What is memoization and when would you use it in front-end or back-end code?

What is memoization and when would you use it in front-end or back-end code?

• A. Memoization stores results in a database for long-term persistence.
• B. Memoization duplicates memory to speed up I/O operations.
• C. Memoization computes all possible inputs in advance and stores them in cache.
• D. Memoization caches results of expensive function calls for given inputs to avoid recomputation; used in performance-critical code paths.

Answer: (D)

Memoization is a technique for saving the result of a function call so that if you call the same function again with the same inputs, you can return the saved result instead of recomputing it. This is most helpful in performance-critical code paths where the function is expensive and the same inputs appear repeatedly. In front-end code, memoization can stop expensive derived calculations from running on every render—for example, a costly data transformation that depends on props or state. In back-end code, it can speed up responses by caching the result of a heavy computation or a database query for the same parameters within a short time window. Key idea: the cache is usually in memory and keyed by the inputs, so it’s temporary and per-process, not long-term storage. If inputs change or underlying data updates, you need to invalidate the cache. This differs from storing results in a database or precomputing every possible input, which are separate approaches with different trade-offs.

Memoization is a technique for saving the result of a function call so that if you call the same function again with the same inputs, you can return the saved result instead of recomputing it. This is most helpful in performance-critical code paths where the function is expensive and the same inputs appear repeatedly.

In front-end code, memoization can stop expensive derived calculations from running on every render—for example, a costly data transformation that depends on props or state. In back-end code, it can speed up responses by caching the result of a heavy computation or a database query for the same parameters within a short time window.

Key idea: the cache is usually in memory and keyed by the inputs, so it’s temporary and per-process, not long-term storage. If inputs change or underlying data updates, you need to invalidate the cache. This differs from storing results in a database or precomputing every possible input, which are separate approaches with different trade-offs.