Let the business message continue through its normal route.
System Management
Message History
Attach a record of the components a message has traversed so teams can reconstruct its path and understand where transformations or failures occurred.
How can we effectively analyze and debug the flow of messages in a loosely coupled system?Adapted from Enterprise Integration Patterns under CC BY 3.0. The visualization and explanatory content on this page are original GateSift material.
Original GateSift visualization
How Message History works
The normal flow continues while an additional operational or diagnostic view is produced.
Live message flow
Message History
Primary destination
Operational insight
Apply Message History without changing the intended business destination.
Capture trace, history, control or diagnostic information for operators.
What this pattern helps you decide
Attach a record of the components a message has traversed so teams can reconstruct its path and understand where transformations or failures occurred.
What happens when processing fails or the same message is delivered twice?
Where does state, correlation or routing configuration live?
How will operators trace the message and understand the decision path?
Where you may see it
- Diagnostic baggage or trace state
- Application Insights distributed tracing
- BizTalk tracking data
How the analyzers can surface it
- Correlation and logging coverage
- Execution flow documentation
Pattern detection is contextual. GateSift should present these as architectural signals, not claim a pattern is implemented solely because one policy statement or adapter exists.
Source, licence and attribution
The pattern name and selected problem statement are adapted from Enterprise Integration Patterns by Gregor Hohpe and Bobby Woolf under CC BY 3.0. GateSift summaries, Azure mappings, analyzer guidance and diagrams are original. No endorsement by the original authors is implied.