JohnBasrai/argus-events

AI Summary: The task involves replacing `unwrap()` calls with more robust error handling in the integration tests of a Rust-based event tracking service. This includes addressing potential panics caused by unexpected failures in network operations, environment variable retrieval, and JSON parsing. The goal is to improve the reliability and maintainability of the test suite.

AI Summary: Implement a PostgreSQL database persistence layer for the Argus Events project, replacing the existing in-memory storage. This involves creating a new PostgreSQL repository implementation that adheres to the existing `EventRepository` trait, implementing database migrations, connection pooling, ensuring API compatibility, and benchmarking performance against the in-memory solution.

AI Summary: Implement pagination for the `/events` endpoint, adding `limit` and `offset` query parameters to control the number and starting point of returned events. The response should include metadata indicating the total number of events and whether more events exist beyond the current page. Default limits should be set to prevent abuse, and integration tests must be updated to cover the new functionality.

AI Summary: Implement application-level rate limiting in the existing Rust/Axum event tracking service using a token bucket or sliding window algorithm. The solution should be configurable per IP/endpoint, include rate limit headers in responses, and integrate with the existing Prometheus metrics system. This replaces the current nginx-based rate limiting.

AI Summary: Implement JWT and API key authentication/authorization middleware for an existing Rust/Axum event tracking service, including role-based access control and updated security documentation. This requires integrating authentication mechanisms into the existing API endpoints and potentially modifying the service layer to handle authorization checks based on user roles.

AI Summary: Enhance the existing Rust/Axum event tracking service's logging to output structured JSON logs including correlation IDs and configurable log levels. A performance impact assessment is also required.

AI Summary: Integrate OpenTelemetry distributed tracing into the existing Rust/Axum event tracking service. This involves adding OpenTelemetry instrumentation to track requests across multiple services, ensuring trace context propagation, and configuring compatibility with a tracing backend like Jaeger or Zipkin. Performance benchmarks should be conducted to assess the impact of the integration.

AI Summary: Implement JSON schema validation for event payloads in the Argus Events Rust/Axum project. This involves adding support for configurable schema rules, generating informative error messages upon validation failure, and conducting performance tests to assess the impact of the added validation.

AI Summary: Implement a WebSocket endpoint for real-time event streaming, including filtering capabilities, connection management, and rate limiting. This will enhance the existing event tracking service to support live dashboards and monitoring applications.

AI Summary: Create a Kubernetes Helm chart for deploying the Argus Events application, including configurable settings, health checks, resource limits, and Horizontal Pod Autoscaler support.

AI Summary: Implement a performance benchmarking suite for the Argus Events project, including load testing with varying event sizes and concurrency, memory profiling, and CI integration for regression detection.