The subtle trap of useMemo for large collections - and a tiny alternative

When working with lists in React, the go-to optimization for many developers is useMemo:

const filtered = useMemo(() => tasks.filter(filter), [tasks, filter]);
const sorted   = useMemo(() => filtered.sort(cmp), [filtered, cmp]);

This looks right, but it isn't:

1. If you mutate the array in place, the reference doesn’t change and useMemo will return stale results.
2. If you recreate the array every render (common in immutable flows), the memo dependency always changes and you pay the computation cost every time anyway.

Hence, I built a tiny alternative — memotable (≈1.4 KB min+gz). It’s a read-friendly data structure that maintains derived views efficiently. This matters only for a narrow set of apps (typically apps that manage large in-memory datasets).

Example

Instead of the typical write-friendly pattern-

const todos = new Map<string, ITodo>();

function getTodos(listId: string) {
    return Array.from(todos.values())
        .filter((todo) => todo.listId === listId
        .sort(byTitle);
}

getTodos("list1"); // Full pass and sort on every invocation

you can express the same as a read-friendly structure using memotable like-

const todos = new Table<string, ITodo>();

todos.sort(byTitle);
todos.index(
    (todo) => todo.listId,
    (p) => p.memo(), // Memoize list level partitions
);

todos.partition("list1"); // Pre-computed list enabling fast read
todos.values(); // Root partition is not-memoized and hence still sorts at read-time

If your app works with thousands of items and reads are much more common than writes, memotable may be useful. As an example- based on my synthetic benchmark with R/W ratio of 4:1, memotable is ~4x faster.

If you’ve hit similar problems with list-level memoization — in React or otherwise — I would love to hear about how you approached it.

This is a tiny library by design, and I’m still refining where it adds the most value. Contributions, feedback or comments - all are welcome: https://github.com/shudv/memotable

Comments

[Shubham Dwivedi]

Author here - happy to answer questions or hear any feedback!