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Edge Request Timing and Logging

  • By Gcore
  • August 20, 2026
  • 2 min read
An API request flows through a timer labeled 'GOOD_USER' to generate detailed logs.

When your code runs across 100+ edge locations worldwide, understanding what each worker is doing becomes critical for debugging, optimization, and reliability. FastEdge provides logging and timing capabilities that give you visibility into every request processed at the edge.

This post covers Edge Request Timing and Logging — a pattern for adding observability to your edge workers.

Why Edge Observability Matters

Log request processing times and other metrics from your FastEdge workers for observability and debugging.

Without observability at the edge, you're flying blind. A slow response could be caused by a cold start, a network issue at a specific PoP, or a bug in your edge logic — and you'd have no way to tell which. Adding logging and timing to your workers turns them from black boxes into transparent, debuggable components.

Implementation

Here is how you add request timing and logging to your FastEdge worker:

 

What You Can Track

  • Request timing — how long each phase of processing takes, including routing, KV lookups, and HTTP calls.
  • Error rates — identify failing edge workers across specific PoPs.
  • Cache hit ratios — optimize your caching strategy based on real data.
  • Geographic distribution — understand where your traffic originates.
  • Cold start frequency — monitor how often workers are being initialized fresh.

Log Levels

The FastEdge SDK supports standard log levels: trace, debug, info, warn, and error. Use them appropriately to avoid noise while retaining the ability to debug production issues:

 

 Tip: Combine edge logging with structured logging at your origin for end-to-end request tracing. Include a unique request ID in edge logs that gets passed to upstream services through headers.

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