The article is very nice, thanks a lot for it. Especially since I hear the word concurrency and parallelism often thrown around without any distinction.
Very off topic, but I have read several times the argument that the rise of functional programming is due to it's easy concurrency (since functions don't have side effects) and that concurrency becomes more and more important due to moores law being dead (i.e. we can't scale the hardware up, we have to add cores to our processors).
Could someone with more experience comment on that? Is concurrency really easier in functional languages and is the rising importance of concurrency a valid reason to look into functional programming?
Is concurrency really easier in functional languages and is the rising importance of concurrency a valid reason to look into functional programming?
I think it depends on who's writing the code. A little bit of shared mutable state here and there isn't gonna kill you, but if the program's architecture is poor, that'll blow up and spread everywhere and the next thing you know is you're spending half your time worrying about data races, deadlocks, and a locking nightmare.
Shared-nothing architectures fix that; your synchronization will happen over a narrow range of well defined primitives (e.g. message queues), though this doesn't prevent race conditions.
Functional programming that encourages pure functions & immutable state is a bit of a straight jacket that keeps you from making a mess that blows up. I think it helps, and I wish $stuffAtWork were written in such a language because I'm tired of wrestling with terrible architecture (that I'm not really allowed to fix, given the time constraints).
The other thing that makes a difference is what your problem looks like. Sometimes concurrency is absolutely trivial, like dispatching sections of an array for threads to perform (independent) compute on, and come back with a result. Sometimes it's way more complicated...
Where you roughly read the '>>=' as follows: "the left side might take an arbitrary amount of time to produce a result. While it's off and doing its thing, go and do something else. Once it has produced the result, take it and feed it as a parameter to function on the right."
BTW, It's quite easy to implement your own concurrency monad, however: the real work is the wrapping of all the blocking system calls into this framework.
Lots of functional language featured end up copied into traditional languages (monads, immutability, pattern matching, lambdas, etc). The difference is that these are almost always more ergonomic in a functional language that was designed with these features in mind vs. having them bolted on afterwards
Concurrency is all about simultaneous effects. A language like Haskell is nice enough because it makes effects explicit, so you at least know where things can go wrong, but if you have a bunch of concurrent effectful Haskell threads running at the same time, you have the usual issues with nondeterminism. Functional languages to tend to emphasize higher-level concurrency patterns than old-school shared mutable state, so in practice it works OK.
Specifically, concurrent Haskell is good and also quite fast. It doesn't quite compare to Erlang in the concurrency department although the languages are very different in other ways, so there are reasons to prefer Haskell anyway. I think this shows that just having a functional language that emphasizes effect-free programming is not sufficient. If you want a good concurrent language, then you have to design it like that, and not simply rely on the absence of shared mutable state.
In practice, people write massively concurrent systems all the time, in the form of dynamic web sites that handle many concurrent requests, and synchronise access to a shared mutable state via a transactional database of some kind. These don't depend on fancy languages, but on a rigid architecture that tries to isolate synchronisation issues in the web server and database components.
While this article is about concurrency, I'll also add a note on parallelism: Parallel functional programming has IMO not been shown to work well in practice. While it is trivial to parallelise more or less any pure functional program correctly, it is very hard to actually gain meaningful speedup from it. The single point of parallelism is performance, and most current implementations of functional languages (including all general purpose functional languages I know of) simply have too many bottlenecks or pitfalls to scale for computational work as well in practice as one might expect in theory. For example, concurrent Haskell is fast because most of the actual work tends to be IO and GHC has an excellent parallel IO manager and scheduler, while parallel Haskell is hampered by, among other things, a slow garbage collector.
Do you refer to Erlang excelling in distributed concurrency, due to how its built to rely on the actor model? Or Erlang also excels in concurrency on a single machine?
Btw, what are some of those other things hampering Haskell's performance with parallel computing? Wouldn't the garbage collector mainly produce pauses instead of really destroying parallel performance? Although a concurrent garbage collector has recently been merged to the nightly GHC.
Do you refer to Erlang excelling in distributed concurrency, due to how its built to rely on the actor model? Or Erlang also excels in concurrency on a single machine?
Erlang has great concurrency also on a single machine. That is scales well to clusters is of course also good. And yes, it is the actor model that makes it work so well. That the individual processes are written in a (mostly) functional language is less important, I think. It could have been an imperative language and it wouldn't have made much difference, as long as process state was still isolated from other processes.
Btw, what are some of those other things hampering Haskell's performance with parallel computing? Wouldn't the garbage collector mainly produce pauses instead of really destroying parallel performance?
Haskell's garbage collector is pretty slow when run on multiple cores, and when run on a single core it becomes a bottleneck to parallelism (and can also trash the caches of the other cores). I don't know all the specific technical details of GHCs GC, though.
Another problematic thing is laziness, which is operationally control flow plus side effects. Those are exactly the things that make efficient parallelism difficult.
Although a concurrent garbage collector has recently been merged to the nightly GHC.
A concurrent GC is a different thing, that interleaves garbage collection with ordinary computation. That can be very useful, but it doesn't help with exploiting more cores.
Yep Erlang is fairly procedural as a language in a sense. I don't think being functional was ever a priority, more like a side-effect. I don't know how mailboxes and message passing would really work in a purely functional language anyway, could be awkward to work with them. Particularly with a functional (ML or what have you) type system (how to type mailboxes?).
it doesn't help with exploiting more cores.
Yeah you're right, I believe the aim is to get rid of those garbage collection pauses for better concurrency (not parallelism).
From what I understand, GHC can do pre-emptive or cooperative scheduling. Pre-emptive being better for concurrency, just like Erlang does it.
how mailboxes and message passing would really work in a purely functional language anyway, could be awkward to work with them.
It works really well and intuitively I would say, and even more; this is a common application to show the advantages of functional programming. Here is an article comparing sending messages asynchronously between threads in Elixir and Clojure (two common functional languages) https://bentomi.github.io/posts-output/2017-06-04-message-se...
Elixir and Clojure are not pure functional languages at all. They allow you to perform side effects freely, such as sending messages to different threads. They're not reflective of what the actor model would look like when implemented in a more purely functional language.
Moreover, I was mainly referring to using the actor model in a language with a static type system of functional languages (ML). What would be the type of the mailbox? The message? I believe even Facebook (Whatsapp) has struggled to find a solution to this question, they're developing a statically typed language on top of BEAM right now.
What you heard is correct. If you have pure functions and provided, that you have working primitives of concurrency, you can go to any place in you code, where you call a pure function and change the code, so that the function runs in a new concurrency (or parallelism) primitive. For example in a new thread or process. The places where this is useful can be discovered by profiling.
Another advantage is, that you can unit test each function on its own, without setting up a whole host of preconditions. That's because of referential transparency.
I'm just an idiot EE who learned to program, but it seems like functional languages without side-effects accomplish their task by basically working on data structures stored on the stack. That basically means each call stack has its own data structure. If you wrote threaded code so that each thread had its own data structure, and worked off a shared set of read-only data structures, you wouldn't need to worry about locking.
IME locking isn't really all that hard until you need to squeeze out performance. If you can handle one big dumb lock around everything, it's easy. The finer grained your locking gets, the harder it is to get right(even then, lock hierarchies can help to a great degree).
One big dumb lock only protects against data races. There are other classes of concurrency bugs that require smart locking to avoid, and it's always a pain.
For example, iterator invalidation. Which is probably the most common thing I've seen when people use dumb locks to make a concurrent program sound. Either your iterators need to be aware of the lock and hold it through their lifetime (which means practically only one iterator can exist, which is undesirable) or your datastructure needs to be aware of outstanding iterators to it and update them (which means you have added logic to simple mutations, which is also undesirable).
It's problems like that you look for in non-functional languages that require library authors to say things like "this is/isn't thread safe" in their documentation, while providing those guarantees manually. And as a consumer of the library you need to be aware of these things and look for them, and what an error looks like when someone messes up.
I don't know what you're trying to say about stacks/the call stack in functional languages, because they implement their patterns in variety of ways - including locking. It's just about providing particular guarantees in the API, and the benefits of those guarantees are that it prevents entire classes of bugs like iterator invalidation and data races.
There are also all sorts of clever data structures you can use that don't require duplication of the underlying data. The easiest is a singly linked list without insertion. No locks required.
Could someone with more experience comment on that? Is concurrency really easier in functional languages and is the rising importance of concurrency a valid reason to look into functional programming?
Only you know if concurrency is relavant to your work. A lot of internet stuff is deeply concurrent, but there's a lot of sequential work out there, too.
I have experience with Erlang. It's amazing for concurrency, but the reason is really not anything it gives you, but everything it prevents you from doing, or requires you to do.
Erlang's immutability is important because it means you can't save a reference to something and have it magically updated; if you want a value updated somewhere, you need to send the current value or request the current value at time of use; explicit synchronization makes concurrency clearer, and helps influence system design to reduce synchronization. Immutability also makes message passing easier; the content of a message can't change after it's composed which means the implementation is free to make a copy of the message if it's convenient and as a programmer, there's no need to consider the effects of changing something that you sent to another process. Immutability also makes garbage collection very simple; it's impossible to form a looped datastructure, so there's no need for mark and sweep, a simple copying collector is sufficient. Individual stacks per Erlang process (like a green thread) means GC pauses don't stop the world, only individual processes, and pause length is bounded and proportional to the memory use of the process.
You could certainly set up a similar environment in C or whatever traditional language; the problem would be enforcing your system design on your own code, and partitioning off library code (including libc and/or kernel syscalls) to ensure it doesn't impact your system design either.
I don't have more experience, necessarily, but I think the argument builds on the facts that pure functions don't have side-effects, which avoids many of the problems of simultaneous data editing, and also the use of immutable data structures, which can be shared freely (in a read-only manner).
Is concurrency really easier in functional languages
You can use the same approaches in non-functional languages. You can only use immutable data structures, and pure functions everywhere. Then you get the same benefits of easier reasoning about concurrent execution, as you get from a functional programming language.
So the point of using an actual functional language, is that the compiler pushes you very strongly towards programming this way. It's kind of like using a language with built in garbage collection. You could implement a garbage collector in C, and use it everywhere in your program where you allocate and release memory. But having it build into the language frees you up from having to think about it very much.
I don't get the immutable data structures thing though. If I have a vector of 1 billion numbers and I need to sort it it'll be unfeasible to make a copy every time I swap elements. RAM isn't infinite either.
So, deep down in the guts of the language's libraries there must be mutable operations. Which kind of defeats the whole point.
I don't get point of functional programming. Machines don't work that way.
Functional programming builds immutable semantics as an abstraction on top of mutable semantics. This abstraction layer includes internal optimizations that elide unnecessary copying, such as persistent data structures and "list fusion." When you're processing a list, the compiler can optimize away intermediate sublists. If you "copy" a list or sublist in Haskell, you don't actually get a copy, you get a new pointer to the same physical data, which is safe because it's immutable and garbage collected.
A key premise of functional programming is that the constraints of immutable semantics free the compiler up to do all sorts of performance optimizations, and the compiler can apply those optimizations much faster than a human developer can.
Also, when you really need a vector that you can mutate in place, you can still do that. Haskell has a mutable vector type. It's not the default idiom, but if you needed to sort a billion numbers in place, this is what you would use. Note all the function names prefixed with "unsafe": https://hackage.haskell.org/package/vector-0.12.1.2/docs/Dat...
I don't get point of functional programming. Machines don't work that way.
FP is useful because it provides an abstracted model that is pretty easy to reason about. It doesn't matter how it gets implemented, that's the point of the abstraction.
We don't write programs for machines to understand. We write programs for humans to understand.
We don't write programs for machines to understand. We write programs for humans to understand.
True, but that program ends up executing on a machine. You normally want that program to execute as efficiently as possible. 'Efficiency' can be measured several ways... time to complete, resources required, financial cost.
A case in point is our company switched from using Cassandra (JVM based) to ScyllaDB (a C++ clone of Cassandra). Query latencies were the same if not faster, and we needed fewer nodes to handle the same load. All because the language used has some mechanical sympathy for the CPU it's going to run on, and no GC.
I see Scala is somewhat the defacto language used for 'big data' stuff. I have no idea why. I reckon if some bean counter translated that choice into $$$ things would change.
True, but that program ends up executing on a machine. You normally want that program to execute as efficiently as possible. 'Efficiency' can be measured several ways... time to complete, resources required, financial cost.
No, you normally want that program to execute efficiently enough to do its required job. Abstractions are often necessarily costly, but that's ok because their value is greater than their cost. Otherwise, you'd just throw all the abstractions away and use assembler.
Query latencies were the same if not faster, and we needed fewer nodes to handle the same load. All because the language used has some mechanical sympathy for the CPU it's going to run on.
It seems like there's a whole load of reasons why it might have been more efficient on ScyllaDB that are unrelated to what language it was written in. Maybe the software design fit your requirements better? Maybe it was just better implemented? Maybe it was easier to configure for your load? Etc. etc. Programs aren't magically "more efficient" because they're written in C.
No, you normally want that program to execute efficiently enough to do its required job
but... the "required job" is to go as fast as possible - in two ways :
1/ You must go faster than your competitors. Else people will switch, your software will get bad reviews, etc.
2/ you must go fast enough for anything that people will throw at you. And, in many cases, people can throw arbitrarily large problems to your program, and telling them "sorry but it will take 1 hour to process your 2TB dataset" just won't cut it at all. And even when you manage to cut it, then people come at you like "now can this run on a raspberry pi?"
Take for instance image processing software, audio or multimedia software... they will never be fast enough, and there is always an use case where getting a 1% perf improvement will allow a project to make instead of break (especially when working with uncompromising artists).
but... the "required job" is to go as fast as possible - in two ways :
This is sometimes true, but hardly ever. Most software is not image/audio processing software. Most software is not even interacted with directly by humans. Most software exists as a component as part of a larger system, the performance of which is typically determined by a small fraction of that system. In other words, most software is not on the critical path.
Even if you're on the critical path, the performance requirements could be of the form "Here's some data, I need an answer in an hour". If your current system spends 2 minutes computing and delivering the result, why spend time and money making it faster? Just because you're on the critical path for you, doesn't mean you're on the client's critical path.
This is the whole point of performance engineering - identifying which bits are important and then figuring out how to optimise those. Optimising everything to run as fast as possible is a waste of time and effort - worse, it often degrades the overall system by making previously easy-to-understand components more complex and more difficult to reason about. See the whole "Worse is better" thing.
If you're on the critical performance path, go nuts. Throw away the abstractions and take it down to the metal. If you're not? Why bother?
Perhaps the 'I need answer in a hour' is currently acceptable because that's how long people are used to waiting for the answer. If they knew that you could get that answer in 2 minute do you not think they'd ask for that instead?
Like I mentioned previously, Scala's use in Big Data is perplexing. I reckon Cloud providers are making an absolute mint from design / language inefficiencies.
It seems like you're saying that abstractions are worthless unless they're perfect, which is obviously not true.
Since we are not quite there yet, could you explain in broad terms how a large collection is sorted while maintaining performance and immutability.
What do you mean by "maintaining performance"? Relative to what? Who's asking for the sorted collection? How are they going to access it? What are the resource constraints? Give me a real-world example.
Don't get me wrong, I'm not saying that FP is the answer to everything. It's a useful tool, and part of being good at software development is knowing what tools to use and when.
The simplest way, but which essentially dodged the question, is to utilize a language with uniqueness types. Where the data structure may only exist in a single place and there can be no aliasing. Then any operation on the data structure occurs and can be done ‘in place’ returning the new unique data structure. This is similar (but not identical) to linear and affine type systems, of which Idris is the only language I know of that actually implements such a type system.
Also, along the same lines, it is certainly possible to craft types in some FP languages which allow for this same logic to be captured, mostly via some monad construct.
This reply is late, so if you don’t see it or care I don’t blame you.
To start, I am aware of Rust and left it out of the above comment purposefully. While Rust does indeed make many ‘functional programming’ idioms available and, some would say, is basically an impure, eager functional language, I don’t tend to view Rust as a language featuring persistent, immutable data structures. I certainly don’t think that is a negative characterization, just how I see things. I find that one of the defining features of the functional programming ‘persistent’ data structure is the structural sharing that is all too frequent in most of the mainline FP languages (Haskell, ML, Clojure, etc.). Rudy’s focus on low level/systems level programming is much more likely to use standard imperative data structures without the sharing and deep pointer chasing style seemingly inherent in FP implementations.
I have had a few spirited web-discussions with people about the claim that Rust has an affine type system. I don’t think that stance is accurate, from a type theoretic stance. I subscribe to the theoretical standpoint that Rust has a standard non-substructural type systems with constraints placed on types that meet a mutability predicate. I think that those constraints are better described as a uniqueness constraint per instance of a type. But the sequent calculus presentations for Rust (especially concerning mutable data) would look very similar and act very similar as well. So really it’s much ado about nothing, just fun to talk about.
Sorry for the wall of text, and rambling about type theory. Stay safe.
The key idea is that there are functional data structures that only require you to copy a small part and reuse a lot of the old. For example, if you have a binary tree and you want to swap out the root value, you create a new root node that simply uses the existing children.
So, deep down in the guts of the language's libraries there must be mutable operations. Which kind of defeats the whole point.
Why does it defeat the point? If you can guarantee that the mutability doesn't leak (there are ways to do this), it behaves just like a pure implementation.
The implementations of immutable data structures in most concurrency aware functional languages are smarter than that. Take Clojure as an example: "All of the Clojure collections are immutable and persistent. In particular, the Clojure collections support efficient creation of 'modified' versions, by utilizing structural sharing, and make all of their performance bound guarantees for persistent use." https://hypirion.com/musings/understanding-persistent-vector...
Mutability is an access pattern and not an underlying data layout.
In fact, some structures that lend themselves to immutability represent the hardware better. Take your vector of a billion elements. It probably is too big for L1/L2 cache. Is it actually a billion things next to each other in memory? Not at all.
If you used something like a radix balanced balanced tree to represent your vector, you get amortized O(1) access and mutation can be implemented using copy on write at individual leaves of the tree. And your abstraction is much friendlier with the cache, because it reflects how the hardware works much better than a simple vector.
In practice, immutable data structures can be much more performant at scale than pretty much anything. They're not just convenient for soundness. Trivial data structures like vectors are really only useful when you're talking hundreds to the low thousands of elements, at which point who cares how much memory they take up?
IMHO the advantages of functional programming is often looked for in examples that are too small for them to exist.
There is no advantage in sorting a billion numbers the functional way.
There is an advantage to gain, though, when the unsorted numbers are used for other purposes besides sorting. Then, functional programming demands that the output of sorting is written to a new 1-billion array instead of "in place", so other clients can consume the same array in parallel without being disturbed by the sorting.
So, deep down in the guts of the language's libraries there must be mutable operations. Which kind of defeats the whole point.
It doesn't defeat the point if the abstraction doesn't leak. If a pure functional language offers sorting as a pure primitive, then language-wise it doesn't matter that it actually mutates memory internally.
The problem with "mostly" pure functional languages is that the abstraction leaks enough to make it useless.
No machine works like the high level languages, including C.
Many believe the portable Assembler story and aren't aware that ISO C is defined in terms of an abstract machine.
The abstraction level that functional programming allows for, all the way back to Lisp, is to take advantage of the underlying hardware without being forced to hand code lowlevel details.
Something written in ML like language could be running on 70's, 80's, NUMA architectures, GPGPU, whatever, without changing one single line of code. Naturally we are not speaking about the same runtime implementation.
Whereas a language like C, requires a bunch of dialects to actually make use of the low level details, only exposed via Assembly and not part of ISO C.
Yeah but those "low level details" are not portable between computer architectures, so why not stop whining.
And you can actually USE these low level details if you really want, with very low friction (if you are willing to lock down to specific architectures). Are you STILL whining?
C level (or Pascal if you're masochistic) is still a very nice abstraction, and for a lot of people it's everything they care about 99% of the time, to get all the control they need, while simultaneously ensuring they're not wasting any time fighting pointless language features that are mutually incompatible.
Sorry, but that's a rough shifting of goalposts. The statement that I argued against clearly was "C is not low level". You might not have noticed that you shifted, because you have an agenda.
Comments
The article is very nice, thanks a lot for it. Especially since I hear the word concurrency and parallelism often thrown around without any distinction.
Very off topic, but I have read several times the argument that the rise of functional programming is due to it's easy concurrency (since functions don't have side effects) and that concurrency becomes more and more important due to moores law being dead (i.e. we can't scale the hardware up, we have to add cores to our processors).
Could someone with more experience comment on that? Is concurrency really easier in functional languages and is the rising importance of concurrency a valid reason to look into functional programming?
I think it depends on who's writing the code. A little bit of shared mutable state here and there isn't gonna kill you, but if the program's architecture is poor, that'll blow up and spread everywhere and the next thing you know is you're spending half your time worrying about data races, deadlocks, and a locking nightmare.
Shared-nothing architectures fix that; your synchronization will happen over a narrow range of well defined primitives (e.g. message queues), though this doesn't prevent race conditions.
Functional programming that encourages pure functions & immutable state is a bit of a straight jacket that keeps you from making a mess that blows up. I think it helps, and I wish $stuffAtWork were written in such a language because I'm tired of wrestling with terrible architecture (that I'm not really allowed to fix, given the time constraints).
The other thing that makes a difference is what your problem looks like. Sometimes concurrency is absolutely trivial, like dispatching sections of an array for threads to perform (independent) compute on, and come back with a result. Sometimes it's way more complicated...
Yes. Concurrency is easier in functional languages.
Many of them provide a concurrency monad which allows you to write expressions like this:
Where you roughly read the '>>=' as follows: "the left side might take an arbitrary amount of time to produce a result. While it's off and doing its thing, go and do something else. Once it has produced the result, take it and feed it as a parameter to function on the right."BTW, It's quite easy to implement your own concurrency monad, however: the real work is the wrapping of all the blocking system calls into this framework.
This sounds a bit like async in some other languages.
Lots of functional language featured end up copied into traditional languages (monads, immutability, pattern matching, lambdas, etc). The difference is that these are almost always more ergonomic in a functional language that was designed with these features in mind vs. having them bolted on afterwards
Concurrency is all about simultaneous effects. A language like Haskell is nice enough because it makes effects explicit, so you at least know where things can go wrong, but if you have a bunch of concurrent effectful Haskell threads running at the same time, you have the usual issues with nondeterminism. Functional languages to tend to emphasize higher-level concurrency patterns than old-school shared mutable state, so in practice it works OK.
Specifically, concurrent Haskell is good and also quite fast. It doesn't quite compare to Erlang in the concurrency department although the languages are very different in other ways, so there are reasons to prefer Haskell anyway. I think this shows that just having a functional language that emphasizes effect-free programming is not sufficient. If you want a good concurrent language, then you have to design it like that, and not simply rely on the absence of shared mutable state.
In practice, people write massively concurrent systems all the time, in the form of dynamic web sites that handle many concurrent requests, and synchronise access to a shared mutable state via a transactional database of some kind. These don't depend on fancy languages, but on a rigid architecture that tries to isolate synchronisation issues in the web server and database components.
While this article is about concurrency, I'll also add a note on parallelism: Parallel functional programming has IMO not been shown to work well in practice. While it is trivial to parallelise more or less any pure functional program correctly, it is very hard to actually gain meaningful speedup from it. The single point of parallelism is performance, and most current implementations of functional languages (including all general purpose functional languages I know of) simply have too many bottlenecks or pitfalls to scale for computational work as well in practice as one might expect in theory. For example, concurrent Haskell is fast because most of the actual work tends to be IO and GHC has an excellent parallel IO manager and scheduler, while parallel Haskell is hampered by, among other things, a slow garbage collector.
Do you refer to Erlang excelling in distributed concurrency, due to how its built to rely on the actor model? Or Erlang also excels in concurrency on a single machine?
Btw, what are some of those other things hampering Haskell's performance with parallel computing? Wouldn't the garbage collector mainly produce pauses instead of really destroying parallel performance? Although a concurrent garbage collector has recently been merged to the nightly GHC.
Erlang has great concurrency also on a single machine. That is scales well to clusters is of course also good. And yes, it is the actor model that makes it work so well. That the individual processes are written in a (mostly) functional language is less important, I think. It could have been an imperative language and it wouldn't have made much difference, as long as process state was still isolated from other processes.
Haskell's garbage collector is pretty slow when run on multiple cores, and when run on a single core it becomes a bottleneck to parallelism (and can also trash the caches of the other cores). I don't know all the specific technical details of GHCs GC, though.
Another problematic thing is laziness, which is operationally control flow plus side effects. Those are exactly the things that make efficient parallelism difficult.
A concurrent GC is a different thing, that interleaves garbage collection with ordinary computation. That can be very useful, but it doesn't help with exploiting more cores.
Yep Erlang is fairly procedural as a language in a sense. I don't think being functional was ever a priority, more like a side-effect. I don't know how mailboxes and message passing would really work in a purely functional language anyway, could be awkward to work with them. Particularly with a functional (ML or what have you) type system (how to type mailboxes?).
Yeah you're right, I believe the aim is to get rid of those garbage collection pauses for better concurrency (not parallelism).
From what I understand, GHC can do pre-emptive or cooperative scheduling. Pre-emptive being better for concurrency, just like Erlang does it.
It works really well and intuitively I would say, and even more; this is a common application to show the advantages of functional programming. Here is an article comparing sending messages asynchronously between threads in Elixir and Clojure (two common functional languages) https://bentomi.github.io/posts-output/2017-06-04-message-se...
Elixir and Clojure are not pure functional languages at all. They allow you to perform side effects freely, such as sending messages to different threads. They're not reflective of what the actor model would look like when implemented in a more purely functional language.
Moreover, I was mainly referring to using the actor model in a language with a static type system of functional languages (ML). What would be the type of the mailbox? The message? I believe even Facebook (Whatsapp) has struggled to find a solution to this question, they're developing a statically typed language on top of BEAM right now.
There's been plenty of good discussion around implementing a strong static type system around BEAM, here's some bits for reference: https://elixirforum.com/t/how-hard-would-it-be-to-have-a-str...
What you heard is correct. If you have pure functions and provided, that you have working primitives of concurrency, you can go to any place in you code, where you call a pure function and change the code, so that the function runs in a new concurrency (or parallelism) primitive. For example in a new thread or process. The places where this is useful can be discovered by profiling.
Another advantage is, that you can unit test each function on its own, without setting up a whole host of preconditions. That's because of referential transparency.
You are confusing Moores' Law with Dennard scaling. Without Moores' Law you're not adding more transistors and therefore more cores.
I'm just an idiot EE who learned to program, but it seems like functional languages without side-effects accomplish their task by basically working on data structures stored on the stack. That basically means each call stack has its own data structure. If you wrote threaded code so that each thread had its own data structure, and worked off a shared set of read-only data structures, you wouldn't need to worry about locking.
IME locking isn't really all that hard until you need to squeeze out performance. If you can handle one big dumb lock around everything, it's easy. The finer grained your locking gets, the harder it is to get right(even then, lock hierarchies can help to a great degree).
One big dumb lock only protects against data races. There are other classes of concurrency bugs that require smart locking to avoid, and it's always a pain.
For example, iterator invalidation. Which is probably the most common thing I've seen when people use dumb locks to make a concurrent program sound. Either your iterators need to be aware of the lock and hold it through their lifetime (which means practically only one iterator can exist, which is undesirable) or your datastructure needs to be aware of outstanding iterators to it and update them (which means you have added logic to simple mutations, which is also undesirable).
It's problems like that you look for in non-functional languages that require library authors to say things like "this is/isn't thread safe" in their documentation, while providing those guarantees manually. And as a consumer of the library you need to be aware of these things and look for them, and what an error looks like when someone messes up.
I don't know what you're trying to say about stacks/the call stack in functional languages, because they implement their patterns in variety of ways - including locking. It's just about providing particular guarantees in the API, and the benefits of those guarantees are that it prevents entire classes of bugs like iterator invalidation and data races.
There are also all sorts of clever data structures you can use that don't require duplication of the underlying data. The easiest is a singly linked list without insertion. No locks required.
The problem is that if you have one big dumb lock around everything then you have no actual concurrency or parallelism.
Depends on what the big dumb lock protects. You may still be able to process data you accessed while holding the big dumb lock.
Only you know if concurrency is relavant to your work. A lot of internet stuff is deeply concurrent, but there's a lot of sequential work out there, too.
I have experience with Erlang. It's amazing for concurrency, but the reason is really not anything it gives you, but everything it prevents you from doing, or requires you to do.
Erlang's immutability is important because it means you can't save a reference to something and have it magically updated; if you want a value updated somewhere, you need to send the current value or request the current value at time of use; explicit synchronization makes concurrency clearer, and helps influence system design to reduce synchronization. Immutability also makes message passing easier; the content of a message can't change after it's composed which means the implementation is free to make a copy of the message if it's convenient and as a programmer, there's no need to consider the effects of changing something that you sent to another process. Immutability also makes garbage collection very simple; it's impossible to form a looped datastructure, so there's no need for mark and sweep, a simple copying collector is sufficient. Individual stacks per Erlang process (like a green thread) means GC pauses don't stop the world, only individual processes, and pause length is bounded and proportional to the memory use of the process.
You could certainly set up a similar environment in C or whatever traditional language; the problem would be enforcing your system design on your own code, and partitioning off library code (including libc and/or kernel syscalls) to ensure it doesn't impact your system design either.
I don't have more experience, necessarily, but I think the argument builds on the facts that pure functions don't have side-effects, which avoids many of the problems of simultaneous data editing, and also the use of immutable data structures, which can be shared freely (in a read-only manner).
You can use the same approaches in non-functional languages. You can only use immutable data structures, and pure functions everywhere. Then you get the same benefits of easier reasoning about concurrent execution, as you get from a functional programming language.
So the point of using an actual functional language, is that the compiler pushes you very strongly towards programming this way. It's kind of like using a language with built in garbage collection. You could implement a garbage collector in C, and use it everywhere in your program where you allocate and release memory. But having it build into the language frees you up from having to think about it very much.
I don't get the immutable data structures thing though. If I have a vector of 1 billion numbers and I need to sort it it'll be unfeasible to make a copy every time I swap elements. RAM isn't infinite either.
So, deep down in the guts of the language's libraries there must be mutable operations. Which kind of defeats the whole point.
I don't get point of functional programming. Machines don't work that way.
Functional programming builds immutable semantics as an abstraction on top of mutable semantics. This abstraction layer includes internal optimizations that elide unnecessary copying, such as persistent data structures and "list fusion." When you're processing a list, the compiler can optimize away intermediate sublists. If you "copy" a list or sublist in Haskell, you don't actually get a copy, you get a new pointer to the same physical data, which is safe because it's immutable and garbage collected.
A key premise of functional programming is that the constraints of immutable semantics free the compiler up to do all sorts of performance optimizations, and the compiler can apply those optimizations much faster than a human developer can.
Also, when you really need a vector that you can mutate in place, you can still do that. Haskell has a mutable vector type. It's not the default idiom, but if you needed to sort a billion numbers in place, this is what you would use. Note all the function names prefixed with "unsafe": https://hackage.haskell.org/package/vector-0.12.1.2/docs/Dat...
FP is useful because it provides an abstracted model that is pretty easy to reason about. It doesn't matter how it gets implemented, that's the point of the abstraction.
We don't write programs for machines to understand. We write programs for humans to understand.
True, but that program ends up executing on a machine. You normally want that program to execute as efficiently as possible. 'Efficiency' can be measured several ways... time to complete, resources required, financial cost.
A case in point is our company switched from using Cassandra (JVM based) to ScyllaDB (a C++ clone of Cassandra). Query latencies were the same if not faster, and we needed fewer nodes to handle the same load. All because the language used has some mechanical sympathy for the CPU it's going to run on, and no GC.
I see Scala is somewhat the defacto language used for 'big data' stuff. I have no idea why. I reckon if some bean counter translated that choice into $$$ things would change.
No, you normally want that program to execute efficiently enough to do its required job. Abstractions are often necessarily costly, but that's ok because their value is greater than their cost. Otherwise, you'd just throw all the abstractions away and use assembler.
It seems like there's a whole load of reasons why it might have been more efficient on ScyllaDB that are unrelated to what language it was written in. Maybe the software design fit your requirements better? Maybe it was just better implemented? Maybe it was easier to configure for your load? Etc. etc. Programs aren't magically "more efficient" because they're written in C.
but... the "required job" is to go as fast as possible - in two ways :
1/ You must go faster than your competitors. Else people will switch, your software will get bad reviews, etc.
2/ you must go fast enough for anything that people will throw at you. And, in many cases, people can throw arbitrarily large problems to your program, and telling them "sorry but it will take 1 hour to process your 2TB dataset" just won't cut it at all. And even when you manage to cut it, then people come at you like "now can this run on a raspberry pi?"
Take for instance image processing software, audio or multimedia software... they will never be fast enough, and there is always an use case where getting a 1% perf improvement will allow a project to make instead of break (especially when working with uncompromising artists).
This is sometimes true, but hardly ever. Most software is not image/audio processing software. Most software is not even interacted with directly by humans. Most software exists as a component as part of a larger system, the performance of which is typically determined by a small fraction of that system. In other words, most software is not on the critical path.
Even if you're on the critical path, the performance requirements could be of the form "Here's some data, I need an answer in an hour". If your current system spends 2 minutes computing and delivering the result, why spend time and money making it faster? Just because you're on the critical path for you, doesn't mean you're on the client's critical path.
This is the whole point of performance engineering - identifying which bits are important and then figuring out how to optimise those. Optimising everything to run as fast as possible is a waste of time and effort - worse, it often degrades the overall system by making previously easy-to-understand components more complex and more difficult to reason about. See the whole "Worse is better" thing.
If you're on the critical performance path, go nuts. Throw away the abstractions and take it down to the metal. If you're not? Why bother?
Perhaps the 'I need answer in a hour' is currently acceptable because that's how long people are used to waiting for the answer. If they knew that you could get that answer in 2 minute do you not think they'd ask for that instead?
Like I mentioned previously, Scala's use in Big Data is perplexing. I reckon Cloud providers are making an absolute mint from design / language inefficiencies.
I have never been in the case where the answer to the question "when" wasn't "as fast as physically possible".
That works if an abstraction doesn't leak.
Since we are not quite there yet, could you explain in broad terms how a large collection is sorted while maintaining performance and immutability.
Here is a worked example of how to implement radix sort using only immutable data-parallel operations: https://futhark-lang.org/examples/radix-sort.html
There will of course be "impure" writes at the machine level, but at the language level, the abstraction doesn't leak.
It seems like you're saying that abstractions are worthless unless they're perfect, which is obviously not true.
What do you mean by "maintaining performance"? Relative to what? Who's asking for the sorted collection? How are they going to access it? What are the resource constraints? Give me a real-world example.
Don't get me wrong, I'm not saying that FP is the answer to everything. It's a useful tool, and part of being good at software development is knowing what tools to use and when.
No need for a real-world example or an exact definition of performance or efficiency, just make up something sensible.
How can efficient sorting be implemented with immutable data structures? Genuinly curious.
The simplest way, but which essentially dodged the question, is to utilize a language with uniqueness types. Where the data structure may only exist in a single place and there can be no aliasing. Then any operation on the data structure occurs and can be done ‘in place’ returning the new unique data structure. This is similar (but not identical) to linear and affine type systems, of which Idris is the only language I know of that actually implements such a type system.
Also, along the same lines, it is certainly possible to craft types in some FP languages which allow for this same logic to be captured, mostly via some monad construct.
Rust has an affine type system, and https://docs.rs/im/14.3.0/im/ implements this https://docs.rs/im/14.3.0/im/#in-place-mutation
This reply is late, so if you don’t see it or care I don’t blame you.
To start, I am aware of Rust and left it out of the above comment purposefully. While Rust does indeed make many ‘functional programming’ idioms available and, some would say, is basically an impure, eager functional language, I don’t tend to view Rust as a language featuring persistent, immutable data structures. I certainly don’t think that is a negative characterization, just how I see things. I find that one of the defining features of the functional programming ‘persistent’ data structure is the structural sharing that is all too frequent in most of the mainline FP languages (Haskell, ML, Clojure, etc.). Rudy’s focus on low level/systems level programming is much more likely to use standard imperative data structures without the sharing and deep pointer chasing style seemingly inherent in FP implementations.
I have had a few spirited web-discussions with people about the claim that Rust has an affine type system. I don’t think that stance is accurate, from a type theoretic stance. I subscribe to the theoretical standpoint that Rust has a standard non-substructural type systems with constraints placed on types that meet a mutability predicate. I think that those constraints are better described as a uniqueness constraint per instance of a type. But the sequent calculus presentations for Rust (especially concerning mutable data) would look very similar and act very similar as well. So really it’s much ado about nothing, just fun to talk about.
Sorry for the wall of text, and rambling about type theory. Stay safe.
The key idea is that there are functional data structures that only require you to copy a small part and reuse a lot of the old. For example, if you have a binary tree and you want to swap out the root value, you create a new root node that simply uses the existing children.
Why does it defeat the point? If you can guarantee that the mutability doesn't leak (there are ways to do this), it behaves just like a pure implementation.
The implementations of immutable data structures in most concurrency aware functional languages are smarter than that. Take Clojure as an example: "All of the Clojure collections are immutable and persistent. In particular, the Clojure collections support efficient creation of 'modified' versions, by utilizing structural sharing, and make all of their performance bound guarantees for persistent use." https://hypirion.com/musings/understanding-persistent-vector...
Mutability is an access pattern and not an underlying data layout.
In fact, some structures that lend themselves to immutability represent the hardware better. Take your vector of a billion elements. It probably is too big for L1/L2 cache. Is it actually a billion things next to each other in memory? Not at all.
If you used something like a radix balanced balanced tree to represent your vector, you get amortized O(1) access and mutation can be implemented using copy on write at individual leaves of the tree. And your abstraction is much friendlier with the cache, because it reflects how the hardware works much better than a simple vector.
In practice, immutable data structures can be much more performant at scale than pretty much anything. They're not just convenient for soundness. Trivial data structures like vectors are really only useful when you're talking hundreds to the low thousands of elements, at which point who cares how much memory they take up?
IMHO the advantages of functional programming is often looked for in examples that are too small for them to exist.
There is no advantage in sorting a billion numbers the functional way.
There is an advantage to gain, though, when the unsorted numbers are used for other purposes besides sorting. Then, functional programming demands that the output of sorting is written to a new 1-billion array instead of "in place", so other clients can consume the same array in parallel without being disturbed by the sorting.
It doesn't defeat the point if the abstraction doesn't leak. If a pure functional language offers sorting as a pure primitive, then language-wise it doesn't matter that it actually mutates memory internally.
The problem with "mostly" pure functional languages is that the abstraction leaks enough to make it useless.
Does this make e.g. erlang useless? Processes have mutable dictionaries and you can perform I/O anywhere in your functions..
No machine works like the high level languages, including C.
Many believe the portable Assembler story and aren't aware that ISO C is defined in terms of an abstract machine.
The abstraction level that functional programming allows for, all the way back to Lisp, is to take advantage of the underlying hardware without being forced to hand code lowlevel details.
Something written in ML like language could be running on 70's, 80's, NUMA architectures, GPGPU, whatever, without changing one single line of code. Naturally we are not speaking about the same runtime implementation.
Whereas a language like C, requires a bunch of dialects to actually make use of the low level details, only exposed via Assembly and not part of ISO C.
Yeah but those "low level details" are not portable between computer architectures, so why not stop whining.
And you can actually USE these low level details if you really want, with very low friction (if you are willing to lock down to specific architectures). Are you STILL whining?
C level (or Pascal if you're masochistic) is still a very nice abstraction, and for a lot of people it's everything they care about 99% of the time, to get all the control they need, while simultaneously ensuring they're not wasting any time fighting pointless language features that are mutually incompatible.
In the end everybody's using whatever they like. https://twitter.com/fabynou/status/1242517783092400129
As long as people are lyabble for the security exploits they produce I am all for it, hammer them hard with lawsuits.
Sorry, but that's a rough shifting of goalposts. The statement that I argued against clearly was "C is not low level". You might not have noticed that you shifted, because you have an agenda.
I have always been quite clear about it, it is even part of my profile.
But please let's stay focused and keep discussing specific points. When changing topic, we can make a new subthread.
Fair enough.
Not defending fp but if memory is a limitation why not create a sortable index and leave the data alone?
Yes. I can say that doing concurrency in Clojure is particularly easy, check for yourself: https://www.braveclojure.com/core-async/