Plan and test capacity · 10 example briefs
Robust systems for demanding traffic. When the target is millions of requests, I translate the workload into architecture and testable capacity: peak traffic, latency, read/write mix, tenant isolation and recovery.
Platform engineering10 Agentic engineering10 High-scale systems10 System-design reviews10 Solution architecture10 SDK engineering10 Design systems10 ↗ Workload models and capacity targets↗ Caching, queues and bounded load↗ Load tests and measurable release gatesCapacity starts with the workload
Millions of requests is a target to define and test. I first establish the time window, peak concurrency, latency goals, read/write mix and recovery requirements. Then I choose the architecture and validate it with load tests. These examples describe design approaches, not measured throughput results.
Model the traffic Protect the edge Bound service load Queue slow work Measure & test Ways I can apply this capability
10 concrete examples. Example briefs showing the challenge, the approach I would take and the deliverable you can review. Related work is linked below.
01 Example brief
Read-heavy public API The challenge A popular endpoint receives uneven bursts of repeated reads. Engineering approach Cache safe responses, use stateless workers and protect the data store with limits. What you get A workload model, cache policy and peak-load test report. Discuss this challenge ↗ 02 Example brief
Payment event processing The challenge Duplicate delivery and downstream outages can corrupt financial workflows. Engineering approach Idempotency keys, durable events, bounded retries and reconciliation. What you get An event-processing path with replay and duplicate-delivery tests. Discuss this challenge ↗ 03 Example brief
High-volume webhook intake The challenge External providers retry events faster than workers can process them. Engineering approach Acknowledge validated events quickly, queue durable work and isolate failed consumers. What you get A webhook gateway with backlog monitoring and dead-letter recovery. Discuss this challenge ↗ 04 Example brief
Real-time notification delivery The challenge A burst of notifications can overwhelm users and delivery providers. Engineering approach Partition fan-out, enforce provider quotas and coalesce repeated notifications. What you get A delivery pipeline with throughput targets and per-channel receipts. Discuss this challenge ↗ 05 Example brief
Telemetry ingestion The challenge Many devices send small events continuously and during reconnect bursts. Engineering approach Partition by device, batch writes and separate ingestion from analytics. What you get An ingestion design with burst, ordering and backpressure tests. Discuss this challenge ↗ 06 Example brief
Large content delivery The challenge Global readers should not overload the origin for the same assets. Engineering approach Edge caching, explicit invalidation, origin protection and cache-aware releases. What you get A delivery architecture with cache-hit and origin-load measurements. Discuss this challenge ↗ 07 Example brief
Booking contention The challenge Concurrent users try to reserve the same scarce slot. Engineering approach Atomic reservation, expiration, idempotent confirmation and concurrency tests. What you get A booking flow that exposes contention and verifies single ownership. Discuss this challenge ↗ 08 Example brief
Tenant traffic isolation The challenge One busy customer slows down every other customer. Engineering approach Per-tenant quotas, queue partitions and resource budgets. What you get An isolation strategy with noisy-neighbor load scenarios. Discuss this challenge ↗ 09 Example brief
Search and analytics workload The challenge Expensive reporting queries compete with customer transactions. Engineering approach Separate serving and analytical paths, index query patterns and budget expensive work. What you get A data-access plan with latency and query-cost baselines. Discuss this challenge ↗ 10 Example brief
Recovery under peak load The challenge An outage creates a replay backlog while new traffic continues. Engineering approach Reserve recovery capacity, prioritize live traffic and rate-limit replay. What you get A failure drill with backlog-drain targets and documented recovery limits. Discuss this challenge ↗ Bring the hard part
Build it. Review it. Give it a better foundation. Tell me what you are building, what is breaking and what needs to change. We can start with a focused diagnostic and a concrete next step.