Table of Contents
(Correction on 2020/01/31): Spelling mistakes. Thank you to
jackdk for copying block quotes with high
fidelity in his comments. This notified me of further checks I should make to
I’ve used Python in production for about three years now. Even as it’s a wonderful Swiss Army knife, Python also feels limiting in some ways. The same classes of bugs (e.g. type casting / translation errors, state management errors) kept cropping up, and Sisyphean bugs frustrate me. Python is also quite slow when you compare the operations you want to execute vs. the theoretical maximum performance of those operations on the underlying hardware. I kept wondering about what was possible if I had used a different tool.
What would happen if a data pipeline was lazy instead of eager, not because I’m using a lazy library API, but because the language is non-strict by default? What if you could use types in order to register properties of data instead of state?
Lastly, I wanted to break free of the practitioner’s pragmatic culture and see what academia had to offer in the start of the art. I remember one conversation I had years ago with a professor in college who had previously worked in industry, and he said that A* was invented twenty years before it was widely used in industry (e.g. video game development). I think having an understanding of what’s possible and what the future might bring could prove useful in understanding how to get there as a practitioner.
I thought learning Haskell provided the highest likelihood of satisfying these requirements. So over the past three months, I’ve been reading through “Haskell Programming, From First Principles” by Chris Allen and Julie Moronuki, the 4th release candidate of the 1.0 edition (1.0-rc4). I’m pleased to say that I made it to the end of this 1,857 page (by the e-reader PDF version) monstrosity. Here’s some of the things that I, as a software engineer who has used Python in production and Haskell doing book exercises only, liked and didn’t like about Haskell.
What I like about Haskell
The type system
Haskell’s type system makes Python’s type system look downright primitive. The
closest analogy I can think of is if you had direct access to dunder methods,
__eq__ vs. Haskell’s
Data.Eq typeclass constraint, when
defining classes or methods. Then you had the ability to create
signatures on different, overlapping sets of these dunder methods somehow, and
baked it all into your native toolchain as a set of compile-time guarantees.
This still doesn’t encapsulate the full power of the Haskell type system, like
higher-kinded types and actual sum/product types. It’s insanely mind-blowing.
Constrained polymorphism is probably worth learning Haskell for alone.
I was reading through some of Hillel Wayne’s blog posts, and one of them discussed the Curry-Howard correspondence, which somehow proves a 1:1 association between aspects of a mathematical proof and aspects of a type system. You can convert a proof into a type, if your type system supports it. I think Haskell’s type system respects this. I don’t think Python’s type system does.
An emphasis on structure
One key aspect of Haskell the authors came back to repeatedly (and I mean repeatedly) is Haskell’s emphasis on structure. If I had to explain functors, applicatives, and monads to somebody else, it wouldn’t be in terms of I/O, or error handling, or burritos. It’d be structure.
Structure, in this case, refers to a higher-kinded or partially-applied type,
like a list. You apply another type to a higher-kinded type to create a concrete
 to create
[Integer] or a list of integers.) A
functor instance lifts a function over some structure to affect only the
values within. An applicative (a monoidal functor) leverages a structure and
function together while lifting the function over structure. A monad (an
applicative functor) creates new structure whilst in the process of lifting a
function over existing structure.
Let’s take this example that might explain functors and structure to a tiny degree. Say you want to add one to a list of integers in Python. You can do this:
xs = [1, 2, 3, 4, 5] # xs = [2, 3, 4, 5, 6] xs_ = map(lambda x : x + 1, xs)
In Haskell, this may look like:
xs = [1, 2, 3, 4, 5] -- xs' = [2, 3, 4, 5, 6] xs' = map (+1) xs
Sure. But Haskell also has
fmap, which stands for “functor map”. On lists
this is straightforward:
-- xs'' = [2, 3, 4, 5, 6] xs'' = fmap (+1) xs
But you can also define it on other types, such as concrete
-- ys = (Just 2) ys = fmap (+1) (Just 1)
In both cases, you’re lifting the method
respectively to affect the value(s) within.
You can also more explicitly lift
fmap inside nested structure, such as a list
Maybe values using function composition:
-- zs = [Just 2, Just 3, Just 4] zs = (fmap . fmap) (+1) [Just 1, Just 2, Just 3] -- zs' = [Just 2, Nothing, Just 4] zs' = (fmap . fmap) (+1) [Just 1, Nothing, Just 3]
I don’t think you can do anything like this with Python or any other practitioner’s language that I’m familiar with. This barely begins to touch the power of Haskell structure. I definitely recommend thinking in terms of structure when learning Haskell.
As an aside, I published a monad tutorial last year before reading through the monads section, and uh, yeah. Not a good idea to speak authoritatively on a subject you know nothing about (if only because it may lead others astray). You can read the feedback I got on that from lobste.rs here.
Streams and stream-oriented programming foundations
Haskell has native streams in the form of unbounded structures (such as lists),
where you can apply
take to take a number of elements for evaluation:
-- [1..] is a never-ending stream of increasingly large integers. -- -- `$` is an associativity forcing function, to evaluate everything to its right -- before evaluating anything to its left. -- -- `take 5` takes the first 5 values. -- -- The compiler takes the full expression, and returns: -- xs = [1, 2, 3, 4, 5] xs = take 5 $ [1..]
No need for separate generator syntax. This is due to Haskell’s non-strict
evaluation, compiler optimizations, and the clear delineation between structure
and values. This could be useful for languages implementing streaming
frameworks, such as
streaming. Streaming is
important for, among other things, implementing real-time analytics, which is
slowly becoming a basic requirement for various BI platforms.
Even the base error type,
_|_, is defined as an evaluation that
never completes successfully, either because a computation failed, or because
there’s an infinite loop. See the Haskell wiki definition of
bottom. I don’t think Python’s
has a built-in notion of infinite loops evaluating to an error condition. This
appears super powerful to me, because the big error type introduced by going
over network connections are network timeouts, which appear to most programs as
infinitely long computations.
property-based testing framework that generates values to check a declared
property. Python has
hypothesis, but having
used it a tiny bit for an open-source contribution, I don’t think it’s the same
without the Haskell type system. The type system empowers effective
property-based testing to a high degree. For example, in
can literally create a wholly abstract type, and then cast it to different
concrete types for different QuickCheck runs. So you can check that your
declared monoid is properly associative for a set of different concrete types,
which QuickCheck will validate by running at least 100 different concrete
values per concrete type signature, while only needing to mutate the type
signature. In production, libraries like
abstract away testing properties of common typeclass instances like functors,
applicatives, and monads to give you soft assurances they work as intended. From
my current understanding, I would categorize property-based testing as something
between Monte Carlo simulation and constraint solving.
I can’t overstate how powerful
Test.QuickCheck is. I can run hundreds of
unit tests per dozen lines of code, without having to set and mutate my own
oracle values. By contrast, a couple thousand unit tests in a project with 10^5
LOC in Python would be considered well-tested. The vast majority of testing I
did as part of the book exercises would be through
Hspec, the Haskell unit
testing framework, hardly gets mentioned.
An interpreter for a compiled language? Yup.
stack ghci was a good friend
during this time period. You can pretty much do anything that compiled Haskell
can do. Load GHC language extensions? Check. Set runtime conditions like
:set -Wall to see all raised warnings? Check. Print type signatures, kind-ness
signatures, and other information around methods? Check.
There’s some weird edges around GHCi, mostly around the difference in runtime
behavior between GHC and GHCi. Ordering matters in GHCi and not in an equivalent
source file compiled using GHC. GHCi natively prints results to terminal and
Show is necessary for type definitions referenced in GHCi.
GHCi defaults types to
() if they’re not specified vs. GHC’s compile-time
error, which for me led to some interesting
QuickCheck behavior. However, none
of this really impacted my workflows very much, or took away from how impressive
Hoogle is Haskell’s API search engine where, among other things, you can search a method for by passing in the type signature of a method you think you want. There’s Hackage, whose search maps more closely to that of PyPI, but I don’t think Python has anything like Hoogle. I don’t think Hoogle exists just as a flex on lesser-typed languages. I’ve had to search for some things there and I’d say it’s been useful even to a beginner like me. I can see how using such a tool would become easier as you become more and more familiar with the Haskell standard library and the major package ecosystem.
What I dislike about Haskell
My appreciation of programming as an art and as a discipline increased by my learning Haskell, at the cost of my brain constantly melting out my ears and my eyeballs not comprehending what I was reading. The cognitive load was insane. Three months in and I still have little idea on how to write production-grade Haskell code.
This may just be that this specific book is a high-level overview of Haskell and not a practitioner’s guide. Chris, one of the authors, mentioned some differences in direction with Julie with respect to the book’s purpose. Julie’s new book, “The Joy of Haskell”, sells itself as “extremely approachable” and “a practical problem-motivated approach”, which may be more catered towards her interests. In my experience, the longer a book is, the more times I need to review it in order to absorb all the content.
Then again, I remember how my old Python code was absolute hot garbage, and I only really got good at Python about a year into my first job and asked questions every 20 minutes for a month and read 10-15 books on the subject. If I started using Haskell at a Haskell-friendly workplace, I’m sure it’d be much the same story: first overwhelmed, then awed, then routine.
IMHO, the solution to high cognitive load, given a high enough reward, is just persistent, methodical execution towards learning. If I had to choose one feeling, I generally prefer being overwhelmed to being helpless. I find it easier to sprout enough dendrites to understand this stuff one day and add it to my toolbox rather than stomach the regret of the path not taken.
Haskell, like Python, practices
indentation affects program structure and execution. I found myself unused to
Haskell notation where the program seems to backtrack in on itself, such as
where notation. Here’s an example with all three in
Haskell, taken from the book:
gameWords :: IO WordList gameWords = do (WordList aw) <- allWords return $ WordList (filter gameLength aw) where gameLength w = let l = length (w :: String) in l >= minWordLength && l < maxWordLength
IIRC the last few lines wouldn’t compile until I indented at least that much
(like, past the start of variable
gameLength, instead of just 2 spaces in like
the rest of the program). If I indented more, it would work the same. In Python,
I would get a clean
IndentationError for everything except the proper
indentation, and if I indented more and remained valid, it would imply different
code behavior (like a scoping change).
Concurrency / Parallelism
Since Haskell is a lambda calculus, and everything is functional, I thought I could get parallelism trivially. Trivially, as in I expected GHCi could be run with multiple processes / threads using a config flag or file, and you could configure GHC to create a compiled binary targeting a (max?) specific number of processes / threads.
Nope. As far as I can tell, you still have to mutate your source code, and use a
library, such as
(external dep) or
(base stdlib). Simon Marlow’s “Parallel and Concurrent Programming in
is probably a must-read to understand this topic (and things like
I get concerns about resource contention, and the fact that side effects may
exist and compile may imply non-trivial parallelization, but it does reduce the
value proposition of Haskell when comparing to something that’s more
“traditional”, where you have to change the source anyways to get those
benefits. When I apply property-based testing to check monadic associativity and
applicative composition on naive custom-defined
List types using
the fact that it stalls one CPU core while not using the other 11 CPU cores (I
have an Intel Xeon) kind of kills me inside.
I could be wrong. There might be an option in GHC or GHCi I don’t know about that enables this to occur. If that’s not the case though, I think if Haskell made parallelization more upfront and trivial, it would be a material selling point for practitioners.
Strictness / Non-strictness
Haskell is fundamentally a non-strict language. However, you still need
strictness in order to do useful stuff. So the way Haskell ended up appearing
to me in this regards was a mishmash of strict and non-strict portions of the
codebase. You have to take a look at GHC Core, the underlying strict
(transpilation?) of GHC Haskell to understand how the code actually behaves, and
GHC Core is not the most readable language. For example, for
Foldable types, a
foldl is strict over spine evaluation, while
foldr is non-strict over spine evaluation.
There’s some conventions people follow. For example, when implementing
Foldable types, making the spine lazy and values strict is a point constantly
hammered home. So does properly raising asynchronous exceptions. However, to me
this falls far short of the guarantees I thought I could get, and I honestly
prefer having an explicit distinction between lazy/eager APIs after seeing the
stack uninstall $PACKAGE does not exist, and I wish the reasoning was more
clear (like code samples to indicate why it is unnecessary). Here’s the
GitHub issue related to implementing
stack uninstall, which was,
as far as I can tell, closed without action. As a newbie, I do wish that there
was guidance on the “proper” way to uninstall packages if
stack uninstall is
I also wish that there was a better way to version and lock environments. I would have loved to have package lockfiles available for source code work as part of the book, as parts of GHC have changed since the book was written.
On the issue of compatibility.
Haskell has the ability to declare a type alias around an existing type, called
newtype, that guarantees only the type name and not the underlying data
representation changes. Ostensibly, this serves as a way to typecheck data and
act as an extra constraint. What
newtype really reminds me of though is a
comment on Hacker News talking about how Lisp’s type system enabled the creation
of Lisp macros and tightly coupled a developer’s team to its codebase. If you
could define a DSL on top of Haskell and shoot yourself in the foot,
would likely the culprit. I’m not sure how library compatibility would work.
newtype exist as part of native top-level APIs for Haskell libraries? How
much churn do production Haskell type signatures suffer from? This is an unknown
quantity to me.
In addition to concerns about “horizontal” compatibility between third-party
libraries in the same environment, I also worry about “vertical” (backwards /
forwards) compatibility when upgrading or downgrading environments. Haskell
respects the mathematics behind type theory, and when there are new discoveries,
Haskell doesn’t mind making breaking changes. For example, this Stack Overflow
answer helped me understand why I
needed to implement
Semigroup every time I needed to implement
wasn’t the case in the book examples. When
were added to GHC 7.10. I believe I had to implement
Applicative when I needed
Monad for various type definitions. Yeah. Math changes.
I’m interested in seeing any published upgrade paths, or how existing Haskell projects pull core dependencies from upstream. Maybe it’s just a different workflow than what I’m used to, but in any case it would be helpful to understand for any production Haskell projects.
Haskell has no null definition of type
Char, at least according to this Stack
Overflow answer. Python doesn’t
String types with single and double quotes, so
empty char is empty string is an empty list. It seems weird to me that for all
the typing wealth Haskell provides, this base case doesn’t exist, though I
personally don’t know what it is.
Integer are two different types, according to this Stack Overflow
post. Apparently it’s easier to
just change the type signature rather than execute a type conversion. I’m sure
they have their reasons, but I personally don’t understand this.
I don’t like how Haskell applies wildcard imports by default. Here’s Haskell:
-- Wildcard import import Package -- Namespaced import import qualified Package as Package
# Namespaced import import package # Wildcard import from package import *
Haskell can raise errors partway through evaluating an error message. This (feature?) seems alien to me:
Prelude> map (+1) [1, 2, undefined] -- '[2, 3,' is still visible, and has been executed. [2,3,*** Exception: Prelude.undefined CallStack (from HasCallStack): error, called at libraries/base/GHC/Err.hs:79:14 in base:GHC.Err undefined, called at <interactive>:27:17 in interactive:Ghci14 Prelude>
One day, I was looking up how to use
FlexibleInstances, which is a GHC
language extension. The book referenced this URL:
2020-01-14T16:38:59.758039-05:00, this link returned
nginx: 404 Not Found. When I searched for
FlexibleInstances using DuckDuckGo, I got this
completely new link:
From Christopher Allen’s blog post finalizing “The Haskell Book”, it looks like the book was last updated around end of 2018. So in less than two years, the link had rotted.
I think Python’s spoiled me. I can go to python.org’s website, and pull up a set of offline docs for every GA version of Python going back to Python 1.4, published October 25th, 1996. Don’t take my word for it. Here’s the Python 1.4 documentation. I have no idea who even uses Python 1.4. But I’m pretty sure that for whoever needs Python 1.4 (maybe some poor legacy embedded systems engineer), this specific link will not rot.
To be clear, I didn’t choose to learn Haskell because it’s friendly to developers. One of my reasons for learning Haskell, is to see what a language is like when it doesn’t compromise. However, things like link rot, or a general affinity towards terseness, or a lack of unstructured documentation (IMHO type signatures aren’t a substitute for documentation) or migrating the bug tracker to a GitLab deploy for reasons (where you now have to create an account and sign in to read issues) stand out in my mind as impediments that might make learning Haskell and growing the Haskell community unnecessarily difficult.
Perhaps I’m being dramatic, but I oftentimes forget that I’m extraordinarily privileged to live in a first-world country, with the financial capacity to attend conferences at will, gain access to core devs and library maintainers over email, and spend time to learn things for fun. Not everybody has that ability, and this stark contrast between community management styles brings this privilege to my mind’s forefront. I think the Haskell community could take many cues on this subject from the Python community, and I think that would lend a massive step towards in sharing Haskell’s beauty with more people around the world.
Projects I’d like to do in Haskell
I’m probably tabling my journey towards learning additional Haskell until I ship some other stuff, but I already have some ideas for projects I would like to implement in Haskell:
A tool to model ETL workflows for integration testing: I’ve found ETL workflows to be tremendously difficult to model, because verifying correctness almost entirely revolves around parsing side effects. Haskell is great at abstracting away side effects, and it’s great at generative testing. Both of these attributes could apply to a new integration testing framework for something like Apache Airflow.
An RFC-4180 compliant CSV parser: There’s plenty of CSV parsers out there. There aren’t too many that openly state to be RFC-4180 compliant, given the need to satisfy business requirements. Of those that are, I haven’t seen any that publish test reports that can automatically verify RFC-4180 compliance. Of course, the goal of a CSV parser is to get the CSV file into a highly structured format as quickly as possible in order to avoid parsing errors further down the pipeline, but even getting that first step done is a materially non-trivial task. I’d love to have a CSV parser that could be verified using some kind of IETF grammar generation tool, or modeled using formal reasoning.
cassavawould be great prior art for me to study.
A POSIX-compatible, lazy data engineering pipelining tool: The big data engineering tool I’ve used in the past is Apache Spark, but there are parts of Spark I wish were different. I believe the Spark RDD binary format underneath is framework-specific. Using it involves a REPL, instead of
stdout. Finally, while the pipeline is lazy, the pipeline constructor is monolithic.
I’d love to have a POSIX-compatible, composable data engineering framework with something like standard Parquet files (highly structured, a small, independent type system striped alongside data, columnar formatted, binary) acting as a persistent backing. I think POSIX and Haskell paired together would be such a great combination, and it’s where Haskell’s strengths like bare-metal performance and non-strictness can really shine. Imagine something like this:
pipelineBuilder source.parquet --conf baseConf.json \ | transformA --conf conf1.json \ | transformB -conf conf2.json \ > out.parquet
One interesting section of Haskell code the author pointed out was this method,
as part of
pinned at commit
addSubWidget :: (YesodSubRoute sub master) => sub -> GWidget sub master a -> GWidget sub' master a addSubWidget sub w = do master <- liftHandler getYesod let sr = fromSubRoute sub master i <- GWidget $ lift $ lift $ lift $ lift $ lift $ lift $ lift get w' <- liftHandler $ toMasterHandlerMaybe sr (const sub) Nothing $ flip runStateT i $ runWriterT $ runWriterT $ runWriterT $ runWriterT $ runWriterT $ runWriterT $ runWriterT $ unGWidget w let ((((((((a, body), title), scripts), stylesheets), style), jscript), h), i') = w' GWidget $ do tell body lift $ tell title lift $ lift $ tell scripts lift $ lift $ lift $ tell stylesheets lift $ lift $ lift $ lift $ tell style lift $ lift $ lift $ lift $ lift $ tell jscript lift $ lift $ lift $ lift $ lift $ lift $ tell h lift $ lift $ lift $ lift $ lift $ lift $ lift $ put i' return a
The author points out that this is an abuse of monad transformers, and I’m sure
you can tell this isn’t the cleanest Haskell code out there. My goal here isn’t
to diss the maintainers or authors of this code. I’m pretty sure
@snoyberg is one of the most pre-eminent
Haskell programmers in the world and he probably had good reason to write this
method the way it is. What I do want to point out is Haskell isn’t a magical
elixir. It’s still a programming language. For me, I put Haskell and those who
used it on a pedestal and I thought that if I just learned Haskell, all my
technical problems would go away. Learning Haskell and understanding it
intimately helped me take it down from its pedestal, and I realized that the
fundamental truths of software engineering still hold. How you use a tool is
just as important as which tool you use. There are always tradeoffs you weigh
when making decisions, especially in the face of development limitations like
manpower and money. Make things work, then right, then fast, in order to ship.
Getting to a bare beginner’s level in Haskell was an informative experience for me, and I hope I can continue learning and applying Haskell in my life. I think I grew immensely, both personally and professionally, from this experience, and I’m tremendously grateful to the Haskell community for the journey thus far. To more adventures! 🍷
(If you’re interested, here are my notes for “The Haskell Book”.)