What is a Circular Queue? Why is it needed?

Imagine you're building a simple keyboard input buffer for a game. You have a fixed-size array of 5 elements. If a user types quickly, how would you implement the buffer so that new keystrokes overwrite the oldest ones without shifting all the elements in memory every time?

We have a fixed-size array-based queue. After enqueuing 5 items and dequeuing 3, our tail pointer is at the end of the array, but we have 3 empty slots at the front. How would you write the logic to insert a 6th item without resizing the array?

We are seeing a memory leak and high CPU usage in our logging microservice. The developer used a standard dynamic array-backed queue to buffer logs before writing them to disk. Why might switching to a fixed-size ring buffer solve this, and how would you handle the case where the log generation rate spikes and the buffer fills up?

You are implementing a sliding window rate limiter that allows at most 100 requests per minute. If you use a circular queue to track the timestamps of the last 100 requests, how would you handle the cleanup of expired timestamps when a new request comes in?

We're designing a high-throughput, low-latency audio streaming player. We need a buffer between the network thread downloading chunks and the audio rendering thread. Why is a circular queue a good fit here, and how do you handle thread synchronization without introducing heavy lock contention?

In a multi-producer, single-consumer telemetry pipeline, we want to use a circular queue to buffer metrics before flushing them. What are the edge cases when the queue is completely full versus completely empty, and how would you design the pointer updates to be thread-safe?

Our embedded IoT devices are running out of memory because of erratic network connectivity causing telemetry data to pile up. We want to enforce a strict memory bound using ring buffers across all device micro-agents. How would you design a standardized buffering framework that allows different teams to configure drop-oldest vs. block-producer strategies, and how do you handle data corruption recovery if a device suddenly reboots mid-write?

We are migrating a legacy high-frequency trading execution engine from a lock-based queue to a lock-free ring buffer (like the LMAX Disruptor pattern) to reduce tail latency. How would you pitch this architectural change to the risk team, and how do you design the migration path to ensure zero-loss guarantees during the transition?