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Render is a cloud application hosting platform that provides developers with an easy way to build, deploy, and scale web apps, APIs, databases, and static sites. It is known as a modern alternative to platforms like Heroku, offering managed infrastructure with automatic deployments from Git.

3 interview notes · updated Jul 2026

Render·Software Engineer·Online Assessment (OA)

Jun 2026
Render gave me a coding assessment centered on a rate-limiting simulation over a CSV dataset. The problem looked manageable at first glance but the edge cases around sliding windows and double-counting blocked requests had me second-guessing myself for a while.
  • Given a CSV log of HTTP requests with timestamp, IP, and hostname fields, implement a sliding-window rate limiter with two simultaneous rules: one capping total requests per IP across all hosts, and another capping requests per IP per hostname, both over a configurable time window. A request is blocked if either rule is violated, and blocked requests still count toward future rate-limit decisions. Return the total number of blocked requests for the provided dataset.

“The part that tripped me up was the double-counting rule.”

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Render·Software Engineer·Take-home Assignment

Jun 2026
Render gave me a take-home coding problem centered on rate limiting. Pretty well-scoped, one question, and you either get the edge cases or you don't.
  • Implement a sliding-window rate limiter and run it against a CSV log of requests. A single IP is blocked if it exceeds 50 requests within any 60-second window. Blocked requests still count toward the window. Return the total number of blocked requests across the full dataset.

“The two edge cases are where people trip up.”

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Render·Software Engineer·Take-home Assignment

Jun 2026
Render gave me a take-home style coding problem centered on rate limiting logic with sliding windows. Two parts, escalating complexity, and you really need to think about efficiency from the start or you'll paint yourself into a corner on part 2.
  • Given a sorted in-memory request log with timestamps, IPs, and hostnames, implement a sliding window rate limiter that counts how many requests would be blocked when a per-IP request cap is enforced over the previous T seconds. Blocked requests still count toward future windows.
  • Extend the rate limiter to also enforce a per-(IP, host) cap alongside the existing per-IP cap. A request is blocked if either rule triggers, but it only counts as one blocked request even if both rules fire simultaneously. Both counters must still be updated regardless of whether the request was blocked.

“The sliding window boundary being exclusive on both ends tripped me up at first.”

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