There is a shortage of good books on intermediate Haskell and above, and I think it somewhat contributes to the ivory tower feel of the language. Back when I was getting into C++ a couple of decades ago, there were some pretty well-acclaimed books and even whole series like “Effective C++” by Meyers, “Exceptional C++” by Sutter, and so on. They don’t necessarily teach writing production-level code (and I doubt that kind of coaching is at all possible), but these books are good at building the language philosophy, spirit, and style.
I can’t quickly think of as many Haskell-oriented (or even general FP-oriented) resources, so when I heard about the book “Thinking with Types: Type-Level Programming in Haskell” by Sandy Maguire a few months ago, it really caught my attention — I even got to read it the same year!
Although, reading this book gave me mixed feelings. On the one hand, I learned a few interesting tricks and corner cases, so the hours I invested in this book were well-spent. On the other hand, I think this book has some shortcomings, and, while being interesting, it still fails to address the audience it aims at (and, given the first paragraph, I’d like it to aim at).
To summarize my feedback, I think the best title would be “A Few Case Studies in the Intricacies of the GHC’s Type System.” Hence the audience benefitting the most are the folks already having a decent grasp of type-level computations in other, more expressive languages (like Idris) or who have read a book or two on type theory. Why would they want a book like this, then? Well, eventually, one’d need to solve real-world problems with a production-ready compiler and a mature ecosystem that GHC provides instead of recursing infinitely into yak-shaving (like in Idris). So, this book is quite good at showing by example how these advanced abstract type-level things can be somewhat leveraged in a production-level language.
This book would have been a reasonable 9/10 with a title like “A Few Case Studies…”. But with the title it actually has and the target audience it claims, it’s more like 6 or 7 out of 10.
For a more detailed review, follow the spoiler!Read more...
It’s been a while since I last used C++ for anything serious, but once a C++ guy, you’re always a C++ guy, right? So, I decided to see how modern C++ fares in a seemingly simple task: eliminating duplicate list elements.
That sounds trivial, so why bother with a whole blog post? Well, the catch is we’re gonna do this at compile-time. Moreover, lists will be represented as tuples, and the elements might have different types.
Hopefully, during this little exercise, we’ll also learn (or at least reinforce) a pattern or two of modern metaprogramming.Read more...
Like the last time, this post describes both the final result and the road there. So, there will be lots of code and lots of diffs, beware!Read more...
I’ve recently come across the new “Quite OK Image” format — a fast lossless image compression algorithm. It’s a very straightforward algorithm that’s a pleasure to work with, so, naturally, I got curious what would be the performance of a Haskell implementation if:
- I just write reasonably efficient code without getting too deep into low-level details to get the job done in a couple of hours.
- I try to push the envelope and see what could be done if one’s actually willing to go into those details (within some limits, of course, so no GHC hacking!)
Turns out that yes, it’s indeed possible to write something with C-level performance in a matter of a couple of hours. Moreover, Haskell’s type system shines here: class-constrained parametric polymorphism enables using the same decoder implementation for pixels with very different representations, allowing to squeeze as much performance as is reasonably possible without duplicating the code.
In this post, I’ll describe the Haskell implementation of the decoder, and the steps I took to get from (1) to (2) for the decoder.Read more...
In this post, I’ll describe my setup for doing Haskell (which I almost exclusively do with
Spoiler: it’s much, much more straightforward than a few years ago, almost to the point of “vim and Haskell” posts being no longer necessary.Read more...
If we want to use dependently typed languages as proof checkers, we better be sure they are consistent as a logic, so that we don’t accidentally prove ⊥ and, as a consequence, any proposition.
One huge source of inconsistency is non-terminating computations; hence languages like Idris or Agda go to great lengths to ensure that functions indeed do terminate. But, for deep reasons, a purely automated check having neither false positives nor false negatives just does not exist, so compromises must be made. Naturally, when talking about proofs, it’s better to be safe than sorry, so these languages strive to never label a function that doesn’t really terminate for all inputs as terminating. Consequently, this means that there are terminating functions that the termination checker does not accept. Luckily, these functions can be rewritten to make the checker happy if all the recursive calls are "smaller" in some sense.
This post emerged from me trying to persuade Agda that a bunch of mutually recursive functions are all terminating. I went through the Agda’s standard library to figure out how to do this, taking notes about what different abstractions I encountered mean and expand to. Then I figured that, if I pour some more words into my notes, it might turn out to be useful for somebody else, so, well, here it is.Read more...
Haskell is a very special language, and one of the peculiarities setting it aside is its evaluation model. In fact, the thing I, for one, find most complicated about Haskell is not monads nor all the countless type system extensions, but rather reasoning about space and time complexity of whatever I write. Thus I better have a good mental model about how Haskell code gets to run, and one of the most fruitful mental models for me is treating a Haskell program as a set of equations that some graph reduction engine churns until… well, the termination criteria are not the point of this post. The point of this post is that it’s ultimately a graph without any good intrinsic notion of a call stack.
On the other hand,
there is a
(by the way, described as
Access to GHC’s call-stack simulation, italics ours)
as well as some mechanism for capturing something called
How do those call stacks connect with the graph reduction model?
Let’s maybe carry out a few computational experiments all while keeping track of the obstacles we hit, shall we?
we’ve looked at implementing a toy
and we’ve also compared its performance against the full-blown Unix
The results were quite interesting:
our implementation managed to beat
wc by a factor of 5.
Of course, that’s quite an unfair comparison:
our implementation is hardcoded to count just the bytes, lines and words.
wc, on the other hand, has command-line options to select specific statistics,
it supports some additional ones like maximum line length,
it treats Unicode spaces properly (in an Unicode-aware locale, of course),
and so on.
In other words, it’s better to consider what we’ve done last time
as a proof-of-concept showing that it’s possible to achieve (and overcome)
C-like performance on this task, even if with all those concessions.
Today we’ll look at ways of productionizing the toy program from the previous post. Our primary goal here is allowing the user to select various statistics, computing just what the user has selected to compute. We’ll try to do this in a modular and composable way, striving to isolate each statistic into its own unit of some sorts.
Indeed, if we look at the C version — well, personally I wouldn’t call that as a prime example of readable and maintainable code, as different statistics are computed in a single big 370-lines-long function. This is something we’ll try to avoid here.
Moreover, we’ll try to express that certain statistics like byte count or lines count
can be computed more efficiently if we don’t have to look at each byte,
while other statistics like word count or max line length just need to look at each byte one by one
(unless one does some clever and non-trivial broadword programming or SIMD-enabled things,
which is beyond the scope of this post).
For instance, byte count can be computed in
O(1) if we know we’re reading from a file —
we can just take the file size and call it a day!
In addition to that, we will, among other things:
- implement more statistics with ease, enjoying local reasoning;
- throw up some tests, enjoying local reasoning once more;
- try out some kinda-dependently-typed techniques, successfully obtaining working code but failing spectacularly on the performance side of things;
- play around with Template Haskell;
- marvel at the (un)predictability and (un)reproducibility of the resulting code performance.
tl;dr: today we’ll look at implementing a toy
that is about 4-5 times faster than the corresponding GNU Coreutils implementation.
So I’ve recently come across a post by Chris Penner
describing a Haskell implementation of the Unix
Chris did a great job optimizing the Haskell version as well as
showing how some high-level primitives (monoids and streaming, for one) turn out to be useful here,
although the result was still a bit slower than C.
There’s also a parallel version that relies on the monoidal structure of the problem a lot, and that one actually beats C.
But that post left me wondering: is it possible to do better without resorting to parallel processing?
Turns out the answer is yes. With some quite minor tweaks, the Haskell version manages to beat the hell out of the C version that presumably has decades of man-hours put into it.Read more...
In this post we will implement the Levenshtein edit distance algorithm, aiming at a reasonably performant code. We will start with a more or less idiomatic pure implementation and see how various changes (including strictness annotations or compilation options) affect the performance.
We will also compare this to a baseline C++ implementation.
- C++ implementation turns out to be slower than the fastest Haskell implementation.
- LLVM backend really shines here.
In this post we’ll see how some algebraic considerations help us to discover one more pattern
MonadError (and a
Generic implementation thereof),
and we’ll also update our
Has class with one more method that brings it closer to
something lens-like and makes it useful with writable environments like
Last time we briefly covered the
which allows expressing more precisely in types what does a function need from the application environment.
We also wrote a few instances for our
Has-like classes for a toy environment type.
The problem is that we had to write those classes. As the number of fields in our environment type grows, the number of classes we have to write grows linearly even in the best case. This much manual labour is surely not the Haskell way!Read more...
A few weeks ago I’ve been trying to remove the boilerplate of writing instances of a certain type class,
and I learned a couple of cool tricks that are probably worth sharing.
The class in question is a generalization of the type classes comprising what is known as the
So, before describing those tricks in detail, let’s briefly discuss what’s the
Note: this is an introductory post.
Has pattern is definitely not something I’ve created or even coined a term for,
and seasoned haskellers are surely familiar with this approach.
Yet I feel obliged to give a brief overview before delving into the more interesting stuff.
How do Haskell folks solve the problem of managing some environment
(think some global application configuration)
that’s needed by a bunch of different functions?
One obvious way is to just pass the
Env to the functions that need it.
Unfortunately, this does not compose as nicely as some other primitives we’re used to in Haskell.
Indeed, this way every function that needs even a tiny-tiny piece of the environemnt gets the whole of it.
Thus, we lose the ability to reason about what parts of the environment does a given function need
merely from looking at its type.
That’s surely not the Haskell way!
Let’s explore some other approaches, shall we?Read more...
This is gonna be a short one.
Some time ago I’ve written a tiny helper
Curry for, well,
currying functions and function-like objects:
given some callable
foo accepting arguments of types
T_1, ..., T_n,
Curry(foo) returns an object such that
t_i is of type
T_i) would, as you might expect, call
passing all those
t_is to it.
This was so long ago that C++11 compatibility was a thing for me back then,
Curry is written with that version of standard in mind.
And then a couple of days ago I stumbled upon that code again,
and couldn’t help but realize how terribly verbose it is.
Let’s see how modern C++ allows reducing the verbosity.
One of my hobby projects includes a long-running service, so it’d be nice if the service provided some
metrics (say, using the
ekg library) to the outside world for monitoring and alerts.
As a consequence, the service needs an internal metrics storage that encapsulates all things
related to creating them as needed, updating them, and so on.
Writing a metrics storage (especially on top of
ekg) is trivial,
but one cannot just solve a problem when doing recreational programming.
You’ve got to abstract things away, generalize, and then abstract further and generalize further.
So, quite soon I found myself writing an extensible and customizable storage
supporting unknown metrics of unknown types in such a way
that new metrics could be added in different modules without touching any existing definitions.
This deserves a post or two on its own, but today we’ll consider just a tiny part of the solution:
writing a type-safe wrapper over types that are only known at runtime.
So, yeah, something like dynamic typing but with static guarantees that we don’t do any nonsense.
I don’t think this short post will reveal anything new for seasoned haskellers, but at least we’ll get this little part done and out of our way in our next articles about the storage itself. Or I could be less shy and claim instead that I created a new programming pattern.
Anyway, first things first. Let’s spell out what problem we are trying to solve. So, we want to be able to associate some objects (whose types aren’t known before the runtime) with some values of some other type (which we don’t use so we don’t care about). In other words, we want objects of more or less arbitrary (and different) types to be the keys in the same associative container.Read more...