Immutable Data Patterns
Slap yourself if
You think immutability just means using spread syntax or that it’s only about avoiding bugs.
Why this exists
Immutable data patterns exist to make state changes predictable, enable efficient change detection, and support advanced rendering models like memoization and concurrent rendering without subtle timing bugs.
Mental model
Immutable data patterns treat state as a series of snapshots. Instead of modifying existing data, you create new versions that share unchanged structure. This makes change detection explicit and reasoning about state over time reliable.
- State is treated as read-only once created.
- Updates create a new version of the state instead of mutating the old one.
- Unchanged parts of the data are reused by reference.
- Consumers rely on reference equality to detect what changed.
- Old and new state snapshots can safely coexist.
- Using shallow copies but mutating nested objects later.
- Assuming spread syntax guarantees deep immutability.
- Breaking referential equality by recreating unchanged objects.
- Mixing mutable and immutable updates in the same state tree.
- Relying on immutability without enforcing it consistently.
Immutable data patterns mean treating state as snapshots: updates create new versions instead of mutating existing data, enabling reliable change detection through reference equality and safer concurrent rendering.
- Immutability is only about preventing bugs.
- Using spread syntax makes data immutable.
- Immutable updates are always slow.
Deep dive
Premium deep dives include more internals, more scars.
How immutable update patterns actually work
How partial immutability breaks everything
Mixing mutable and immutable updates creates bugs that are extremely hard to trace.
Tradeoffs vs mutable state
Immutability trades short-term convenience for long-term correctness.
Performance implications of immutable patterns
Immutable data patterns enable performance optimizations — they don’t guarantee them.
Interview landmines
- Equating immutability with deep cloning
- Ignoring referential equality
- Not mentioning concurrency or memoization benefits
Related terms
Structural Sharing
You think structural sharing means copying objects less or that immutability always means deep cloning.
Referential Equality
You think two objects with the same values are equal in JavaScript or that referential equality is an implementation detail you can ignore.
Memoization Pitfalls
You think memoization automatically makes code faster or that using useMemo/useCallback is always a performance win.
Concurrent Rendering
You think concurrent rendering means React renders in parallel threads or it always makes apps faster.