High frequency API engineering
Build around useful throughput, bounded latency, and predictable failure behavior—not an unexplained requests-per-second claim.
Start with the workload
A high frequency API needs a concrete workload definition. Describe the operations, expected input sizes, traffic bursts, concurrent clients, and deadlines before choosing a transport or concurrency setting. A small lookup and a long model request should not silently consume the same capacity budget.
For this guide, treat performance as a contract between a client and a system. Define what counts as an accepted result, how late work is handled, and what the client sees during overload. That contract makes optimization decisions easier to evaluate.
Four boundaries worth designing
Deadlines
Carry a remaining time budget through the request path. Connection waits, internal queues, downstream calls, and output validation all consume time. A fresh timeout at every hop can hide an excessive total wait.
Admission control
Bound queue depth and concurrency. Reject or defer work explicitly when capacity is unavailable. A small queue can be more useful than a long line of requests that will all miss their deadlines.
Retry budgets
Separate transient failures from invalid credentials or malformed requests. Avoid automatic replay when an operation may have changed external state and its outcome is uncertain. Add bounded backoff where replay is appropriate.
Observability
Track successful work, rejected requests, invalid results, and timeouts separately. Median latency and high-percentile latency describe different parts of the experience; neither should hide failure counts.
Choose transport by communication pattern
| Pattern | Start by evaluating | Question to resolve |
|---|---|---|
| Bounded request and response | HTTP request | What is the total deadline? |
| Incremental server output | Streaming response | How is partial completion represented? |
| Ongoing two-way exchange | WebSocket | How is session state recovered? |
| Long-running background work | Job and status interface | What proves completion or cancellation? |
This is a design starting point, not a universal protocol ranking. Prefer the simplest approach that supports your task and recovery requirements. The streaming guide explains buffering, event parsing, and reconnect behavior in more detail.
Treat idempotence precisely
The HTTP semantics standard describes safe and idempotent methods. An application must still document how a repeated operation is handled. An arbitrary idempotency header does not establish server-side deduplication unless the endpoint actually supports that contract.
For state-changing operations, preserve an operation identifier, reconcile uncertain outcomes, and document how long duplicate detection remains valid. For read-only calls, keep the retry budget inside the original deadline so recovery does not become an unbounded traffic multiplier.
A practical acceptance test
Run a representative request set at controlled concurrency. Include a slow dependency, a rate-limit response, a large input, and a connection interruption. Observe whether the API returns a documented state, releases cancelled work where supported, and preserves capacity for control operations.
Record the conditions beside the results: version, environment, payload mix, warmup, arrival pattern, and exclusions. Keep raw observations where appropriate. Do not turn a component benchmark into an end-to-end promise for a workload it did not measure.
Continue with the full API design article, our benchmark methodology, and the reliability checklist.
Read the architecture guide