How to make any immutable data structure distributed
unison-lang.org
unison-lang.org
If you look at the architecture of something like Apache Beam, while you describe your computations in a language like Java or Python, you're really using a DSL for creating an immutable DAG that processes chunks of immutable data, persisiting each chunk (being loose with terms here) for durability, 'modifying' it in the immutable sense and then passing it on to the next edge and so on.
In a single standalone system, many argue that a purely immutable conceptual approach has pros and cons. In the Big Data world, I've never heard anyone argue for an imperative approach. Immutability is necessary, and as soon as you need immutability you want all the other goodies that facilitate it in a clean way like monads, lenses, and the like.
1) creating an immutable DAG that processes chunks of immutable data,
2) persisting each chunk (being loose with terms here) for durability,
3) 'modifying' it in the immutable sense
4) and then passing it on to the next edge and so on
And when you say "'modifying' it in the immutable sense", does this mean that the original data stays untouched, but "instructions" on how to modify the data get "pinned" to the data?
Then every other subsequent step in the DAG is just modifying the "instructions"?
If I understand you correctly, your point about how this is where FP shines is really illustrated in #4. You end up passing instructions around instead of the data. But how FP instructions can be modified and modified again really baffles me. The closest I can get to understanding it in practical terms is by using something like Clojure and thinking about how Clojure is just data, and you can modify, or build upon Clojure by modifying like you modify data. I struggle extending this to another language like Scala or JavaScript.
Now if you're doing a bunch of computations off of tens or hundreds machines you don't want to fail the whole thing because one hypervisor crashes or there's a JVM segfault or whatnot. So each step is usually saved to disk via checkpoint and then moved along to the next transformation in batches, so if the subsequent transfomation is where it dies, you have a safe spot to restart that particular computation from.
In addition, your (chunk of) data might need to be shipped to an additional machine to be transformed so you're not so much 'updating' data as you are creating new data in a space efficient way of the changes and shuffling those along the various steps.
(update-fn (fn [{:keys [address]}
:as customer]
(if address (update customer :address clojure.string/capitalize) customer))
customers)
(update-vals customers #(update-in % [:address 0] str/capitalize))In this sense it's closer to Clojure's transients, if Clojure came prepackaged with special mutation syntax (notably assignment via =) and if transient-ness could be propagated transitively to all nested structures in a data structure.
Most good FP data structures will be tree like, but with a low branching factor and chunky leafs. Now of course that does not save you from lots of memory consumption if you use a language/platform where even primitives are boxed, like the JVM. But that is a completely separate topic.
To be clear it is certainly a solvable problem (as this post shows) but it can make things difficult.
The point is neither copying the whole structure nor diffs are as easy as mutex + inner mutation.
Although I don't know what you mean by growing lenses.
Mutex + inner mutation is no easier than CAS (which is the usual solution with concurrent writing of immutable data structures) a la Java AtomicReference or STM (another popular one) and in my opinion significantly harder as soon as you have multiple mutexes.
I am not against FP I think the point of "it is good if you can fix performance" is very accurate. I just think it is also important to acknowledge that the paradigm can be more complex in situations where it is supposed to help leading to a mixed bag.
Similar to distributed databases. Your database can now be phenomenally powerful but you can't do a stored proc anymore without losing that power.
It can be a very effective and totally worth it but it isn't a pure win necessarily.
Why is that?
CAS is a primitive, it isn't complex itself but it can be complex to work with once you are talking non trivial work. Just like locks. A global lock is dumb simple but a real locking system can be as complex as you will let it.
Right but both of those things are true for locks too right? CAS seems no harder than locks.
Additionally while multiple locks is annoying it is way easier than multiple CAS. "Have a global order for locks" is the hard but solvable problem for multiple locks. For CAS if you need to CAS two dependent things you... I don't know it depends.
It's hidden in both cases (usually CAS instructions are hidden behind an `update` interface as in Java's Atomic* family rather than directly used in the same way that generally a lock is an interface for a TAS instruction/spin lock + upgrading to wait queue).
> For CAS if you need to CAS two dependent things you... I don't know it depends.
You use nested atomic references. Just like locks it's not a great way of doing things, and an analogous problem to ordering locks rears its head (by virtue of nesting you cannot mess up ordering in the strict sense, but you can accidentally "cross the boundaries" of two atomics in an update that goes against the nesting order), but it's doable in the same way as locks.
The usual CAS-like but better approach is STM (which is where immutability really shines).
I'm still not seeing how CAS is any harder than locks.
It's very costly though.
Under the hood it's a effectively a single CAS instruction that loops on failure (which only occurs under contention, but then you have waiting with locks too).
If you mean the manning book "Functional Programming in Scala", I don't even use Scala, yet I feel I got so much out of that book. It's the best functional programming book I've read.
It would recommend the book to anyone that wants to learn FP, regardless if you will be using Scala.
I would recommend Essential Scala for the basics. Scala with Cats if you're going to be working with the Typelevel stack (a popular set of FP libraries). Lots of other resources but those two alone will take you far.
1. The posts mention Spark, but were you (the Unison team) also inspired by dataflow / stream processing engines (e.g. Flink, Beam, Timely Dataflow etc.) or any other technologies?
2. Relatedly, are there any plans to add a stream processing API? Or perhaps that would belong in a 3rd-party library?
3. Broadly, where do you think (in your opinion) Unison fits in the ecosystem of tooling? Is Unison specialized at solving big data problems that require distributed execution e.g. something I pull out of my toolbox for a problem I'd otherwise solve with Spark? Or is it a truly general-purpose programming language?
4. With the Tree example, you show the "wrong" way and then the "right" way -- one problem I've experienced in the past is how do you as a new user know the right way. For example, I wrap my entire program in IO then .unsafeRunSync it -- technically following the rules, but got nothing out of doing that. Have you thought about how the APIs could guide the user to the idiomatic solution? Perhaps via types?
By the way, I really loved the FP in Scala book, it was my first step into FP -- thank you and Runar for writing it
We’re planning on doing several of these articles to show different applications. I think some distributed stream processing library would be super cool and there are lots of sources of inspiration for that like you mention. It would be a more interesting translation than the Spark-like stuff though. Likely using the channels ability for message passing.
> By the way, I really loved the FP in Scala book, it was my first step into FP -- thank you and Runar for writing it
That’s great to hear!
> the thing that would have helped me most in avoiding some of the missteps might have been a richer test interpreter, where I could have "mocked" a network
This is a really intriguing idea. Debugging distributed can be a huge headache, and I can see a lot of value in having something like that.
> We’re planning on doing several of these articles to show different applications.
Looking forward to future articles and definitely will be following this project!
Some basic IO functionality is supported by the `base` library (the standard lib) which contains functions like `openFile` which are effectful functions expressed in terms of the `IO` ability.
1. Explicitly marking non-local _data_ vs. in-memory. A very cool idea -- languages and libraries I've used all seem to try to make this transparent (unsuccessfully, as indicated by the random serialization errors you get)
2. Making the noise around serialization and RPC transparent to the user
3. Bring the code to the data, not the data to the code
4. Immutability, FP, and I like the syntax -- no coincidence in its similarities to Haskell and Scala, I'm sure :)
These are all pain points in a lot of existing "big data systems" I've used, where they either solve the problem half-baked or don't address at all. I'm excited to see where this project goes!
But if you make something transparent to the user, it could also mean that you are directly exposing that thing to the user. (Right? I could be wrong about this.)
Just confused me and thought it might be useful to you to know that. Not trying to be an asshole.
> Confusingly, the term refers to overall invisibility of the component, it does not refer to visibility of component's internals
Indeed, perhaps "invisible" would have been a better word choice.
1. Read-only data structures, which are used by everyone in every language all the time.
2. Persistent data structures, which make the underlying immutable structures kind of mutable by creating new versions and reusing parts from the old versions of the structure.
Or are you talking about something more specific, like mutable data structures that provide immutable snapshots of the backing data?
1-https://en.wikipedia.org/wiki/Persistence_(computer_science)
In computer science, persistence refers to the characteristic of state of a system that outlives (persists more than) the process that created it.
2- https://en.wikipedia.org/wiki/Persistent_data_structure
In computing, a persistent data structure or not ephemeral data structure is a data structure that always preserves the previous version of itself when it is modified.