dianping/cat
Cat
Cat is a distributed application performance monitoring tool and tracing framework designed to track transactions, latency, and health across distributed services. It functions as a Kubernetes-native monitoring stack that utilizes multi-language monitoring clients and a real-time alerting system to maintain system visibility.
The system provides monitoring clients for Java, Go, Python, Node.js, and C++ to collect performance metrics and trace data. It distinguishes itself by sampling request flows to record call chains and identify bottlenecks, while using a monitoring engine to trigger immediate notifications when performance indicators breach defined thresholds.
The observability surface includes distributed trace analysis, application error logging, and web endpoint monitoring. It aggregates performance metrics and transaction data to generate statistical health reports and identify problematic requests through metadata capture and transaction tracking.
The project is packaged for containerized deployment and supports automated installation via Helm charts.
Features
- Application Performance Monitoring - Tracks runtime errors and performance bottlenecks in distributed software to analyze latency and resource usage.
- Distributed Tracing - Samples request flows across service chains to visualize call sequences and diagnose latency root causes.
- Service Health Monitoring - Tracks the start, end, and status of individual requests to monitor performance and identify service failures.
- Alerting Systems - Monitors performance thresholds and triggers immediate notifications via custom APIs when system health deviates.
- Application Performance Monitoring - Provides a comprehensive system for tracking transactions, latency, and health across distributed services.
- Distributed Monitoring Tools - Tracks the health and performance of microservices across multiple programming languages to identify bottlenecks.
- Application-Level Probes - Provides mechanisms for connecting monitoring probes to frameworks and logging libraries to track real-time service behavior.
- Multi-Language Instrumentation - Embeds monitoring probes and logging libraries into diverse codebases to unify event reporting and health tracking.
- Distributed Tracing and Execution Analysis - Samples request flow data to provide visibility into the call chain and latency of service interactions.
- Metric and Performance Monitors - Provides high-frequency collection and aggregation of numerical performance data and system health metrics.
- Cross-Language Performance Tracking - Analyzes real-time performance and health across distributed services using multi-language monitoring clients.
- Multi-Language Monitoring Clients - Provides monitoring clients for Java, Go, Python, Node.js, and C++ to collect performance metrics and trace data.
- Request Tracing - Records the lifecycle of a request using decorators or context managers to capture timing and status.
- Containerized Deployments - Packages the monitoring stack into container images to ensure consistent behavior across environments.
- Helm Chart Deployment - Supports automated installation into clusters using packaged Helm charts for component setup.
- Metric Aggregations - Combines metrics on the client side to prevent server overload while ensuring complete data transmission.
- Logging Frameworks - Integrates with standard logging libraries to unify event reporting and error tracking across distributed services.
- Alert Notification Systems - Monitors performance indicators and triggers immediate notifications when defined health thresholds are breached.
- Error Reporting - Captures application-level runtime failures, exceptions, and stack traces in production environments.
- Kubernetes Monitors - Functions as a Kubernetes-native monitoring stack to observe service behavior within a cluster.
- Performance Reporting - Aggregates raw monitoring data into statistical reports to analyze system trends and overall health.
- Request Traffic Monitors - Tracks incoming request volume, processing duration, and error rates specifically for URL-based web endpoints.
- Error-Based Request Preservation - Automatically flags messages with non-zero status codes to preserve full request trees for debugging.
- Request Metadata Attachment - Records categories, action names, and custom data fields for calls to facilitate root-cause analysis.
- Infrastructure and Monitoring - Real-time application and business monitoring platform.
- Monitoring & APM - Real-time application monitoring and performance analysis.
