ReadOnly Patterns in TypeScript, C#, and Java

Speed, Safety, and ReadOnly: Improving Code ReliabilityRead-only concepts appear across programming languages and systems with slightly different names — immutable, const, frozen, readonly — but they share a single idea: data that can’t be changed after creation. Applying read-only patterns deliberately improves reliability by reducing bugs, enabling optimizations, and clarifying intent for other developers. This article examines why read-only matters, where and how to apply it, trade-offs to consider, and practical patterns and examples in modern languages.

Why read-only matters

• Safer reasoning: When data cannot change, you eliminate whole classes of bugs caused by unexpected mutations. Functions that consume immutable inputs are easier to understand and test.
• Concurrency friendliness: Immutable data can be safely shared across threads without locks, avoiding race conditions and reducing synchronization overhead.
• Performance opportunities: Some runtimes and compilers can optimize code when they know values won’t change (e.g., sharing interned structures or avoiding defensive copies).
• Clearer contracts: Read-only types signal developer intent; an API that returns a read-only view sets expectations about mutability and helps prevent accidental modification.

Types of read-only semantics

• Shallow vs deep immutability:
  • Shallow read-only (e.g., a constant reference): the reference or top-level container is immutable, but the objects it points to may still be mutable.
  • Deep (transitive) immutability: the entire object graph is immutable.
• Compile-time vs runtime enforcement:
  • Compile-time (e.g., const/readonly keywords, type system enforcement) prevents certain mutations before the program runs.
  • Runtime enforcement (e.g., Object.freeze in JavaScript) throws errors or silently prevents writes at runtime.
• Structural vs nominal immutability:
  • Structural immutability means the data’s structure cannot change (typical of functional languages’ persistent data structures).
  • Nominal immutability ties immutability to types or classes.

Language examples and idioms

• TypeScript
  • readonly properties and Readonly mapped types provide compile-time guarantees for many common use cases.
  • Example benefit: avoiding accidental property assignment in APIs; type system enforces non-mutating usage.
  • Limits: TypeScript’s readonly is structural and shallow; nested objects remain mutable unless each level is readonly-ified (e.g., DeepReadonly utility).
• C#
  • readonly fields and the readonly modifier for struct members, plus ImmutableArray and System.Collections.Immutable for deep immutability.
  • record types (C# 9+) provide concise syntax for creating immutable data models with value-based equality.
• Java
  • final for variables and unmodifiable wrappers in Collections; Project Valhalla and other improvements are evolving Java’s approach.
  • Common idiom: expose unmodifiable views (Collections.unmodifiableList) while retaining internal mutable lists.
• Functional languages (Haskell, Clojure, Elm)
  • Immutability is the default, with persistent data structures that share memory efficiently.
  • Concurrency models (actors, STM) combine naturally with immutable data.
• JavaScript
  • Object.freeze and libraries for immutable data (Immutable.js, Immer).
  • Modern patterns: use pure functions and avoid mutation; copy-on-write + structural sharing libraries give performance-friendly immutable semantics.

Practical patterns and guidance

• Prefer immutable data for domain models and values where identity doesn’t change (configuration, DTOs, most state snapshots).
• Use read-only interfaces for API boundaries:
  • Return read-only collections to prevent callers from mutating internal state.
  • Accept read-only parameters where functions should not modify inputs.
• Apply immutability incrementally:
  • Start with shallow immutability (readonly fields, const) and enforce deeper immutability where bugs or concurrency demands require it.
• Use copy-on-write and persistent data structures to maintain performance with immutability:
  • Structural sharing lets you avoid full copies while producing logically new versions.
• Document intent clearly:
  • Marking something readonly is both a technical and communicative act. Combine type-level guarantees with API docs.
• Defensive copying when exposing mutable internals:
  • If you can’t use read-only wrappers, return a defensive copy to prevent callers from altering internal state.
• Immutable by default in libraries:
  • Libraries that return immutable data reduce surprises for users and make APIs safer by default.

Performance considerations

• Costs:
  • Naive copying on every change can be expensive for large structures.
  • Deep immutability enforcement at runtime can incur allocation and CPU costs.
• Mitigations:
  • Use persistent data structures (trees with structural sharing) to reduce copying cost.
  • Use lazy copying, copy-on-write, or builders to batch mutations then produce an immutable result.
  • Let the compiler/runtime optimize when immutability is visible — for example, JITs can inline and optimize code paths that rely on const-like guarantees.
• Measure and profile:
  • Don’t assume immutability always improves performance. Benchmark relevant workloads and measure memory and CPU impacts.

Common pitfalls

• False sense of safety: shallow readonly is not full immutability — nested objects can still be mutated.
• Overuse: making everything immutable can lead to unnecessary copying or complex code when simple mutability would be fine.
• API friction: clients sometimes need to mutate data; design APIs to make common mutation patterns ergonomic (builders, withX methods, or transient mutable phases).
• Interop: integrating immutable structures with libraries that expect mutability requires careful adapters or conversion layers.

Examples

TypeScript — shallow readonly

type User = {   readonly id: string;   name: string; // still mutable }; function greet(u: Readonly<User>) {   // u.id is readonly; attempting u.id = "x" is a type error } 

C# — immutable record

public record User(string Id, string Name); // With-expression creates a new instance: var u2 = u with { Name = "New" }; 

JavaScript — Object.freeze

const config = Object.freeze({ apiUrl: "https://api", retries: 3 }); // Attempts to write to config in strict mode throw; otherwise writes are ignored. 

Persistent data example (conceptual)

• Instead of copying a large array for each change, use a persistent vector that shares most memory between versions and updates in O(log n) time.

When not to use read-only

• Low-level performance-critical inner loops where allocation must be minimal and overhead of immutability is measurable.
• Highly stateful objects where mutation models map directly to the problem domain and complexity of immutable patterns outweighs benefits.
• Quick prototypes where developer velocity matters more than long-term maintainability — but consider migrating later.

Checklist for adopting read-only practices

• Identify critical data: configuration, shared state, and public API surfaces.
• Enforce at compile-time where possible (readonly, const, type-level immutability).
• Use libraries or language features for deep immutability when needed.
• Provide ergonomic mutation paths: builders, withers, or controlled mutation phases.
• Profile to confirm performance is acceptable.
• Document immutability guarantees in API contracts.

Conclusion

Read-only and immutability are powerful tools for increasing code reliability, making programs easier to reason about, safer under concurrency, and sometimes faster due to optimization opportunities. The key is to apply them judiciously: combine shallow guarantees for everyday safety with deeper immutability where correctness, concurrency, or maintainability demand it. With practical patterns like persistent data structures, read-only interfaces, and builder-based mutations, you can get the best of both worlds—robust, clear code without undue performance cost.