Haskell's Challenges in Startups

Functional programming, particularly in Haskell, has profoundly shaped my understanding of software development—more so than all the object-oriented languages I’ve worked with combined. It prioritizes developer efficiency, enabling you to accomplish a great deal with confidence that your code will run reliably. It minimizes the need for defensive coding or extensive error-checking, allowing developers to focus on expressing their intent clearly.

Using Haskell in Big Company

In 2017, I had the opportunity to lead a Haskell team at Conde Nast Internaional (VOGUE, GQ, WIRED, AD…). From that experience, I observed that for developers proficient in languages like Java, Python, or C++, it typically takes about 2-3 weeks of full-time onboarding—divided between coaching and self-study—to become productive on a standard industrial Haskell project. After approximately three months, they’ve usually explored a wide array of libraries and feel equipped to tackle nearly any challenge.

For us, one of the most significant advantages of Haskell is how it reduces cognitive load. In object-oriented programming, developers constantly juggle low-level details like memory management, object lifetimes, and performance optimizations. This mental overhead can slow development and introduce subtle, hard-to-find bugs. Haskell alleviates these concerns through its high-level abstractions and declarative style.

Its strong static typing, combined with a system that makes side effects explicit, helps prevent entire classes of bugs at compile time. Haskell’s lazy evaluation strategy and sophisticated type system enable developers to compose simple functions into complex behaviors efficiently.

Example 1: Filtering List Elements

data Person = Person { name :: String, age :: Int }

people = [ Person "Alice" 25, Person "Bob" 32, Person "Charlie" 19 ]

namesOver21 :: [Person] -> [String]
namesOver21 = map name . filter ((>21) . age)

main = print $ "Names over 21: " ++ show (namesOver21 people)

This code defines a Person data type, filters the list to include only those over 21, and maps their names into a new list. It showcases how Haskell’s higher-order functions allow for complex operations to be expressed in a concise and declarative manner.

Example 2: Leveraging Lazy Evaluation

-- Infinite sequence of natural numbers
naturals :: [Integer]
naturals = iterate (+1) 0

-- Take elements from the infinite sequence until a number greater than 10 is found
takeWhileLessThan10 :: [Integer] -> [Integer]
takeWhileLessThan10 = takeWhile (<= 10)

main = print $ takeWhileLessThan10 naturals

Haskell’s lazy evaluation allows the program to generate and process an infinite sequence, halting when a condition is met. This approach avoids unnecessary computation and exemplifies how Haskell can efficiently manage potentially vast data sets.

Example 3: Using Higher-Order Functions

-- Define a list of numbers
numbers = [1..10]

-- Use map to double each number in the list
doubleNumbers :: [Integer] -> [Integer]
doubleNumbers = map (*2)

-- Use filter to keep only even numbers
evenNumbers :: [Integer] -> [Integer]
evenNumbers = filter even

main = do
    let doubled = doubleNumbers numbers
    let evens = evenNumbers doubled
    print $ "Doubled and filtered numbers: " ++ show evens

This example illustrates how Haskell’s map and filter functions can be combined to transform and filter data declaratively, without the need for complex control structures.

Example 4: Harnessing Algebraic Data Types

data Tree a = Leaf a | Node (Tree a) (Tree a) deriving Show

-- Function to count the number of nodes in a tree
countNodes :: Tree a -> Int
countNodes (Leaf _) = 1
countNodes (Node left right) = 1 + countNodes(left) + countNodes(right)

main = do
    let tree = Node (Leaf "hello") (Node (Leaf "world") (Leaf "!"))
    print $ "Number of nodes: " ++ show (countNodes tree)

Here, algebraic data types are used to define a binary tree, and pattern matching provides a concise and readable way to traverse and process the structure.

Using Haskell in Tech Startups

Haskell offers several advantages, including reduced cognitive load, strong static typing, and lazy evaluation strategy. However, it may not be the best fit for tech startups that prioritize “move fast” culture due to its need to write a lot of code from scratch compared to using established libraries and frameworks.

One of the main reasons for this is the lack of mature libraries and frameworks for key enterprise capabilities. The library ecosystem is probably the biggest issue, making it difficult to overcome without a lot of effort.

The reality is that Haskell’s ideas and principles have undoubtedly influenced many other languages, but its adoption in tech startups might be limited. Some challenges faced by developers include:

The compiler being slower than most mainstream language compilers Its ability to effectively report errors is poorer It tends to have ‘first error, breaks rest of compile’ problems The tooling is still poor compared to other functional languages and really poor compared to mainstream languages like C# This significantly steepens the learning curve for juniors and those new to Haskell, getting in the way for those more experienced. The library ecosystem for key capabilities in ‘enterprise dev’ is poor. Many unmaintained, substandard, or incomplete implementations. After a year of running a team with Haskell, everything was much slower, which sapped momentum from the team. I think Haskell’s biggest contribution to the wider community is its ideas, which have influenced many other languages.

But, I’m not sure it will ever have its moment in the future unfortunately.