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Implement sliding-window rate limiter function | Atlassian Interview Question

Q: Implement sliding-window rate limiter function

This question evaluates proficiency with streaming algorithms and sliding-window data structures, focusing on time-based rate limiting, efficient counting, and space/time complexity management.

Problem

You are implementing a per-user rate limiter. Time is discretized into integer time buckets (e.g., each bucket is 1 second).

You are given two parameters:

x : window size in buckets
y : maximum allowed requests within any trailing window of x buckets

Implement:

boolean shouldPass(int timeBucket)

Each call represents an incoming request attempt at the given timeBucket. The function returns:

true if the request is allowed
false if the request should be rate-limited

Rate-limit rule

A request at time t should be allowed iff the number of request attempts whose bucket is in:

[t - x + 1, t] (inclusive)

is < y (i.e., the current request would not make it exceed y).

Assumption: Even rejected attempts still count toward the limit (they are still request attempts).

Notes / Constraints

timeBucket values are non-decreasing across calls.
Aim for efficient time and space usage as the number of requests grows.
Provide a few test cases (including edge cases such as repeated calls in the same bucket, large gaps between buckets, and bursts).

Problem

You are implementing a per-user rate limiter. Time is discretized into integer time buckets (e.g., each bucket is 1 second).

You are given two parameters:

x : window size in buckets

y : maximum allowed requests within any trailing window of x buckets

Implement:

boolean shouldPass(int timeBucket)

Each call represents an incoming request attempt at the given timeBucket. The function returns:

true if the request is allowed

false if the request should be rate-limited

Rate-limit rule

A request at time t should be allowed iff the number of request attempts whose bucket is in:

[t - x + 1, t] (inclusive)

is < y (i.e., the current request would not make it exceed y).

Assumption: Even rejected attempts still count toward the limit (they are still request attempts).

Notes / Constraints

timeBucket values are non-decreasing across calls.

Aim for efficient time and space usage as the number of requests grows.

Provide a few test cases (including edge cases such as repeated calls in the same bucket, large gaps between buckets, and bursts).

Implement sliding-window rate limiter function

Quick Overview

Problem

Rate-limit rule

Notes / Constraints

Submit Your Answer to Earn 20XP

Implement sliding-window rate limiter function

Quick Overview

Problem

Rate-limit rule

Notes / Constraints

Submit Your Answer to Earn 20XP