Error handling patterns provide structured approaches to detecting, reporting, and recovering from failures. Good error handling makes systems robust, debuggable, and predictable. Poor error handling hides bugs, corrupts state, and produces cascading failures.

Pattern Comparison

Pattern	Purpose	Mechanism	Key Benefit
Guard Clause	Validate inputs at entry, bail early	Early return / raise	Flat, readable control flow
Result Type	Encode success-or-error in return value	`Result<T, E>` / Either	Compiler-enforced error handling
Fail Fast	Detect errors immediately at boundaries	Validate + abort on first error	Prevents silent corruption
Exception Hierarchy	Organize exceptions in a meaningful tree	Class inheritance	Granular catch + categorization

When to Use Which

Validating function arguments at the top? Use Guard Clause to reject bad input early.
Returning errors without exceptions? Use Result Type for explicit success/failure.
Want to crash immediately on invalid state? Use Fail Fast to prevent downstream corruption.
Need categorised, catchable errors? Use Exception Hierarchy for domain-specific error trees.

Relationships Between Patterns

Guard Clause is often the first check inside a function; it may raise exceptions from an Exception Hierarchy or return a Result Type.
Fail Fast philosophy motivates Guard Clauses — both prefer early detection over late recovery.
Result Type and Exception Hierarchy are alternative error-propagation mechanisms: Result is explicit in the type system; exceptions use control flow.
Languages like Go and Rust strongly favour Result Type; Java and Python traditionally use Exception Hierarchy.

Design Guidelines

Be explicit — callers should not be surprised by errors.
Fail close to the source — detect problems where they originate.
Provide context — error messages should include what went wrong, why, and what to do.
Don't swallow errors — empty except: / catch blocks hide bugs.
Match error granularity to recovery needs — don't over-categorise if callers won't distinguish.