Designing Decision and Logging APIs for Scalable API Protection

Introduction

When protecting APIs at scale you need two distinct but complementary capabilities: fast, deterministic decisions (allow/throttle/block) and rich logging for analytics and learning. The Decision API must be tiny and fast; the Logging API can be richer and asynchronous. This article explores design guidelines, data models, and deployment patterns for both components.

Why separate Decision and Logging?

Mixing heavy analytics into the request path kills latency. Separating responsibilities lets you optimize each path: the Decision API focuses on millisecond responses using in-memory state and simple checks, while the Logging API streams detailed events for storage, training ML models, and investigating incidents. This split also enables different scalability and reliability design choices for each service.

Decision API design

Key requirements:

• Sub-50ms latency: decisions must not add noticeable overhead.
• Stateless or small-state: use cached counters and fast in-memory stores.
• Deterministic outputs: clear actions like allow, throttle, restrict, block, and suggested headers (e.g., Retry-After).

Request payloads should be minimal: IP, API key/user id, user agent, endpoint, and basic headers. The response is a compact JSON with action and optional metadata (reason code, TTL for restriction).

Logging API design

The Logging API accepts full request/response metadata for downstream analytics: timestamps, full headers, payload fingerprints, status codes, latency, geo data, and decision reasons. Logging should be asynchronous and idempotent; use batching to reduce overhead. Stream logs into a message queue for downstream processing in analytics engines.

Data models and counters

Counters are the building blocks of decision logic. Maintain per-identifier counters across windows: second, minute, hour, day. Use rolling windows or fixed-window increments with decay for reputation scoring. Store counter deltas in a fast store (Redis or in-memory ART structures) optimized for high throughput.

High-throughput considerations

To achieve high throughput:

• Cache decisions: short-lived caching for identical requests to reduce repeated checks.
• Use eventual consistency: for non-burst windows, eventual consistency reduces coordination overhead.
• Autoscale: the Decision API should be horizontally scalable with minimal shared state.

Observability and audit trails

Decisions must be auditable. Track reason codes and counters that led to a decision. Correlate decision logs with analytics logs to allow post-mortem analysis. This helps fine tune thresholds and reduce false positives over time.

Integration patterns

Integrate the Decision API inline in a reverse proxy or as a nearline step in your edge stack. Use the Logging API in a fire-and-forget manner: the edge sends logs and continues. For many teams, adopting a managed product that provides both APIs reduces build/ops overhead. APIGate (https://apigate.in), for example, explicitly separates Decision and Logging APIs to deliver sub-50ms decisions while handling heavy analytics asynchronously.

Conclusion

Designing scalable Decision and Logging APIs requires clear separation of concerns, minimal request-path overhead, and robust logging for analytics. Follow these patterns to build a protection layer that’s both fast and intelligent—capable of reacting in real time while learning from historical data.