Audit Logs Micro-Frontend
A searchable, filterable enterprise audit experience built as a reusable micro-frontend for compliance and operational visibility.
Context
Enterprise users and internal teams needed a clearer way to browse user and system activity. Event data existed, but the experience for finding, filtering, and understanding activity was not strong enough for compliance and operational workflows.
Problem
There was no dedicated frontend surface to inspect platform events with search, filters, pagination, and contextual detail views. Teams relied on fragmented tools and manual lookups.
Ownership
- Micro-frontend UI architecture and component boundaries
- Search, filter, and pagination state management
- API integration with audit events contract
- Loading, empty, and error state handling
- Reusable event card, filter, and detail panel components
- Testing with Jest and React Testing Library
Architecture
The audit experience is mounted as a micro-frontend inside the host application. Query state drives filter and pagination behavior, which calls the audit events API. Results render in timeline/table views with a detail panel for event context.
flowchart LR User[Admin User] --> Host[Host Application] Host --> MFE[Audit Logs MFE] MFE --> Query[Query State and Filters] Query --> API[Audit Events API] API --> Store[(Event Store)] MFE --> UI[Timeline / Table / Detail Panel] UI --> Export[Optional Export / Copy Link]
Key Technical Decisions
| Decision | Why | Trade-off | Result |
|---|---|---|---|
| Micro-frontend boundary | Allow independent ownership and deployment of audit surface | Requires dependency and version discipline across host/remotes | Safer modular delivery without blocking host releases |
| URL-synced filter state | Preserve search context across navigation and sharing | More complex state serialization | Shareable, bookmarkable audit views for teams |
| Explicit UI states | Audit data can be empty, loading, or error-prone | More component variants to maintain | Predictable UX under real enterprise data conditions |
Implementation
- Modeled filters as typed query parameters synced with URL state
- Built paginated data fetching with loading/error/empty boundaries
- Created reusable event card, filter chip, and detail drawer components
- Integrated with host app navigation without exposing private host internals
- Added unit tests for filter logic and component rendering
Results
- Improved visibility into platform activity
- Created a reusable audit interface pattern for enterprise workflows
- Faster access to event information for common operational queries
Reflection
- The hardest part was modeling filter state so it felt instant without over-fetching.
- Separating MFE boundaries early made the surface easier to test and embed.
- Next improvement: server-side filter optimization for very large event volumes.