Diagnosing and Fixing Space Leaks in Haskell Programs

Recognizable Condition

During execution the resident memory (RSS) or heap size grows steadily while CPU usage remains low. The program may slow down or be terminated by an OOM limit despite minimal computation.

Cause and Diagnostic Table

Ordered Checks

1. Recompile with profiling support: ghc -prof -fprof-auto -rtsopts Program.hs -o Program. Requires source write access and GHC invocation.
2. Run with runtime flags: ./Program +RTS -p -hy. The -p flag generates a time/allocation report (.prof); -hy produces a heap profile by closure type (.hp.
3. Examine .prof for cost centres with high allocation or time. Identify entries labelled THUNK or CONSTR retaining large memory fractions.
4. If a growing retained set appears in the heap profile, use hp2ps Program.hp for a PostScript graph or hp2pretty for interactive viewing. Identify the cost centre with the largest band.

Fixes Tied to Findings

• Replace a hot spot foldl with the strict version Data.List.foldl' or add a bang pattern: foldl' (\acc x -> acc + x) 0 xs.
• For lazy maps that build thunks, switch to Data.Map.Strict or insert seq/bang patterns at update sites.
• When large lazy I/O is the cause, replace getContents or hGetContents with strict reads from Data.ByteString (ByteString.hGet) or Data.Text.IO.
• Add explicit strictness annotations where profiling indicates thunk buildup, using BangPatterns (!x) or seq to force evaluation at accumulation points.

Escalation Criteria

• If the heap profile shows no large THUNK/CONSTR retention but memory still grows, consider runtime system bugs, foreign memory leaks (C FFI), or excessive thread stacking. Check with +RTS -s for GC stats and thread counts.
• Enable detailed GC statistics: ./Program +RTS -s and look for increasing total memory in use or high GC time.
• Consult GHC Trac or mailing lists, providing the .prof, .hp, and GC output for further analysis.

Verification

1. Re-run the program with the same profiling flags after applying a fix.
2. Confirm the heap profile no longer exhibits a large retained band for the previously suspect cost centre.
3. Check that RSS or heap size remains bounded across varied input sizes matching production workloads.
4. Ensure the test suite passes and performance benchmarks show no regression; if performance degrades, re-evaluate strictness placement.

Note: Over-strictness can remove laziness benefits and may cause stack overflows; apply strictness only where profiling indicates a thunk buildup, and verify semantics after each change.