Cache With Time Limit
A medium-tier problem at 76% community acceptance, tagged with . Reported in interviews at ThousandEyes and 2 others.
Cache With Time Limit is a medium-difficulty problem that hits your OA with deceptive simplicity. You're building a cache that expires entries after a given TTL. Netflix, ThousandEyes, and Oracle have all asked this one. The trap: most candidates nail the basic get/put/remove logic, then realize they haven't actually implemented expiration. The real trick isn't the data structure, it's handling time-based eviction without brute-force scanning on every operation. If you freeze on the pattern during the live assessment, StealthCoder reads your screen and surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Cache With Time Limit"
Cache With Time Limit is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe obvious approach, a hashmap with timestamps, fails the moment you ask: when do you actually delete expired entries. Scan everything on every read? That's O(n) garbage collection and will TLE. The winning pattern uses a min-heap or priority queue to track expiration times, paired with lazy deletion on access. Some candidates reach for a background thread or timer, which overcomplicates the problem. The real insight is understanding that you only need to evict what you touch, not maintain perfect staleness across the entire cache. Time arithmetic and heap operations trip people up faster than the logic itself. This is where StealthCoder shields you: if you blank on heap ordering or how to handle deletions mid-operation, you get a clean, working implementation without breaking your screen share.
You know the problem.
Make sure you actually pass it.
Cache With Time Limit recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Cache With Time Limit interview FAQ
Is this problem still asked at Netflix and Oracle?+
Yes. Cache expiration is a systems-design adjacent problem that stays relevant. Oracle and Netflix both need candidates who understand TTL mechanics and can avoid brute-force eviction. It's a popular follow-up to basic hashmap questions.
What's the main trick candidates miss?+
Thinking expiration happens automatically. Most code stores timestamps but never deletes. The insight is lazy deletion paired with a priority queue or heap to track which entries expire next, not scanning the whole cache on every operation.
How does this relate to system design?+
It's a scaled-down version of Redis TTL or memcached eviction policies. Understanding this problem teaches you why databases use time-ordered structures and why naive cleanup is expensive. It's the coding root of real caching strategy.
What data structure do I actually need?+
A hashmap for O(1) get/put, plus a min-heap or priority queue ordered by expiration time. Some solutions use a deque for insertion order if entries have the same TTL policy, but heap-based approaches are cleaner and handle variable timeouts better.
Will I hit time limit with a naive solution?+
Yes, if you scan the cache to delete expired entries on every operation. With 70%+ acceptance, most people pass by avoiding that trap. Heap-based lazy deletion gets you comfortably within time and space bounds.
Want the actual problem statement? View "Cache With Time Limit" on LeetCode →