Choose an overload policy from the quote deadline budget
At overload, a larger queue increases completed work but most quotes arrive after the dispatch screen has already given up.
- Focused work estimate
- 6h + prerequisites
- Priority in the scenario
- High
- Engineering practice
- Capacity planning · Tail latency · Load testing
Estimated field mix
- Performance engineering60%
- Site reliability40%
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
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Project context
A fictional freight broker sees slow quote responses at dispatch handover. The service looks healthy in average-latency charts, yet a few long requests occupy every worker. Build a small synthetic quote service and controlled dependency stub before taking implementation tickets.
Setup prerequisites
- Create a local quote endpoint and a deterministic carrier-price stub; no repository or dataset is supplied.
- Use synthetic routes and a fixed workload manifest. Record runtime, machine resources and instrumentation settings.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
- PLATENCY-101 · Write down the traffic mix before comparing quote timings
- PLATENCY-102 · Show quote latency percentiles alongside rejected and timed-out requests
- PLATENCY-103 · Separate quote queue time from carrier lookup time
- PLATENCY-105 · Stop expired quotes from holding carrier connections
- PLATENCY-106 · Bound parallel carrier lookups without serializing every quote
Acceptance criteria
- Compare at least two admission policies at 0.5, 1.0 and 1.5 times the measured sustainable arrival rate.
- Evaluate deadline success rate, rejection rate, p99 and queue depth together; document the selected tradeoff.
- Implement the selected bounded policy and retain exact pricing correctness for every admitted request.
Implementation constraints
- Use an open-loop arrival schedule and account for generator saturation; define sustainable rate from the recorded local baseline, not a guessed production number.
Verification to include
- Run three repetitions per load level after fixed warmup and publish spread, request counts and all timeout outcomes.
- Inject a carrier slowdown midway through a run and show queue depth remains bounded and recovery does not require restart.
Deliverables
- Admission decision record, load results and overload recovery regression
Rollout and recovery
Canary against the declared deadline-success guardrail; revert policy configuration if correctness or rejection behavior differs from the approved contract.
Value of the work
For the engineer: Learn to distinguish queueing, dependency delay, CPU work and measurement mistakes using reproducible observations.
For the team: Produce a reviewable diagnosis and guarded changes that could guide a team investigating customer-visible latency.
Evidence boundaries
Outcome Evidence: Tests, patches, and runbooks are requested deliverables. They become Outcome Evidence only through a qualified Mission and immutable Evidence IDs.
Ownership Evidence: Independent adaptation must be observed under a declared verification policy and cite immutable Evidence IDs. Completing a planning ticket establishes no Ownership Evidence.