igorkasyanchuk/rails_performance
Rails Performance
This project is a self-hosted application performance monitoring tool designed for Ruby on Rails environments. It functions as a diagnostic platform that tracks request response times, database query efficiency, and background job performance to help identify bottlenecks within web application infrastructure.
The tool distinguishes itself by integrating directly into the framework to provide real-time performance insights and developer-focused utilities, such as direct navigation from error reports to the corresponding lines in a code editor. It supports complex analysis by correlating performance metrics with deployment markers and custom events, allowing teams to observe how specific code releases or infrastructure changes impact system behavior.
The platform provides a comprehensive observability suite that includes system resource monitoring for CPU, memory, and disk usage, alongside request and activity filtering to focus on relevant operations. It features role-based dashboard access control to secure sensitive metrics and supports data management through configurable storage destinations and performance data exports. Additionally, the tool includes development workflow enhancements like live reloading to accelerate the testing cycle.
Features
- Application Performance Monitoring Platforms - Functions as a diagnostic platform for visualizing system resource usage and request throughput.
- Rails Query Profilers - Provides a self-hosted dashboard for tracking request latency and database query efficiency.
- Application Performance Monitoring - Tracks request response times and rendering bottlenecks to identify performance issues.
- Performance Visualization - Visualizes application performance data through interactive charts tracking resource usage and request duration.
- Ruby on Rails Integrations - Provides specialized performance monitoring for the Ruby on Rails framework.
- Error-to-Editor Navigators - Enables developers to jump directly from error reports to the problematic code in their editor.
- Application Observability - Secures performance monitoring data through integrated authentication and access control.
- Background Job Monitoring - Observes asynchronous task queues to record latency and throughput metrics.
- Resource Monitoring - Tracks CPU, memory, and disk usage across server instances to provide infrastructure health visibility.
- Deployment Event Markers - Overlays deployment markers on performance charts to correlate code releases with system behavior changes.
- Framework Instrumentation - Provides automated telemetry capture by subscribing to internal framework events.
- Metric Storage & Querying - Persists performance metrics in relational tables for historical trend analysis and querying.
- Background View Renderers - Improves perceived performance by rendering components in the background after the initial page response.
- Request Middleware - Intercepts web requests via middleware to measure execution time and resource consumption.
Star history
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