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Shadow Pattern

Overview

The Shadow Pattern (also known as Shadow Deployment or Shadow Traffic) is a release strategy where a new version of a service runs in parallel with the current version — but does not handle real user responses.

Instead:

  • Live traffic is duplicated and sent to both the active version (production) and the shadow version.
  • The shadow version processes the requests silently — results are observed/logged, but not returned to the user.

This allows teams to:

  • Test real-world performance
  • Detect regressions or incompatibilities
  • Validate new logic, integrations, or data handling

All without affecting real users or production outcomes.

Conceptual Flow

  1. User sends a request to the production app.
  2. That request is cloned (mirrored).
  3. It’s sent to the shadow version of the app/service.
  4. The shadow service processes the request, but its response is ignored.
  5. Metrics, logs, errors, and performance from the shadow version are captured for analysis.

Advantages

  • Safe real-world testing: Use actual production traffic without impacting users.
  • Early detection of bugs or regressions in new versions.
  • Compare results between old and new logic/output.
  • No risk to live data if set up properly (read-only).

Drawbacks / Considerations

  • Cost overhead: You're running two versions in parallel.
  • Data integrity: The shadow version must not alter production data or side-effects.
  • Observability is essential: You must capture logs/metrics effectively.
  • Latency risk: Duplicating and processing traffic must not affect the main request path.

Common Use Cases

  • Testing new algorithms or ML models (e.g. recommendations, fraud detection)
  • Verifying API changes (e.g. new response formats)
  • Comparing performance of different service versions
  • Detecting edge-case failures under production load

Example in Azure

In Azure, you might use the Shadow Pattern by:

  • Duplicating traffic using Azure API Management or a custom proxy
  • Sending the cloned traffic to the shadow service (hosted in App Service, Container Apps, etc.)
  • Using Application Insights or Log Analytics to track behaviour
  • Prevent the shadow version from making mutating calls (e.g. no DB writes)

Summary

Key Aspect Description
Purpose Safely test a new service version using real production traffic
Risk Level Very low – responses are ignored
Key Benefit Insight into live behaviour without user impact
Key Challenge Proper isolation of side-effects and comprehensive logging

Example Analogy

Imagine you're training a new employee by letting them "shadow" a seasoned worker. They observe and even perform the same tasks, but their work doesn’t affect the outcome — it’s just to learn and validate their skills.