coroot/coroot

Features

• Observability Platforms - Provides an observability platform that automatically collects metrics, logs, and traces at the kernel level using eBPF.
• Automated Root Cause Analysis - Uses AI to investigate performance issues and automatically identify the primary source of system failures.
• eBPF-Based Collection - Captures kernel-level metrics, logs, and distributed traces automatically via eBPF without requiring manual code instrumentation.
• Performance and Resource Profilers - Collects continuous CPU and memory data across all processes to identify code causing performance spikes.
• Cloud Infrastructure Cost Optimization - Analyzes infrastructure spending to identify expensive resources and provide data for cloud cost optimization.
• Log Event Clustering - Clusters log events into recurring patterns and correlates them with traces to accelerate root cause identification.
• Multi-Cluster Telemetry Aggregation - Combines telemetry from separate orchestration environments into a unified view to monitor distributed applications.
• Operational Cost Monitoring - Tracks actual runtime infrastructure expenses to attribute cloud spending to specific applications.
• eBPF Interceptors - Gathers metrics, logs, and traces using eBPF to provide system visibility without manual code instrumentation.
• Anomaly Detection - Automatically highlights logs containing error or warning messages that require immediate operator attention.
• Cluster Health Monitoring - Provides a unified health view by collecting and correlating observability data across multiple distributed clusters and providers.
• eBPF-Based Tracing - Provides automatic distributed tracing at the kernel level using eBPF without requiring manual code instrumentation.
• General Resource Bottleneck Detection - Identifies resource shortages and performance issues across CPU, GPU, memory, and storage volumes.
• Log Analysis - Correlates raw log data with latency and CPU metrics to identify the primary causes of performance issues.
• Observability Platforms - Integrated platform that aggregates logs, metrics, and traces using eBPF for a unified view of system health.
• Service Uptime Monitors - Tracks availability and latency targets for applications to ensure they meet defined performance standards.
• Distributed Tracing - Correlates distributed request traces and logs to identify the root cause of performance regressions.
• Metric and Performance Monitors - Tracks health and performance metrics for specific language runtimes, including JVM and Python.
• Kubernetes Monitors - Tracks resource utilization, performance, and health of nodes, pods, and containers within Kubernetes clusters.
• Application Health Monitors - Tracks operational health and performance metrics of software applications using service level objectives.
• Automated Diagnostics - Runs predefined inspections to automatically detect and alert on application failures and performance regressions.
• Telemetry Collection and Aggregation - Combines telemetry from several clusters or regions into a single unified view of an application.
• Telemetry Collectors - Retrieves high-level orchestration data and configuration from the cluster environment for observability.
• Service Level Objective Management - Defines and monitors specific performance goals to ensure a service meets reliability and quality standards.
• Telemetry Correlation - Links distributed request paths by correlating eBPF data with OpenTelemetry spans to provide unified observability context.
• Model Context Protocol Gateways - Exposes system telemetry and health data to large language models through the Model Context Protocol.
• Columnar Storage Engines - Utilizes storage engines optimized for analytical workloads by organizing high-volume telemetry data by column.
• Metrics Storage Backends - Configures the telemetry backend to use either a time-series database or a columnar store for efficient retrieval.
• Baseline-Comparison Profiling - Identifies specific lines of code causing resource spikes by comparing current behavior against a system baseline.
• Release Performance Comparison - Monitors rollouts to automatically compare the performance of a new release against the previous version.
• Container Orchestration Deployments - Supports the deployment of monitoring components across container environments including Kubernetes and Docker.
• Deployment Monitoring - Discovers and monitors Kubernetes rollouts to identify performance or stability issues during updates.
• Hub-and-Spoke Agent Deployment - Implements a hub-and-spoke architecture where distributed node agents report observability data to a central server.
• Kubernetes Operators - Uses a Kubernetes operator to manage the lifecycle of components via custom resources and configuration updates.
• Connection Latency Analysis - Detects communication failures and latency issues between applications and their dependent services.
• Model Context Protocol Integrations - Exposes system health and observability data to large language models using the standardized Model Context Protocol.
• Database Health Monitors - Identifies availability and latency issues for common database instances like Postgres and MongoDB.
• Database Performance Monitors - Tracks requests between applications and database engines using eBPF to capture performance data.
• Instance Health Monitors - Identifies when application instances become unavailable or undergo unexpected restarts.
• Incident Management Systems - Aggregates ongoing and resolved issues into a centralized list to track the full lifecycle of system failures.
• OpenTelemetry Standard Integrations - Integrates with application code using OpenTelemetry standards to provide granular performance insights across multiple languages.
• System Health Dashboards - Provides automated dashboards with metrics and health summaries to quickly identify performance issues and bottlenecks.
• System Reliability Predictions - Analyzes system behavior to predict potential reliability failures and highlight areas for proactive mitigation.
• Telemetry Agents - Deploys lightweight agents across hosts to collect and forward local logs and metrics to a central server.
• Observability and Profiling - Open-source APM and observability tool for infrastructure.
• Monitoring and Metrics - APM and observability tool powered by eBPF.

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