Haskell
Haskell is a statically typed functional language with non-strict evaluation. It is useful when explicit data models, composable transformations, and clear effect boundaries help express the problem. Start with a small complete program whose accepted inputs and failures are visible.
Types make many mistakes harder to express, but they do not prove your business rules or operating assumptions. A well-typed program can still diverge, exhaust memory, reject the wrong input, or perform the wrong authorized action.
TL;DR
- Use algebraic data types to represent meaningful alternatives and states.
- Represent expected parse failures explicitly instead of relying on partial functions.
- Separate pure transformations from IO at the program boundary.
- Understand when lazy evaluation helps composition and when retained thunks hurt space usage.
- Pin the build environment and test the packaged executable, including failures.
Quick Example
This complete Main.hs accepts double INTEGER or square INTEGER, using arbitrary-precision Integer values. It needs a compatible GHC/base toolchain. The source was reviewed here; GHC compilation and executable tests remain pending.
Proposed acceptance cases are double 7 → 14, square -3 → 9, and nonzero exit for missing arguments, an unknown verb, or a noninteger. These expectations are code-review conclusions, not observed compiled output. An unbounded integer avoids fixed-width overflow here but still needs input/resource limits in a real public-facing service.
Core Concepts
Algebraic data types
Command is a sum of two constructors; each carries an integer. Pattern matching requires the evaluator to handle the supported alternatives. Product types combine fields; sum types express alternatives. Model domain distinctions directly instead of encoding every state as an unrelated string. Haskell declarations
Type classes
A type class describes operations supported by participating types. Constraints such as Eq a state which operations a polymorphic function needs. Choose constraints that express actual requirements. A class is not simply a synonym for an object-oriented class or a container for mutable instances.
Non-strict evaluation
Expressions are evaluated as demanded rather than uniformly at binding time. This supports composition and potentially infinite structures, but deferred work can retain data. Non-strict semantics also mean a type can contain a computation that fails or never returns when demanded. Haskell expressions
Effects at the boundary
The parser and evaluator are pure functions. main obtains arguments and writes output in IO. This organization makes ordinary transformation tests easier, while keeping file, network, exception, and process behavior explicit where it occurs.
Design a small domain before adding abstractions
Start with the states that the program must distinguish. In the example, parsing either produces a valid command or a message. Evaluation cannot accidentally receive an unknown verb because that string has already been translated into a constructor.
Do not hide every invalid state inside error or a partial pattern. A parser that rejects invalid input explicitly gives callers a usable contract. Still test the contract: the compiler does not know whether the product intended to allow negative values or which command names should exist.
Add abstraction after repeated structure becomes clear. Type classes and higher-order functions can clarify a reusable rule, but an elaborate hierarchy can obscure a two-command utility. Evaluate the design by how easily another maintainer can follow a valid input and a failure through the program.
Diagnose space use and package the real executable
When memory grows, investigate the workload and retaining structures. Deferred computations can accumulate when a fold or pipeline retains unevaluated work. Strict fields, strict folds, or evaluation control may help a specific pattern, but adding strictness everywhere can change behavior and defeat useful laziness. Measure the target workload before choosing the change.
Use Cabal to describe the package, executable entry point, dependencies, and supported language/toolchain settings. Cabal’s getting-started guide covers project creation, building, and running. Keep the dependency plan and compiler version with the project so another developer can reproduce the build. Cabal workflow
Test the packaged artifact rather than only a REPL session. Verify exit codes, stdout versus stderr, Unicode/input behavior where applicable, file permissions, and deployment-library requirements. Include malformed and large inputs, not just double 7.
For production IO, specify timeouts, resource cleanup, exception handling, and retry semantics at the external boundary. Pure core logic reduces the surface that needs these concerns; it does not remove them from the program. A database transaction can fail even when the transformation inside it is perfectly typed.
Best Practices
Give domain failures domain names
Replace generic strings with a structured error type as the CLI grows. Keep rendering separate so user messages can change without altering programmatic classification.
Test properties and examples together
Examples verify intended cases; properties can explore relationships over many inputs. A property still needs a correct statement and meaningful generators. Test the pure core and the actual IO boundary separately.
Keep toolchain and runtime evidence
Record compiler, dependency plan, build flags, target platform, and acceptance output. Source review alone is not compilation, and compilation alone is not deployment validation.
Common Mistakes
Bad: Assuming a type signature proves the business rule.
Correct: Test the intended semantics, boundary inputs, and operational behavior.
Bad: Using partial reads for ordinary invalid CLI input.
Correct: Parse to an explicit success/failure result and render a useful nonzero-exit response.
Bad: Treating laziness as automatically memory-efficient.
Correct: Profile retained work and use targeted evaluation changes with correctness and space checks.
FAQ
Is Haskell completely free of runtime failures?
No. IO failures, resource exhaustion, partial functions, and nontermination remain possible. Types address particular classes of mistakes.
Why use Integer here?
It avoids fixed-width integer overflow in the arithmetic example. It does not make arbitrarily large input safe or free to compute.
What must be checked before shipping this CLI?
Compile with the selected toolchain, run positive and negative cases, and test the packaged executable on its target platform.
Related Topics
- Programming Languages — Compare language models and constraints.
- Concurrency and Parallel Computing — Connect evaluation and execution models.
- Memory Leak and Heap Growth Diagnosis — Investigate retained work and memory growth.