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At a pub, one public IP address can represent dozens of people on separate phones. When a rate limit treats that address as one user, ordinary activity can exhaust the whole venue’s allowance. Daniel Pertu describes how polling fallback made that happen in his pub-trivia.app service—and why he now keys some limits to participant or account identity instead.
How one IP limit could block a whole venue
Pub-trivia.app normally uses WebSockets to deliver live updates to players’ phones. When a client cannot establish that connection, it falls back to polling. At a venue, many phones may share the same public Wi-Fi exit IP because the network uses NAT (network address translation).
In his 2026 account, Daniel Pertu illustrates the resulting load with 100 players polling six times per minute each: 600 polling requests per minute, before joins, page loads, or answers. His service’s previous global limit was 300 requests per minute per IP, set to accommodate a burst of players joining. If the players were sharing one exit IP and polling, that single IP’s requests could exceed the limit and cause the venue’s clients to be rejected just when polling was needed. These figures describe Pertu’s example, not a typical pub or an independently measured incident workload. Pertu’s account of the incident
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Model the normal path and the fallback path
A capacity estimate based only on the normal transport or a join burst can miss the load generated when the service degrades. If WebSockets are unavailable for some clients, polling can turn each client into a steady stream of HTTP requests. The limiter must account for that traffic pattern as well as joins, page loads, answers, and any other requests sharing its budget.
Pertu later used another illustrative calculation: 60 phones polling six times a minute amount to 360 requests per minute. This is an example published by the same author, not a general traffic benchmark. Pertu’s follow-up example
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- Estimate requests per client in both normal and fallback modes.
- Multiply by the number of clients that may share one exit IP.
- Add other endpoint traffic that consumes the same limit.
- Decide whether the limit applies to an individual action, a user, or an entire network address.
Choose the limit key for each endpoint
An IP address is not a universal stand-in for a person. The useful key depends on what action is being controlled and what identity is available at that point in the request flow. Pertu reports using participant IDs for player polling and answer submissions, and authenticated user IDs for host controls. He retains IP-based limits for selected endpoints such as sign-in, password reset, and joining, as well as a broad proxy guard. Pertu’s description of endpoint-specific keys
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11| Limit key | What it represents | Consideration |
|---|---|---|
| IP address | Requests from a network exit | Useful when identity is unavailable or the address is itself a relevant signal, but shared NAT can aggregate legitimate users. |
| Participant ID | A player in the trivia application | Can distinguish player activity on a shared network for endpoints such as polling and answer submission. |
| Authenticated user ID | A signed-in account | Can apply a host-control budget to the account operating those controls. |
These keys are not interchangeable. Identity-based limits require an identity to be established and trustworthy for the request; an IP guard may still provide a separate broad control. Pertu’s reported revision raised the global per-IP guard from 300 to 1,200 requests per minute, while the more important design change was choosing identity-based keys for particular player and host actions. The numbers are his service’s reported settings, not recommended thresholds for other applications. Pertu summarizes his view as “The key is the design, not the limit.”
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Decide what happens if the limiter’s datastore fails
Pertu says his rate limiter uses Upstash Redis and fails open if Redis is unreachable: requests are allowed without rate-limit enforcement. That preserves application availability when the limiter’s datastore is down, but it also removes the protection the limiter provides during that period.
Whether to fail open or closed depends on the consequences of each failure. Failing open prioritizes continued service; failing closed preserves enforcement but can reject legitimate traffic because the limiter cannot check it. Make the choice deliberately for the endpoint and threat model rather than treating either policy as universally safe.
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Make the rate-limit error path usable
A limiter can create a second failure if the page it sends users to is itself limited or requires authentication they do not have. Pertu reports exempting the /too-many-requests destination from the limiter and making it publicly reachable, so a visitor can see the error instead of triggering another limit or being sent through an authentication redirect.
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What the incident does—and does not—establish
Pertu’s account illustrates a real design risk: when many legitimate clients share an IP and a fallback transport increases request volume, an IP-keyed budget can behave like a venue-wide cap. The article does not provide independent production telemetry or a quantified before-and-after outcome, so its traffic examples and remedy should be read as the author’s reported experience, not proof that a particular threshold will work elsewhere.
The practical lesson is to size limits against aggregated legitimate traffic and degraded modes, then key each endpoint to the behavior or abuse unit it is intended to control. An IP can still be a useful signal; it simply does not always equal one user.
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