Moving Average from Data Stream
A easy-tier problem at 80% community acceptance, tagged with Array, Design, Queue. Reported in interviews at Spotify and 8 others.
Moving Average from Data Stream hits your assessment, and you blank on how to keep memory linear while values stream in constantly. It's marked easy, but the trick isn't obvious if you code it naively. The problem shows up at Meta, Google, Tesla, and LinkedIn, among others, because it filters on a real constraint: you can't store the entire history. You need a structure that discards old data automatically. If this problem lands in your live OA and you freeze, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Moving Average from Data Stream"
Moving Average from Data Stream 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 naive approach stores every number ever seen. That fails when the window is fixed and old values must evict. Use a queue. Each time a new number arrives, push it in. If the queue size exceeds your window size, pop the oldest. Calculate the sum from what remains. That's it. The trap: candidates try to recompute the sum from scratch each time instead of maintaining a running sum, or they use arrays and shift, wasting cycles. This is a Design problem hiding inside a Data Stream problem. The pattern is: circular window, fixed capacity, FIFO eviction. When you've drilled Array and Queue basics but missed this specific assembly, StealthCoder is your hedge.
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Moving Average from Data Stream 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 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.
Moving Average from Data Stream interview FAQ
Is this problem really asked at Meta and Google?+
Yes. It appears in reports from nine major companies including Meta, Google, Tesla, and LinkedIn. The problem tests whether you understand stream processing and memory-bounded design, both relevant to real production systems. 80% acceptance rate is misleading; the trick catches people in the live OA.
What's the actual trick?+
Maintain a queue of the current window and a running sum. When you add a number, add it to the sum. When the queue exceeds window size, subtract the evicted number from the sum before removing it. Update the average in O(1) per call. Most candidates recalculate the sum naively and waste time.
Which topic is this really testing?+
It's Design disguised as a data structure problem. You need Queue to track order and eviction, Array or a list to hold values, and arithmetic to maintain the sum. The insight is architectural: design a stateful object that processes a stream correctly under memory constraints.
How does this connect to real interviews?+
Stream processing and windowed aggregates appear in production systems: rate limiters, real-time metrics, time-series databases. This problem tests whether you think about state and memory bounds, not just algorithm speed. Companies like Spotify and Arista care deeply about this.
What's the most common mistake?+
Recalculating the average by summing all values in the queue on every call. This tanks performance and shows you didn't think about efficiency. The other mistake: using an unbounded array and ignoring the window constraint until the interviewer asks about space.
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