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Troubleshooting Haskell Space Leaks: Using -prof and Strictness Flags to Fix Thunk Buildup

 In Haskell, the elegance of lazy evaluation comes with a specific operational cost: the space leak. You write a mathematically pure function to fold over a large dataset, it compiles perfectly, but at runtime, it either consumes all available RAM or crashes with Stack space overflow.

This failure usually stems from thunk buildup—the accumulation of unevaluated expressions in memory. This post analyzes the mechanics of this failure, demonstrates how to profile it using GHC's tooling, and provides a robust solution using strictness annotations.

The Root Cause: Lazy Evaluation and Thunks

By default, Haskell is lazy. When you bind a variable or pass an argument, the runtime does not evaluate the expression immediately. Instead, it creates a thunk—a pointer to the code required to calculate the value and its execution environment.

In a recursive operation like foldl, this behavior is dangerous.

Consider a naive average calculation:

-- BAD: This builds a massive thunk chain
mean :: [Double] -> Double
mean xs = s / fromIntegral n
  where
    (s, n) = foldl (\(sum, count) x -> (sum + x, count + 1)) (0, 0) xs

When foldl traverses a list of 10 million items, it does not update the accumulator (0, 0) to (10.5, 1). Instead, it constructs a massive expression tree representing the promise of the result:

((...((0 + x1) + x2)...), (...((0 + 1) + 1)...))

This structure resides in the heap. When the program finally requires the result (e.g., for printing), the runtime attempts to force this massive chain. Since the evaluation of the outer addition requires the evaluation of the inner addition, the runtime pushes stack frames for every element in the list.

Result: Stack space overflow or massive heap fragmentation.

Step 1: Diagnosing with GHC Profiling

Before optimizing, we must verify the leak. We will use the GHC profiling runtime to visualize heap usage.

Create a file named Leak.hs:

module Main where

-- Large list that will cause issues
main :: IO ()
main = do
    let nums = [1..10000000] :: [Double]
    print $ badMean nums

badMean :: [Double] -> Double
badMean xs = s / fromIntegral n
  where
    -- This foldl is the culprit
    (s, n) = foldl step (0, 0) xs
    step (accSum, accCount) x = (accSum + x, accCount + 1)

Compilation

Compile with profiling enabled (-prof) and automatic cost-center annotation (-fprof-auto):

ghc -O2 -prof -fprof-auto -rtsopts Leak.hs

Execution

Run the executable with RTS (Runtime System) options to generate a heap profile (-h):

./Leak +RTS -p -h

This generates Leak.hp. Convert this to a PostScript or inspect it. A distinct characteristic of a space leak caused by foldl is a "sawtooth" or a linearly increasing memory graph that never plateaus until the crash, often dominated by the type acting as the accumulator (e.g., (,) tuples or Double).

Step 2: The Fix

To fix this, we must enforce Strictness. We need to ensure that the accumulator is evaluated to Weak Head Normal Form (WHNF) at every step of the fold.

1. Replace foldl with foldl'

The Data.List module provides foldl', which uses seq to force the accumulator to WHNF before proceeding to the next element.

2. Enforce Deep Strictness

However, foldl' is often insufficient for complex accumulators (like tuples). foldl' only forces the "spine" of the tuple (the constructor), not the contents inside (sum and count).

We must make the data structure itself strict. We can do this using a custom strict data type or, more conveniently, using Bang Patterns with a strict tuple logic.

Here is the rectified, production-grade code:

{-# LANGUAGE BangPatterns #-}
{-# LANGUAGE Strict #-}

module Main where

import Data.List (foldl')

-- A strict data structure is often cleaner than a tuple for accumulators
data Stat = Stat { stSum :: !Double, stCount :: !Int }

main :: IO ()
main = do
    let nums = [1..10000000] :: [Double]
    print $ efficientMean nums

efficientMean :: [Double] -> Double
efficientMean xs = stSum result / fromIntegral (stCount result)
  where
    -- Initial state
    initial = Stat 0 0
    
    -- The folding function
    -- Note: Because 'Stat' fields are strict (marked with !),
    -- creating a 'Stat' forces the values immediately.
    step :: Stat -> Double -> Stat
    step (Stat !s !c) x = Stat (s + x) (c + 1)

    -- foldl' forces the 'Stat' constructor at every step.
    -- The 'Stat' constructor forces 's' and 'c' due to strict fields.
    result = foldl' step initial xs

The Explanation

Why foldl' + Strict Fields works

  1. foldl' mechanics: foldl' f z [] = z foldl' f z (x:xs) = let z' = f z x in z' seq foldl' f z' xs

    The seq ensures z' is evaluated to WHNF before the recursive call happens. This prevents the chain of function calls from growing on the stack.

  2. WHNF vs. NF: Evaluating a tuple (a, b) to WHNF simply checks "is this a tuple?" It does not evaluate a or b. If we used a standard tuple (Double, Int) with foldl', the runtime would create millions of thunks inside the tuple components: val = (thunk1, thunk2)

  3. Bang Patterns / Strict Data: By defining data Stat = Stat !Double !Int, we tell the compiler: "Never construct a Stat unless the fields are evaluated."

    When foldl' forces z' (the Stat object), the strict fields forces the addition operations inside. The memory footprint remains constant (two numbers) regardless of the list size.

Summary

When processing large streams or lists in Haskell:

  1. Always prefer foldl' (from Data.List) over foldl.
  2. Profile first. Use +RTS -h to confirm the leak is accumulator-based.
  3. Check your Accumulator. If you are folding into a tuple (a, b) or a custom record, ensure the fields are strict. Using foldl' on a lazy data structure will simply defer the space leak into the structure's fields.
  4. Use Strict or BangPatterns. For tight inner loops, strict data structures prevent thunk allocation entirely.
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