Logging API Calls

Why log calls

When an integration misbehaves, logs are usually how you find out what happened. A good record of each outbound call lets you answer the questions that matter during an incident: what did we send, what came back, how long did it take, and did it succeed?

Without that record you are reduced to guessing or reproducing the problem live, which is slow and sometimes impossible for a transient failure. Logging is the cheap insurance you only appreciate at 2am. The aim is to capture enough to reconstruct a call's story after the fact, while staying mindful that logs themselves can become a liability if they hold the wrong things.

What to capture

Log the facts that help diagnosis, for both the request and the response:

• Request — method, endpoint, a correlation id, and timing.
• Response — status code, duration, and size or a summary.
• Outcome — success, the error category on failure, and retry count.
• Context — which operation and environment this belonged to.

Resist logging entire request and response bodies by default — they are bulky and often contain sensitive data. Log a summary, and capture full bodies only behind a debug switch when you genuinely need them. The goal is a useful trail, not a firehose. Pair this with monitoring for the aggregate view.

Correlation and tracing

A single user action often spans several calls and services. A correlation id — a unique value attached to the original request and carried through every downstream call — lets you stitch the whole story together from scattered logs.

log.info('api call', extra={
    'correlation_id': cid,
    'endpoint': '/resource',
    'status': 200,
    'duration_ms': 142,
})

Generate the id at the entry point and propagate it everywhere. When something fails, you can filter every log line by that one id and see the entire chain of events in order. Without correlation, related log lines are scattered and nearly impossible to assemble under pressure. This single discipline pays for itself the first time you debug a cross-system failure.

Redact and structure

Logs are frequently shipped, stored and read by many people, so they must never contain secrets or sensitive personal data. Redact credentials, tokens and confidential fields before anything is written — see secrets management.

Prefer structured logging — key-value fields rather than free-form prose — so logs can be filtered, aggregated and searched by machine. A structured field for status, duration and correlation id is far more useful than the same data buried in a sentence. Finally, use log levels deliberately: errors and warnings for things that need attention, info for the normal trail, and verbose body-level detail only behind a debug flag so production stays readable and affordable.