API Throughput
API throughput is the rate at which an API successfully serves requests over time, usually measured in requests per second or concurrent in-flight calls. Sustainable throughput depends on origin CPU, database connections, payload size, and per-request upstream fan-out, not only on raw network speed.
Technical Architecture & Deep-Dive
Peak throughput often collapses when payloads grow, TLS handshakes multiply, or every request fans out to three metered third-party APIs. Clients see errors or long queues even though average latency still looks healthy on a dashboard.
Raising throughput starts with removing redundant work: cache public GET responses at the edge, reuse keep-alive and pooled upstream connections, slim JSON before it crosses the wire, and bound parallelism with timeouts. Aggregation reduces request count on mobile, which indirectly raises effective throughput because each screen load consumes fewer server slots.
Key Architectural Benefits
Implement API Throughput at the Edge
Deploy lightweight API aggregation, edge caching, and secret injection in minutes without Docker containers or complex servers.
Frequently Asked Questions
Q:Is throughput the same as low latency?
No. An API can be fast for one user yet fail under concurrent load. Throughput measures how many successful requests the system sustains at once.
Related Architecture Concepts
Connection pooling is maintaining a set of open, reusable connections to a database or upstream service so requests can borrow an existing connection instead of opening a new one. It avoids repeated TCP and TLS handshakes and prevents exhausting server connection limits.
Upstream Fan-out is an execution strategy where an edge gateway dispatches multiple HTTP subrequests to separate origin services concurrently rather than sequentially. The total latency is bound by the single slowest subrequest (max time) rather than the cumulative sum of all subrequests.
Cache hit ratio is the percentage of requests served directly from a cache rather than forwarded to the origin. It is calculated as cache hits divided by total requests. A higher ratio means lower latency, less origin load, and fewer billable third-party API calls.
Parallel fetching issues multiple independent HTTP requests at the same time instead of waiting for each response before starting the next. When calls do not depend on one another, parallelism reduces wall-clock time to the slowest request rather than the sum of all round trips.
Ready-to-Use Edge Recipes
To solve call two rest apis in parallel and return merged payload, never execute unauthenticated requests or embed private tokens directly in client bundles. RealComposer acts as a sub-10ms edge gateway running on Cloudflare Workers that securely injects credentials from your Secret Vault, normalizes responses, and prevents unauthorized API usage. Route calls through your RealComposer edge URL to guarantee complete privacy, robust origin validation, and low mobile latency.
You do not need to spin up an Express, FastAPI, or Docker microservice just to stitch REST APIs together. RealComposer fetches multiple upstream endpoints concurrently at the edge, injects private credentials, and combines the responses into a single JSON schema with sub-10ms response times.