Diagnosing and Fixing Space Leaks from Lazy Accumulation in Haskell

Recognizable condition

A Haskell program shows steadily increasing resident memory as it processes larger inputs, even though the algorithm should use constant or logarithmic space. The growth is often linear in the size of the input and persists after garbage collection.

Cause / diagnostic table

Symptom	Typical source
Memory grows proportionally to input length	Unevaluated thunks in an accumulator (e.g., lazy foldr)
Retainer set shows many THUNK objects	Lazy data structures built incrementally (lists, tuples) without forcing
IO actions appear in heap profile	Deferred evaluation of IO actions or unsafeInterleaveIO

Ordered checks

1. Compile with profiling enabled: ghc -O2 -prof -fprof-auto -rtsopts Main.hs. Run the program with a representative input size, e.g., ./Main +RTS -p -hc (requires read/write permission on the current directory).

2. Examine the heap profile (Main.hp) with hp2ps Main.hp or ghc-events show. Look for a large band labelled THUNK or FUN that grows with input size.

3. If the retainer set points to a specific function, inspect its Core: ghc -O2 -ddump-simpl -dsuppress-idinfo Main.hs. Search for the function name and see whether its arguments appear as lazy bindings (let x = ...) rather than strict ones.

4. Add a strictness annotation or seq to the suspect accumulator and re‑compile. Observe whether the thunk band shrinks in the next heap profile.

Fixes tied to findings

• Replace a lazy right fold with a strict left fold: change foldr f z xs to foldl' f z xs (import Data.List).

• Add bang patterns to accumulator fields: !acc in function arguments or record fields, requiring the BangPatterns extension.

• For complex data, force full evaluation with deepSeq from Control.DeepSeq: deepSeq acc $ ....

• When state is threaded through many steps, consider the strict state monad (Control.Monad.State.Strict) instead of the lazy variant.

Escalation criteria

If after applying the above fixes the heap profile still shows significant THUNK retention:

• Check for hidden laziness in libraries (e.g., mapM vs mapM_, traverse).
• Consider whether the algorithm inherently builds a large intermediate structure; redesign to process data in a streaming fashion using conduit or pipes.
• Verify that changes have not altered semantics in the presence of exceptions; run property‑based tests (QuickCheck) to confirm.

Practical verification

Create a small test case that feeds increasing input sizes (e.g., lists of length 10ⁿ). Run it with +RTS -s to see total memory allocated. After a fix, the allocated memory should plateau or grow sub‑linearly rather than linearly.

Limitations

Over‑strictness can eliminate beneficial sharing and degrade performance. Apply strictness only where profiling shows thunk buildup, and re‑benchmark to ensure runtime does not regress.