Logger Rate Limiter
A easy-tier problem at 77% community acceptance, tagged with Hash Table, Design, Data Stream. Reported in interviews at AppFolio and 9 others.
Logger Rate Limiter hits your assessment and you freeze on the timestamp logic. Netflix, Google, and Apple ask this one. It's an easy problem on paper, but candidates tank it because they miss the window calculation or implement the obvious solution and fail on edge cases. The acceptance rate is 76%, which sounds high until you realize you're competing against people who nailed the design pattern. The trick is simple once you see it, but the pattern repeats across follow-ups. If this lands in your OA and you blank, StealthCoder solves it invisibly in seconds.
Companies that ask "Logger Rate Limiter"
Logger Rate Limiter 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 who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe pattern here is a hash table keyed by message ID, storing the most recent timestamp. You check if the current timestamp minus the stored timestamp is within the 10-second window. If it is, reject. If it isn't, or the ID's never been seen, allow and update. Most candidates overengineer this with queues or sliding windows. Others implement it cleanly but mess up the boundary condition: whether the window is strict inequality or inclusive. The real edge case is when the same ID logs twice in quick succession. You need to store one value per ID and update it atomically. This is a design problem wrapped in a data-stream skin. It tests whether you think in state, not just algorithm. When you hit this live and doubt your solution, StealthCoder validates it in real time.
Pattern tags
You know the problem.
Make sure you actually pass it.
Logger Rate Limiter recycles across companies for a reason. It's easy-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 who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Logger Rate Limiter interview FAQ
Is this actually easy or are they lying about the difficulty?+
It's genuinely easy if you see the pattern. Hash table, store timestamp, check the window. The 76% acceptance rate confirms that. Most failures come from overthinking or boundary errors, not algorithmic gaps. Once you've done one, you own the archetype.
Do Netflix and Google really ask this or is it noise?+
Yes. It appears in 10 different company reports including Netflix, Google, and Apple. Not noise. It's a real design interview pattern they use to test if you understand rate limiting without overbuilding it.
What's the trick I'm missing?+
No trick. The problem is straightforward: store one timestamp per ID, check if the gap to now is under 10 seconds, allow or reject. The gotcha is the boundary condition. Decide whether your window is inclusive or exclusive and stick with it. Get that right and you're done.
Is this asked at the same companies as other design problems?+
Partially. Atlassian, Intuit, and Oracle also ask it, but you'll see design patterns vary widely. Logger Rate Limiter is lower barrier than, say, LRU Cache. It's often asked earlier in interview loops.
How does this relate to real rate limiting?+
It's a toy model. Real rate limiting involves distributed state, clock skew, and token buckets. This problem teaches the single-machine hash-table mental model. It's the foundation architects expand into production systems.
Want the actual problem statement? View "Logger Rate Limiter" on LeetCode →