Design a Strongly-Consistent Distributed Counter for API Quotas
Overview
What this challenge is about.
Design a strongly-consistent distributed counter for API quotas. Choose storage, failover, and counter scheme. Earn a verifiable certificate.
The scenario
QuotaGuard sells quota-metering infrastructure to AI-application companies from Singapore, and a single miscounted decrement can let a customer exceed a paid limit or wrongly block a paying one. The over-consumption invariant is therefore a contractual and revenue concern, not just an engineering nicety.
The Brief
What you'll do, and what you'll demonstrate.
Design a strongly-consistent distributed counter service that sustains 50,000 quota decrements per second across three regions while provably preventing over-consumption during partitions, regional outages, and leader failovers.
Earning criteria — what you'll demonstrate
- Justify a storage and consensus choice against an explicit strong-consistency requirement rather than by default familiarity
- Design a counter scheme that provably prevents over-consumption at high write throughput
- Reason rigorously about behavior under partition, outage, leader failover, and clock skew
- Translate a workload and unit-price sheet into a defensible, reproducible cost estimate
- Communicate consistency/availability trade-offs clearly enough for a non-author reviewer to audit each decision
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Distributed Databases
Apply distributed databases to solve real industry problems and demonstrate production-level capability.
- Consensus
Apply consensus to solve real industry problems and demonstrate production-level capability.
- Strong Consistency
Apply strong consistency to solve real industry problems and demonstrate production-level capability.
- Sharding
Apply sharding to solve real industry problems and demonstrate production-level capability.
- Failure Mode Analysis
Apply failure mode analysis to solve real industry problems and demonstrate production-level capability.
- System Design
Apply system design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Systems Architect
This challenge mirrors the core of the role: making and defending consistency-versus-availability decisions for a high-throughput service, then communicating them so other engineers can audit the reasoning and the failure behavior before any code is written.
This challenge sharpens
- distributed-databases
- strong-consistency
- system-design
Distributed Systems Engineer
Designing a counter that holds an invariant across partitions and leader failovers is exactly the day-to-day of building replicated state. The failure-mode matrix and consensus reasoning here translate directly into production design reviews for replicated data stores.
This challenge sharpens
- consensus
- sharding
- failure-mode-analysis
Infrastructure Platform Engineer
Platform teams own shared metering and quota services that every product depends on. This challenge builds the judgment to size such a service, justify its storage backbone, and cost it credibly against real unit prices for leadership sign-off.
This challenge sharpens
- distributed-databases
- system-design
- sharding