firehol/netdata

Features

• High-Resolution Sampling - Collects and processes system performance data at one-second intervals for real-time infrastructure visibility.
• Real-time Performance Monitoring - Collects and visualizes high-resolution system performance metrics every second to identify bottlenecks and health issues.
• Edge-Based Anomaly Detection - Implements unsupervised machine learning models locally on each node to identify abnormal metric patterns.
• Time Series Data Storage - Provides an efficient time-series database optimized for rapid retrieval and analysis of collected metrics.
• Time Series Databases - Implements a high-efficiency time-series database for archiving and retrieving high-resolution telemetry data.
• Auto-Discovery Mechanisms - Automatically detects available system resources and running applications to start monitoring without manual setup.
• Zero-Configuration Deployments - Provides rapid infrastructure tracking that activates immediately upon installation without complex configuration.
• Alert Thresholds - Evaluates streaming telemetry against predefined numerical limits and rules to trigger system notifications.
• Anomaly Detection - Uses machine learning and threshold alerts to automatically identify unusual patterns and performance deviations.
• Centralized Metric Streaming - Streams high-resolution metrics and alerts from child nodes to a parent node for unified visualization.
• Centralized Monitoring Platforms - Streams metrics and alerts from multiple child nodes to a parent node for unified observability.
• Container Health Monitors - Provides real-time resource usage and status monitoring for containers and virtualized processes.
• Full-Stack Metric Aggregators - Functions as a log and metric aggregator collecting journals, event logs, and hardware sensors for full-stack visibility.
• Infrastructure Anomaly Detectors - Employs an edge-based analytics engine with unsupervised models to identify unusual patterns in infrastructure behavior.
• System Metrics Collection - Gathers native system-level performance metrics from servers, cloud environments, and IoT devices.
• Edge Anomaly Detection - Processes metric streams locally at the edge using machine learning to identify performance deviations.
• Telemetry Collection and Aggregation - Implements the transport and unification of high-resolution telemetry across distributed infrastructure environments.
• Distributed Observability Platforms - Aggregates performance data and alerts from multiple distributed servers into a unified centralized dashboard.
• Rule-Based Alerting Engines - Evaluates streaming telemetry against predefined thresholds and scoring rules to trigger notifications.
• Parent-Child Data Streaming - Employs a parent-child data streaming architecture to aggregate metrics and alerts from multiple nodes.
• Per-Second Metric Aggregation - Collects and processes system performance data at one-second intervals for real-time visibility.
• Threshold-Based Alerters - Triggers real-time notifications when infrastructure metrics cross predefined numerical thresholds.
• System Resource Monitors - Tracks live system-level resource consumption for CPUs, memory, storage, and network sockets.
• Anomaly Scoring - Quantifies outlierness using machine learning to provide numerical anomaly scores for infrastructure behavior.
• Observability Scaling - Employs a parent-child architecture to centralize and scale data collection from multiple nodes.
• Log-Metric Correlation - Correlates system logs with performance telemetry to troubleshoot application errors and infrastructure incidents.
• Long-Term Statistics Storage - Archives high-resolution data using tiered storage and high-compression formats to maintain historical records.
• Tiered Storage Strategies - Uses high-compression formats and tiered retention levels to balance rapid retrieval with long-term archiving.
• Hardware Sensor APIs - Monitors physical server components including temperatures, fan speeds, and voltage to prevent hardware failure.
• Observability Pipelines - Organizes telemetry data using parent-child streaming architectures with configurable replication and retention.
• Multi-Node Management - Aggregates performance data from distributed nodes into a single interface with role-based access control.
• Alerting Workflow Configurations - Ships pre-configured alerting workflows and rules to detect system issues and notify administrators.
• Predefined Application Monitors - Collects predefined performance metrics from common packaged software such as web servers and databases.
• Cloud Resource Monitoring - Collects telemetry and health metrics for resources hosted on major cloud provider platforms.
• Alert Configuration Management - Provides a centralized interface for managing alert rules and definitions across multiple monitored nodes.
• Resource Usage Inspections - Tracks live CPU, memory, and network statistics for individual system and application processes.
• Log Analysis - Parses and interprets system journals and event logs to identify patterns in system activity.
• External Application Integrations - Allows the ingestion of telemetry from external applications using open industry standards.
• Alert Management Systems - Includes a management system to route, group, and inhibit alerts from multiple nodes to streamline incident response.
• Multi-Channel Alerting - Delivers system health warnings to external messaging services when predefined thresholds are met.
• Performance Dashboards - Offers interactive visual tools for exploring and analyzing system resource activity in real-time.
• Performance Metrics APIs - Provides a programmatic interface for retrieving raw and processed system and container performance statistics.
• Model-Driven Generation - Automatically generates visualizations based on a structured data model without requiring a manual query language.
• Infrastructure Health Monitoring - Provides real-time observation of server hardware and system resource stability through a user interface.
• Synthetic Monitoring - Proactively verifies service reachability by testing API availability, port status, and SSL certificates.
• Data Analytics - Real-time performance and health monitoring for distributed systems.
• Infrastructure Monitoring - Real-time performance and health monitoring for distributed systems.

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