statement that manages to miss the point: the power of both structured programming and structured concurrency comes from defining new primitives that fundamentally do the right thing and don't even give you the option to do the wrong thing, thus producing a more reliable system. There's no "as long as you...", it just works.
Except Akka in Java and for the entirety of Erlang and its children Elixir and Gleam. You obviously can scale those to multiple systems, but they provide a lot of benefit in local single process scenarios too imo.
If I'm not mistaken ROOM (ObjecTime, Rational Rose RealTime) was also heavily based on it. I worked in a company that developed real time software for printing machines with it and liked it a lot.
I've worked on a number of systems that used Akka in a non-distributed way and it was always an overengineered approach that made the system more complex for no benefit.
Fair, I worked a lot on data pipelines and found the actor model worked well in that context. I particularly enjoyed it in the Elixir ecosystem where I was building on top of Broadway[0]
Probably has to do with not fighting the semantics of the language.
Really depends of the ergonomics of the language. In erlang/elixir/beam langs etc, its incredibly ergonomic to write code that runs on distributed systems.
you have to try really hard to do the inverse. Java's ergonomics, even with Akka, lends its self to certain design patterns that don't lend itself to writing code for distributed systems.
I've written a non-distributed app that uses the Actor model and it's been very successful. It concurrently collects data from hundreds of REST endpoints, a typical run may make 500,000 REST requests, with 250 actors making simultaneous requests - I've tested with 1,000 but that tends to pound the REST servers into the ground. Any failed requests are re-queued. The requests aren't independent, request type C may depend on request types A & B being completed first as it requires data from them, so there's a declarative dependency graph mechanism that does the scheduling.
I started off using Akka but then the license changed and Pekko wasn't a thing yet, so I wrote my own single-process minimalist Actor framework - I only needed message queues, actor pools & supervision to handle scheduling and request failures, so that's all I wrote. It can easily handle 1m messages a second.
I have no idea why that's a "huge dead end", Actors are a model that's a very close fit to my use case, why on earth wouldn't I use it? That "nurseries" link is way TL;DR but it appears to be rubbishing other options in order to promote its particular model. The level of concurrency it provides seems to be very limited and some of it is just plain wrong - "in most concurrency systems, unhandled errors in background tasks are simply discarded". Err, no.
Big Rule 0: No Dogmas: Use The Right Tool For The Job.
I don't know if I'd apply your blanket prescription, but at some level I agree... here's where I see Actors often going wrong and substantially agree with you: state propagation / replication:
I've been on more than one team that has broken their (in-process, single machine) process up into multiple "actors" (or "components" or "services") through communicating threads (usually over Rust channels) and then had a situation where they replicate some piece of state through messaging because they're been told that their system must not have global (mutable or immutable) state.
But now they've just created a whole pile of inefficient boiler plate (propagating copies of effectively the same piece of global state through different services) and created a new way of having race conditions and/or just plain old stale or inconsistent data. For what are essentially ideological reasons.
Every new feature in this model ends up being mostly plumbing of state replication between what are supposed to be isolated component models.
The answer to me is just to establish a discipline where a given piece of data is owned for writes by one task or component, but can be freely read by any.
If you truly have a stateless system or extremely clear data ownership boundaries, I can see the value of a CSP/actor approach. And in the context of Rust's borrow checker this model is fairly convenient. But it quickly becomes prone to cargo-culting and becomes a recipe for hairy, hard to maintain code.
I am convinced most teams blanket applying actors would be far better suited to more tuplespaces/blackboard/Linda type model for concurrent coordination. A way of working that never caught on, but has always been attractive to me.
Note that Rust supports a form of structured programming (though only via thread-based concurrency, not via async), provided by the `std::thread::scope` API: https://doc.rust-lang.org/std/thread/fn.scope.html . The blog post above calls out an older version of this API as inspiration.
Is Pony still an actively developed language? I remember watching several talks while they brought the language up to release, and read several of the accompanying papers. However, I thought with the primary corporate sponsor dropping the language it had gone basically EOL. Which was a pretty large bummer as I was very interested to see how the reference capability model of permissions and control worked at large scale for concurrency control and management (as well as its potential application to other domains).
Dunno, now it feels like the "hot" thing is either manual memory languages like Zig, Odin an Rust or languages with novel type systems like Lean, Koka, Idris, etc... GC'd "systems" languages like Nim, Crystal, Pony, Go, etc... all seem kind of old fashioned now.
Go seems to have some enduring affection and popularity for new projects and companies. I recently felt like a lot of the recent shift was less about GC and more about runtime characteristics (static binaries, lean resource consumption, lack of an in-your-face virtual machine).
It never felt like Nim, Pony, or Crystal were ever that popular that a diminished hype cycle registered as something thematic to me (not that I really intend to disagree with your perspective here).
Orleans is pretty cool! The project has matured nicely over the years (been something like 10 years?) and they have some research papers attached to it if you like reading up on the details. The nuget stats indicate a healthy amount of downloads too, more than one might expect.
One of the single most important things I've done in my career was going down the Actor Model -framework rabbit hole about 8 or 9 years ago, read a bunch of books on the topic, that contained a ton of hidden philosophy, amazing reasoning, conversations about real-time vs eventual consistency, Two-Generals-Problem - just a ton of enriching stuff, ways to think about data flows, the direction of the flow, immutability, event-logged systems and on and on. At the time CQS/CQRS was making heavy waves and everyone tried to implement DDD & Event-based (and/or service busses - tons of nasty queues...) and Actor Model (and F# for that matter) was such clean fresh breath of air from all the Enterprise complexity.
Would highly recommend going this path for anyone with time on their hands, its time well spent. I still call on that knowledge frequently even when doing OOP.
Not books, but some inspiring resources. FModel [0] is a set of patterns for functional reactive DDD on the basis of event sourcing. In particular the Decider pattern is a great way to model aggregates, and test them using Scenario's that read like Gherkin in code (given.. when.. then). Combines well with actors to represent aggregates.
On the BEAM used by Erlang, Elixir, and Gleam actors are called processes, and this guide [1] delves into domain modeling with them.
Applied Akka Patterns by Michael Nash, Wade Waldron (Oreilly) was very digestible and relevant at the time, might be dated by now. Just read the intro to get the vibe.
These days I would recommend picking a framework and then ask claude & friends to do a deep dive with you and build an example project out. Ask it to explain concepts, architecture, trade-offs, scalability considerations, hosting considerations, compare it with other frameworks, hook it up to storage systems (sqlite, postgresql, blob storage) and so on. Try running them within a wireguard network and so on. Very interesting learning to be found.
I was disappointed when MS discontinued Axum, which I found pleasant to use and thought the language based approach was nicer than a library based solution like Orleans.
The Axum language had `domain` types, which could contain one or more `agent` and some state. Agents could have multiple functions and could share domain state, but not access state in other domains directly. The programming model was passing messages between agents over a typed `channel` using directional infix operators, which could also be used to build process pipelines. The channels could contain `schema` types and a state-machine like protocol spec for message ordering.
It didn't have "classes", but Axum files could live in the same projects as regular C# files and call into them. The C# compiler that came with it was modified to introduce an `isolated` keyword for classes, which prevented them from accessing `static` fields, which was key to ensuring state didn't escape the domain.
The software and most of the information was scrubbed from MS own website, but you can find an archived copy of the manual[1]. I still have a copy of the software installer somewhere but I doubt it would work on any recent Windows.
Sadly this project was axed before MS had embraced open source. It would've been nice if they had released the source when the decided to discontinue working on it.
Actor model is one of these things that really seduces me on paper, but my only exposure to it was in my consulting career, and that was to help migrate away from it. The use case seemed particularly adapted (integration of a bunch of remote devices with spotty connection), but it was practically a nightmare to debug... which was a problem since it was buggy.
To be fair, the problem was probably that particular implementation, but I'm wondering if there's any successful rollout of that model at any significant scale out there.
I was in a team that built a bigger telco project for machine to machine communication, using akka actors. It was okayish, the only thing that I hated was how the whole pattern spread through the whole code base
> It is generally believed that the next generation of computers will involve
massively parallel architectures.
To this day - we have only taken advantage of parallel architectures in GPUs - a lot of software still runs on single CPU threads. most programming languages- are made optimized for single threads - yeah we might have threads, virtual threads, fibers etc - but how many people are using those on a daily basis?
I was under the impression that parallel and concurrent code was the dominant paradigm for programming tasks currently going in most of the semi-mainstream domains. I am certainly willing to concede that I could just be in a bubble that thinks about and designs for concurrency and parallelism as a first class concern, but it doesn’t seem that way.
I mean one of the large features/touted benefits for Rust is the single mutable XOR multiple immutable semantics explicitly to assist with problems in parallel/concurrent code, all of the OTP languages are built on top of a ridiculously parallel and distributed first ‘VM’. It strikes me as peculiar that these types of languages and ecosystems would be so, apparently, popular if the primary use case of ‘safe’/resilient parallel/concurrent code was not a large concern.
> It captures the nondeterminism in the order of delivery of communications. The Subsequent transition captures fairness arising from the guarantee of delivery. We provide a denotational semantics for our minimal actor language in terms of the transition relations.
Juicy paper, not to mention the declassification. It really reminds me of asyncmachine.dev which has actors, relations, transitions, and embraces non-determinism.
Please change the title to the original, "Actors: A Model Of Concurrent Computation In Distributed Systems".
I'm not normally a stickler for HN's rule about title preservation, but in this case the "in distributed systems" part is crucial, because IMO the urge to use both the actor model (and its relative, CSP) in non-distributed systems solely in order to achieve concurrency has been a massive boondoggle and a huge dead end. Which is to say, if you're within a single process, what you want is structured concurrency ( https://vorpus.org/blog/notes-on-structured-concurrency-or-g... ), not the unstructured concurrency that is inherent to a distributed system.
I'm working on an rest API server backed by a git repo. Having an actor responsible for all git operations saved me from a lot of trouble as having all git operations serialised freed me from having to prevent concurrent git operations.
Using actors also simplified greatly other parts of the app.
Isn’t that just serializing through a queue, aka. the producer consumer pattern? Which I think the correct solution for most common concurrency problems.
For something to be an actor, it should be able to:
- Send and receive messages
- Create other actors
- Change how the next message is handled (becomes in Erlang)
I think the last one is what makes it different it from simple message passing, and what makes it genius: state machines consuming queues.
Agreed, (heirarchical if you must) state machines consuming queues and writing to queues via messages wins. If you are FP minded like me, then you are set up to cleanly separate IO to the edges and have a functional core imperative shell hexagonal architecture for less additional overhead thsn a standard java beans style OO logical design.
So you're just using actors to limit concurrency? Why not use a mutex?
This might be a question of personal preference. At the design stage I already find it more approachable to think in separated responsibilities, and it naturally translates to actors. Thinking about the app, it's much reasier for me to thin "send the message to the actor" than call that function that uses the necessary mutex. With mutexes, I think the separation of concerns is not as strong, and you might end up with a function taking multiples mutexes that might interfere. With the actor model, I feel there is less risk (though I'm sure this would be questioned by seasoned mutex users).
In this simple case they're more or less equivalent if the only task is limiting concurrency, but in general usage of mutexes multiplies and soon enough someone else has created a deadlock situation.
Extending it however reveals some benefits, locking is often for stopping whilst waiting for something enqueued can be parallell with waiting for something else that is enqueued.
I think it very much comes down to history and philosophy, actors are philosophically cleaner (and have gained popularity with success stories) but back in the 90s when computers were physically mostly single-threaded and memory scarce, the mutex looked like a "cheap good choice" for "all" multithreading issues since it could be a simple lock word whilst actors would need mailbox buffering (allocations... brr),etc that felt "bloated" (in the end, it turned out that separate heavyweight OS supported threads was often the bottleneck once thread and core counts got larger).
Mutexes are quite often still the base primitive at the bottom of lower level implementations if compare-and-swap isn't enough, whilst actors generally are a higher level abstraction (better suited to "general" programming).
Atomic operations, memory barriers, condition variables, thread/"virtual processor" scheduling are philosophically cleaner since they're what the specified hardware/OS concurrency model actually provides, and can implement all of locks, mutexes, structured concurrency, arbitrary queues, actors etc. etc.
You are using mutexes, they are on the Actor message queues, amongst other places. "Just use mutexes" suggests a lack of experience of using them, they are very difficult to get both correct and scalable. By keeping them inside the Actor system, a lot of complexity is removed from the layers above. Actors are not always the right choice, but when they are they are a very useful and simplifying abstraction.
Horses for courses, as they say.
Can you share some insights why mutexes are difficult to get correct and scalable?
Lock-free queues and 16-core processors exist though. I use actors for the abstraction primarily anyway.
Because actors were invented to overcome deadlocks caused by mutexes. See page 137. With mutexes you can forget concurrency safety.
I’m pretty sure it’s possible to deadlock an actor system.
A bad one, yes. But it was esp. invented to prevent the mutual exclusion problem. And good actor systems do so.
It is endemic to the JVM world that people try various forms of snake oil concurrency inside an address space like actors and the original synchronized model when java.util.concurrent and Executors are "all you need."
It was a theme in part of my career to pick up something written in Scala that used actors that (1) didn't always get the same answer and (2) didn't use all the CPU cores and struggling for days to get it working right with actors then taking 20 minutes to rewrite it using Executors and getting it to work the first time and always work thereafter.
Nurseries sound similar to run-till-completion schedulers [0].
> IMO the urge to use both the actor model (and its relative, CSP) in non-distributed systems solely in order to achieve concurrency has been a massive boondoggle
Can't you model any concurrent non-distributed system as a concurrent distributed system?
0. https://en.wikipedia.org/wiki/Run-to-completion_scheduling
> Can't you model any concurrent non-distributed system as a concurrent distributed system?
Yes, in the same way that you can give up `for` loops and `while` loops and `if` statements and `switch` statements and instead write them all with `goto`, but you don't do this, and anyone advising you to do this would be written off as insane. The entire thrust of this thread is that you can have a more reliable system that is easier to reason about if you use specific constructs that each have less power, and non-distributed systems have the option to do this. Unstructured concurrency should be reserved exclusively for contexts where structured concurrency is impossible, which is what the actor model is for.
The point I was trying to make is that you can apply the actor model to any system of isolated processes. Whether the isolated processes live on a distributed system of networked computers or on the same computer is an implementation detail. The critical issue is that each actor should own and mutate its own state. Whether all actors run on the same thread or on separate threads is also an implementation detail. For instance, AtomVM is a lightweight implementation of the Beam (actor model) that runs on microcontrollers [0].
> The entire thrust of this thread is that you can have a more reliable system that is easier to reason about if you use specific constructs that each have less power
Easier to reason about, sure, fine. Your earlier comment claims the actor model is a dead end in non-distributed systems.
> Unstructured concurrency should be reserved exclusively for contexts where structured concurrency is impossible, which is what the actor model is for.
Results from my quick search on structured/unstructured concurrency were all references to Swift. Is this a Swift thing? In any case, the issue appears to be more about managing tasks that don't require a preemptive scheduler. As I see it, that issue appears orthogonal to distributed/non-distributed systems.
0. https://atomvm.org/
> Easier to reason about, sure, fine. Your earlier comment claims the actor model is a dead end in non-distributed systems.
If you have two ways of structuring something, and the worse way is so predominant that it obscures even the existence of the better way, that's a dead end in my book. In the pre-structured-programming days when people had to fight tooth and nail to get people to acknowledge the value of `if` and `while` over `goto`, we would have also called `goto` a dead end; it's plain to see that we would be in a worse place today if the structured programming advocates had not managed to convince everyone of its superiority.
> Results from my quick search on structured/unstructured concurrency were all references to Swift. Is this a Swift thing?
I have no idea whether Swift supports it, but no, it's not a Swift thing any more than `while` and `if` are a Python thing. I highly, highly encourage people to read the linked blog post, it will be the best use of your time today.
I read the article you referenced. It's based on, and described wrt, Python's `async with` (so quite a few layers of abstraction), so I can't say with certainty how it's implemented. But, as I noted earlier, it isn't really that different from a run-till-completion task scheduler and is not particularly novel or interesting, imo.
Structured concurrency is also a Kotlin thing https://kotlinlang.org/docs/coroutines-basics.html#coroutine... and a Python thing https://vorpus.org/blog/notes-on-structured-concurrency-or-g... and before that it was a C thing https://sustrik.github.io/250bpm/blog:71/
Don’t Erlang/Elixir model all concurrency as actors, to some level of success. I was under the impression that it allows for quite a bit of deployment flexibility. Actors are addressed in the same way whether they’re on the same machine or not.
yes, to huge levels of success. It's not clear what kibwen is going on about, but local + remote actor concurrency transparency, while not without its complications, comes with massive development and deployment wins.
You can certainly make any simple problem complicated enough to need a complex solution. But that’s just bad engineering.
Hmm, you think?
I’m currently engineering a system that uses an actor framework to describe graphs of concurrent processing. We’re going to a lot of trouble to set up a system that can inflate a description into a running pipeline, along with nesting subgraphs inside a given node.
It’s all in-process though, so my ears are perking up at your comment. Would you relax your statement for cases where flexibility is important? E.g. we don’t want to write one particular arrangement of concurrent operations, but rather want to create a meta system that lets us string together arbitrary ones. Would you agree that the actor abstraction becomes useful again for such cases?
Data flow graphs could arguably be called structured concurrency (granted, of nodes that resemble actors).
FWIW, this has become a perfectly cromulent pattern over the decades.
It allows highly concurrent computation limited only by the size and shape of the graph while allowing all the payloads to be implemented in simple single-threaded code.
The flow graph pattern can also be extended to become a distributed system by having certain nodes have side-effects to transfer data to other systems running in other contexts. This extension does not need any particularly advanced design changes and most importantly, they are limited to just the "entrance" and "exit" nodes that communicate between contexts.
I am curious to learn more about your system. In particular, what language or mechanism you use for the description of the graph.
We’re using the C++ Actor Framework (CAF) to provide the actor system implementation, and then we ended up using a stupid old protobuf to describe the compute graph. Protobuf doubles as a messaging format and a schema with reflection, so it lets us receive pipeline jobs over gRPC and then inflate them with less boilerplate (by C++ standards, anyway).
Related to what you were saying, the protobuf schema has special dedicated entries for the entrance and exit nodes, so only the top level pipeline has them. Thus the recursive aspect (where nodes can themselves contain sub-graphs) applies only to the processor-y bit in the middle. That allowed us to encourage the side effects to stay at the periphery, although I think it’s still possible in principle. But at least the design gently guides you towards doing it that way.
After having created our system, I discovered the Reactor framework (e.g. Lingua Franca). If I could do it all over, I think I would have built using that formalism, because it is better suited for making composable dataflows. The issue with the actor model for this use case is that actors generally know about each other and refer to each other by name. Composable dataflows want the opposite assumption: you just want to push data into some named output ports, relying on the orchestration layer above you to decide who is hooked up to that port.
To solve the above problem, we elected to write a rather involved subsystem within the inflation layer that stitches the business actors together via “topic” actors. CAF also provides a purpose-built flows system that sits over top of the actors, which allows us to write the internals of a business actors in a functional reactive-x style. When all is said and done, our business actors don’t really look much like actors - they’re more like MIMO dataflow operators.
When you zoom out, it also becomes obvious that we are in many ways re-creating gstreamer. But if you’ve ever used gstreamer before, you may understand why “let’s rest our whole business on writing gstreamer elements” is too painful a notion to be entertained.
Sounds good. Thanks for describing it.
Since you still have C++ involved and if you are still looking for composable dataflow ideas, take a look at TBB's "flow_graph" module. It's graph execution is all in-process while what you describe sounds more distributed, but perhaps it is still interesting.
> we don’t want to write one particular arrangement of concurrent operations, but rather want to create a meta system that lets us string together arbitrary ones. Would you agree that the actor abstraction becomes useful again for such cases?
Actors are still just too general and uncontrolled, unless you absolutely can't express the thing you want to any other way. Based on your description, have you looked at iterate-style abstractions and/or something like Haskell's Conduit? In my experience those are powerful enough to express anything you want to (including, critically, being able to write a "middle piece of a pipeline" as a reusable value), but still controlled and safe in a way that actor-based systems aren't.
> both the actor model (and its relative, CSP) in non-distributed systems solely in order to achieve concurrency has been a massive boondoggle and a huge dead end.
Why is that so?
Well, lots of people have tried it and spent a lot of money on it and don't seem to have derived any benefit from doing so.
Actors can be made to do structured concurrency as long as you allow actors to wait for responses from other actors, and implement hierarchy so if an actor dies , its children do as well. And that’s how I use them! So I have to say the OP is just ignorant of how actors are used in practice.
> Actors can be made to do structured concurrency as long as you allow actors to wait for responses from other actors
At which point they're very much not actors any more. You've lost the deadlock avoidance, you can't do the `become`-based stuff that looks so great in small demos. At that point what are you gaining from using actors at all?
To adapt the analogy from the link in the root comment, this is akin to saying "`goto` can be made to do structured programming as long as you strictly ensure that the control flow graph is reducible". Which is to say, it is a true statement that manages to miss the point: the power of both structured programming and structured concurrency comes from defining new primitives that fundamentally do the right thing and don't even give you the option to do the wrong thing, thus producing a more reliable system. There's no "as long as you...", it just works.
Isn't this a bit theoretical since most real world systems are distributed these days, using a browser as GUI?
Except Akka in Java and for the entirety of Erlang and its children Elixir and Gleam. You obviously can scale those to multiple systems, but they provide a lot of benefit in local single process scenarios too imo.
Things like data pipelines, and games etc etc.
If I'm not mistaken ROOM (ObjecTime, Rational Rose RealTime) was also heavily based on it. I worked in a company that developed real time software for printing machines with it and liked it a lot.
I've worked on a number of systems that used Akka in a non-distributed way and it was always an overengineered approach that made the system more complex for no benefit.
Fair, I worked a lot on data pipelines and found the actor model worked well in that context. I particularly enjoyed it in the Elixir ecosystem where I was building on top of Broadway[0]
Probably has to do with not fighting the semantics of the language.
[0] https://elixir-broadway.org/
Really depends of the ergonomics of the language. In erlang/elixir/beam langs etc, its incredibly ergonomic to write code that runs on distributed systems.
you have to try really hard to do the inverse. Java's ergonomics, even with Akka, lends its self to certain design patterns that don't lend itself to writing code for distributed systems.
Eh?
I've written a non-distributed app that uses the Actor model and it's been very successful. It concurrently collects data from hundreds of REST endpoints, a typical run may make 500,000 REST requests, with 250 actors making simultaneous requests - I've tested with 1,000 but that tends to pound the REST servers into the ground. Any failed requests are re-queued. The requests aren't independent, request type C may depend on request types A & B being completed first as it requires data from them, so there's a declarative dependency graph mechanism that does the scheduling.
I started off using Akka but then the license changed and Pekko wasn't a thing yet, so I wrote my own single-process minimalist Actor framework - I only needed message queues, actor pools & supervision to handle scheduling and request failures, so that's all I wrote. It can easily handle 1m messages a second.
I have no idea why that's a "huge dead end", Actors are a model that's a very close fit to my use case, why on earth wouldn't I use it? That "nurseries" link is way TL;DR but it appears to be rubbishing other options in order to promote its particular model. The level of concurrency it provides seems to be very limited and some of it is just plain wrong - "in most concurrency systems, unhandled errors in background tasks are simply discarded". Err, no.
Big Rule 0: No Dogmas: Use The Right Tool For The Job.
> That "nurseries" link is way TL;DR
Please read and understand that blog post, I promise it's worth your time.
Um, no I won't and no it won't. I have no time for tub-thumping.
CSP in Golang makes concurrency in it look pleasant compared to the async monstrosities I've seen in C#.
I heartily recommend reading the link the parent comment on structured concurrency. The alternatives here are not merely goroutines vs. async.
[dead]
Title might have been changed for length.
I don't know if I'd apply your blanket prescription, but at some level I agree... here's where I see Actors often going wrong and substantially agree with you: state propagation / replication:
I've been on more than one team that has broken their (in-process, single machine) process up into multiple "actors" (or "components" or "services") through communicating threads (usually over Rust channels) and then had a situation where they replicate some piece of state through messaging because they're been told that their system must not have global (mutable or immutable) state.
But now they've just created a whole pile of inefficient boiler plate (propagating copies of effectively the same piece of global state through different services) and created a new way of having race conditions and/or just plain old stale or inconsistent data. For what are essentially ideological reasons.
Every new feature in this model ends up being mostly plumbing of state replication between what are supposed to be isolated component models.
The answer to me is just to establish a discipline where a given piece of data is owned for writes by one task or component, but can be freely read by any.
If you truly have a stateless system or extremely clear data ownership boundaries, I can see the value of a CSP/actor approach. And in the context of Rust's borrow checker this model is fairly convenient. But it quickly becomes prone to cargo-culting and becomes a recipe for hairy, hard to maintain code.
I am convinced most teams blanket applying actors would be far better suited to more tuplespaces/blackboard/Linda type model for concurrent coordination. A way of working that never caught on, but has always been attractive to me.
Note that Rust supports a form of structured programming (though only via thread-based concurrency, not via async), provided by the `std::thread::scope` API: https://doc.rust-lang.org/std/thread/fn.scope.html . The blog post above calls out an older version of this API as inspiration.
A more legible version: https://dspace.mit.edu/handle/1721.1/6952
https://en.wikipedia.org/wiki/Gul_Agha_(computer_scientist)
The first link returns a 403.
Both seem to be accessible from Germany. Maybe a geoblock?
May be of interest: Pony Language is designed from the ground up to support the Actor model.
https://www.ponylang.io/
Is Pony still an actively developed language? I remember watching several talks while they brought the language up to release, and read several of the accompanying papers. However, I thought with the primary corporate sponsor dropping the language it had gone basically EOL. Which was a pretty large bummer as I was very interested to see how the reference capability model of permissions and control worked at large scale for concurrency control and management (as well as its potential application to other domains).
It is still developed, although it feels a bit like it was mostly "done" 10 years ago.
https://github.com/ponylang/ponyc
Quite a few recent commits and their blog/X account mentioned their LSP now being bundled with the language...
https://www.ponylang.io/blog/2026/02/last-week-in-pony---feb...
Dunno, now it feels like the "hot" thing is either manual memory languages like Zig, Odin an Rust or languages with novel type systems like Lean, Koka, Idris, etc... GC'd "systems" languages like Nim, Crystal, Pony, Go, etc... all seem kind of old fashioned now.
Go seems to have some enduring affection and popularity for new projects and companies. I recently felt like a lot of the recent shift was less about GC and more about runtime characteristics (static binaries, lean resource consumption, lack of an in-your-face virtual machine).
It never felt like Nim, Pony, or Crystal were ever that popular that a diminished hype cycle registered as something thematic to me (not that I really intend to disagree with your perspective here).
D language is also supporting actor model in the standard library for its concurrent, parallel and distributed programming [1].
[1] D (programming language):
https://en.wikipedia.org/wiki/D_(programming_language)
Mandatory mention of notable actor languages:
I think Microsoft Orleans, Erlang OTP and Scala Play are probably most famous examples in use today.
Orleans is pretty cool! The project has matured nicely over the years (been something like 10 years?) and they have some research papers attached to it if you like reading up on the details. The nuget stats indicate a healthy amount of downloads too, more than one might expect.
One of the single most important things I've done in my career was going down the Actor Model -framework rabbit hole about 8 or 9 years ago, read a bunch of books on the topic, that contained a ton of hidden philosophy, amazing reasoning, conversations about real-time vs eventual consistency, Two-Generals-Problem - just a ton of enriching stuff, ways to think about data flows, the direction of the flow, immutability, event-logged systems and on and on. At the time CQS/CQRS was making heavy waves and everyone tried to implement DDD & Event-based (and/or service busses - tons of nasty queues...) and Actor Model (and F# for that matter) was such clean fresh breath of air from all the Enterprise complexity.
Would highly recommend going this path for anyone with time on their hands, its time well spent. I still call on that knowledge frequently even when doing OOP.
Do any of the books you read on the topic stand out as something you'd recommend?
Not books, but some inspiring resources. FModel [0] is a set of patterns for functional reactive DDD on the basis of event sourcing. In particular the Decider pattern is a great way to model aggregates, and test them using Scenario's that read like Gherkin in code (given.. when.. then). Combines well with actors to represent aggregates.
On the BEAM used by Erlang, Elixir, and Gleam actors are called processes, and this guide [1] delves into domain modeling with them.
[0] https://fraktalio.com/fmodel/
[1] https://happihacking.com/blog/posts/2025/the-gnome-village/
Applied Akka Patterns by Michael Nash, Wade Waldron (Oreilly) was very digestible and relevant at the time, might be dated by now. Just read the intro to get the vibe.
These days I would recommend picking a framework and then ask claude & friends to do a deep dive with you and build an example project out. Ask it to explain concepts, architecture, trade-offs, scalability considerations, hosting considerations, compare it with other frameworks, hook it up to storage systems (sqlite, postgresql, blob storage) and so on. Try running them within a wireguard network and so on. Very interesting learning to be found.
You can always join the Orleans Discord
I was disappointed when MS discontinued Axum, which I found pleasant to use and thought the language based approach was nicer than a library based solution like Orleans.
The Axum language had `domain` types, which could contain one or more `agent` and some state. Agents could have multiple functions and could share domain state, but not access state in other domains directly. The programming model was passing messages between agents over a typed `channel` using directional infix operators, which could also be used to build process pipelines. The channels could contain `schema` types and a state-machine like protocol spec for message ordering.
It didn't have "classes", but Axum files could live in the same projects as regular C# files and call into them. The C# compiler that came with it was modified to introduce an `isolated` keyword for classes, which prevented them from accessing `static` fields, which was key to ensuring state didn't escape the domain.
The software and most of the information was scrubbed from MS own website, but you can find an archived copy of the manual[1]. I still have a copy of the software installer somewhere but I doubt it would work on any recent Windows.
Sadly this project was axed before MS had embraced open source. It would've been nice if they had released the source when the decided to discontinue working on it.
[1]:https://web.archive.org/web/20110629202213/http://download.m...
I would think Akka in Java world is more famous than orleans
Akka's not open source anymore so people tend to look at similar or competing systems like Scala Play.
Apache Pekko is an open-source fork of Akka from before their licensing changes.
That's probably what they meant by "Scala Play".
Actor model is one of these things that really seduces me on paper, but my only exposure to it was in my consulting career, and that was to help migrate away from it. The use case seemed particularly adapted (integration of a bunch of remote devices with spotty connection), but it was practically a nightmare to debug... which was a problem since it was buggy.
To be fair, the problem was probably that particular implementation, but I'm wondering if there's any successful rollout of that model at any significant scale out there.
I was in a team that built a bigger telco project for machine to machine communication, using akka actors. It was okayish, the only thing that I hated was how the whole pattern spread through the whole code base
> It is generally believed that the next generation of computers will involve massively parallel architectures.
To this day - we have only taken advantage of parallel architectures in GPUs - a lot of software still runs on single CPU threads. most programming languages- are made optimized for single threads - yeah we might have threads, virtual threads, fibers etc - but how many people are using those on a daily basis?
I will be glad to be wrong about, but…
I was under the impression that parallel and concurrent code was the dominant paradigm for programming tasks currently going in most of the semi-mainstream domains. I am certainly willing to concede that I could just be in a bubble that thinks about and designs for concurrency and parallelism as a first class concern, but it doesn’t seem that way.
I mean one of the large features/touted benefits for Rust is the single mutable XOR multiple immutable semantics explicitly to assist with problems in parallel/concurrent code, all of the OTP languages are built on top of a ridiculously parallel and distributed first ‘VM’. It strikes me as peculiar that these types of languages and ecosystems would be so, apparently, popular if the primary use case of ‘safe’/resilient parallel/concurrent code was not a large concern.
It doesn't feel 1985. Feels very 2015. Really good insights. Remembering the hardware they had back then too, and ~14 years before Google took off.
Missing: (1985)