[Feature]: End-to-End Tracing & Latency Visualization for Performance Optimization #159
Description
Describe your use case
Proposal: Support End-to-End Distributed Tracing and Latency Visualization in the White-Lebal Management Interface to Aid Performance Analysis and Optimization
Description
To enhance observability and performance tuning capabilities for the Memory Search and Memory Add workflows, we propose integrating end-to-end distributed tracing and latency visualization features into the existing white-screen management interface.
combination #29
Describe the solution you'd like
Proposed Implementation
-
Trigger Search or Add Operations Directly from the UI
Users can manually initiate a complete search or add workflow directly within the management interface, simulating real user requests. -
Auto-Generate a Unique Trace ID per Operation
Each triggered request is assigned a globally unique Trace ID, which is used to record the full execution trace across all system components. -
Visualize Granular Node-Level Latencies Using the Trace ID
The workflow is decomposed into key stages (e.g., query rewriting, vector retrieval, reranking, model inference, result formatting, etc.). For each stage, the system records start/end timestamps and duration, then visualizes the entire trace as a timeline—such as a Gantt chart—linked to the Trace ID.
Expected Benefits
- Precise Bottleneck Identification: Quickly pinpoint the highest-latency stage by inspecting the full trace of a single request.
- Improved User Experience Insights: Correlate perceived UI latency (e.g., white-screen wait time) with specific backend processing phases to understand where users experience delays.
- Enhanced System Observability: Use the Trace ID as a unified correlation key across logs, metrics, and alerts, enabling efficient issue reproduction and root cause analysis.
- Accelerated Development & Validation: Developers can instantly validate the real-world impact of performance optimizations on actual end-to-end request paths.
Future Extensions
Building upon this foundation, we can further support:
- Aggregated performance metrics
- Historical trend comparisons
- A/B testing capabilities for performance-sensitive changes
Describe alternatives you've considered
No response
Additional context
