netdata/netdata

Features

• System Metrics Collection - The monitoring platform gathers system-level metrics using native, high-performance threads running directly within the daemon to ensure minimal overhead and zero external dependencies.
• Infrastructure Monitoring - The monitoring platform collects system metrics, logs, and hardware sensor data in real-time with per-second precision and low-latency visualization across diverse infrastructure environments.
• Infrastructure Metric Collectors - A modular data acquisition engine that gathers system, container, and service-level telemetry through native threads and isolated external processes.
• Distributed Metric Aggregators - Scaling monitoring across complex multi-cloud architectures by centralizing data from multiple nodes into a unified, hierarchical management structure.
• Distributed Observability Platforms - A scalable architecture that aggregates telemetry from multiple nodes into centralized dashboards for unified monitoring and cross-system performance analysis.
• Infrastructure Monitors - A high-frequency observability agent that collects, visualizes, and analyzes system and application metrics with per-second precision across distributed environments.
• Performance Monitoring Tools - The monitoring platform tracks infrastructure performance using pre-built dashboards, intelligent alarms, and metric correlations to identify anomalies and reduce resolution time across systems.
• Real-Time Infrastructure Observability - Monitoring system health and hardware performance with high-precision, per-second data collection to identify bottlenecks across diverse computing environments.
• Cloud-Connected Management Planes - A centralized interface that bridges local monitoring agents with remote dashboards for unified alerting, log aggregation, and infrastructure orchestration.
• Edge Anomaly Detection - Processes historical metric streams through local machine learning models to identify performance deviations without requiring external query engines.
• Metric Streaming - The monitoring platform allows configuration of streaming behavior by defining roles, API keys, and connection parameters within a centralized configuration file for metric centralization.
• Monitoring Dashboards - The monitoring platform provides a local web-based dashboard to visualize real-time system metrics and performance data collected by the agent.
• Automated Root Cause Analysis - Diagnosing root causes of infrastructure issues using natural language queries and automated correlation tools to reduce mean time to resolution.
• Streaming Diagnostics - The monitoring platform provides diagnostic utilities to identify streaming connectivity issues by inspecting system logs for specific connection events on parent and child nodes.
• Anomaly Detection Systems - The monitoring platform detects anomalies using edge-based machine learning models that train on historical metric behavior without requiring manual query language configuration.
• Cloud Monitoring Dashboards - The monitoring platform connects local agents to a centralized cloud dashboard to enable unified metrics, log viewing, and cloud-based alert notifications across multiple systems.
• Agent Deployment Strategies - The monitoring platform supports diverse deployment methods including package managers, automated scripts, source compilation, and containerized orchestration for cluster-wide metric collection.
• Containerized Observability - Deploying and managing observability agents within containerized environments to track service health, pod performance, and infrastructure-wide metrics automatically.
• Automated Update Mechanisms - The monitoring platform provides automated update mechanisms and scripts to refresh deployments, apply configuration changes, and maintain the latest software versions across all nodes.
• Service Discovery - The monitoring platform includes automated service discovery to detect running containers and endpoints, enabling metric collection for services using non-default ports or custom naming conventions.
• Observability Data Isolation - The monitoring platform ensures data security by separating observability data from metadata, keeping system metrics local while routing only essential metadata securely to the cloud.
• Metadata-Only Synchronization - Routes only essential system metadata to centralized dashboards while keeping granular observability data local to maintain data privacy.
• Thread-Per-Core Architectures - Executes high-frequency system metric gathering within dedicated, low-latency threads to minimize CPU overhead and context switching.
• Application Metrics Collection - The monitoring platform gathers application and service metrics using modular, independent processes that communicate with the daemon via pipes to support multiple programming languages.
• Local-First Persistence - Stores high-resolution telemetry data directly on the host filesystem to ensure continuous monitoring availability during network partitions.
• Hierarchical Metric Aggregation - Establishes hierarchical node relationships to aggregate, centralize, and forward observability data across distributed infrastructure environments.
• Hierarchical Scaling - The monitoring platform scales horizontally by establishing parent-child relationships between agents to centralize data collection, retention, and alerting across complex multi-cloud environments.