Explain Memoization vs Caching.
Memoization vs caching both store results of expensive operations to avoid repeating work; memoization specifically caches a function’s return values by input, while caching is a broader practice of storing frequently used data.
When to Use
Use memoization for repeated calls to pure functions with the same inputs (e.g., recursion). Use caching for expensive backend operations like database queries, API responses, or rendered views that are reused across requests.
Example
Memoization: storing results of fib(10) so it runs once.
Caching: storing a frequently requested SQL query result in memory to serve multiple users quickly.
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Why Is It Important
These techniques are core to backend performance optimization, reducing latency and server load. They’re crucial in system design interviews.
Interview Tips
Explain that memoization is a specialized caching technique. Use one short example each, and mention trade-offs like memory usage or stale data risks.
Trade-offs
Both trade memory for speed.
Memoization saves computation at function level. Caching improves system-wide performance but adds invalidation complexity.
Pitfalls
Memoizing functions with unique inputs wastes memory. Caching without invalidation strategies leads to stale or inconsistent data.
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