openobserve/openobserve

Features

• Columnar Storage Engines - Stores telemetry data in highly compressed, column-oriented formats to enable rapid analytical queries and efficient long-term storage.
• Observability Data Platforms - Aggregates metrics from diverse sources like remote-write endpoints and standard API integrations to create a single source of truth.
• Log Management Systems - Provides a high-performance engine for indexing, searching, and visualizing massive volumes of log data.
• Monitoring Tools - Provides a scalable observability solution that supports multi-cluster environments and object storage integration.
• Monitoring and Observability - Collects and correlates logs, metrics, and traces into a single platform for comprehensive system visibility.
• AI and Agent Observability - The platform tracks telemetry from language model pipelines and AI agents to analyze execution traces, token usage, and operational costs.
• Observability Platforms - Acts as a unified platform for ingesting, storing, and analyzing logs, metrics, and traces.
• AI Data Query Interfaces - The platform interprets natural language requests to generate queries and provide intelligent insights across logs, metrics, and traces.
• Telemetry Data Pipelines - Ingests, transforms, and routes telemetry data streams from cloud services and standard collectors.
• Telemetry Collectors - Routes, transforms, and enriches logs, metrics, and traces from diverse sources into a centralized storage backend.
• Multi-tenant Isolation Policies - Enforces logical boundaries between organizational data and user access to ensure secure, independent workspaces within a shared infrastructure.
• Automated Root Cause Analysis - The platform uses natural language processing to query data, generate dashboards, summarize anomalies, and identify root causes for incidents automatically.
• Distributed Tracing Systems - Captures and correlates request spans across microservices to identify latency bottlenecks and system errors.
• Distributed Tracing - Tracks request flows across microservices and language model pipelines to identify latency bottlenecks.
• Telemetry Correlation - The platform links logs, metrics, and traces using shared identifiers to allow seamless navigation from high-level alerts to specific root causes.
• AI-Powered Data Assistants - The platform connects AI agents to observability data via standardized protocols, enabling automated debugging and natural language querying.
• Stream Processing - Transforms, filters, and enriches telemetry data in real-time during ingestion before persisting it to the underlying storage layer.
• Prompt-Based Dashboards - Constructs visual dashboards and monitoring panels based on natural language descriptions of desired metrics, error rates, or latency views.
• Time-Series SQL Querying - Translates standard analytical query languages into optimized operations across distributed datasets for unified log, metric, and trace analysis.
• Object Storage Integration - Utilizes cloud-native object storage buckets as the primary data repository to ensure durability, scalability, and low-cost retention.
• Stateless Services - Runs as a lightweight, stateless service that eliminates the need for complex indexers or search heads.
• Multi-Tenant Isolation Layers - Enforces strict multi-tenant boundaries to separate data, users, dashboards, and alerts between different teams or customers.
• Metric Dashboards - Provides custom dashboards and standard querying for infrastructure and application metrics.
• Infrastructure Monitoring - Tracks resource utilization and system health across clusters to enable proactive alerting and capacity planning.
• Model Provider Integrations - The platform connects custom or third-party language model providers via API keys to maintain control over security, governance, and infrastructure costs.
• Observability and Monitoring - High-performance, cost-efficient alternative to Elasticsearch and Splunk.
• Observability and Monitoring - High-performance, cost-efficient alternative to Elasticsearch and Splunk.
• Stream-Oriented Data Pipelines - Parses, filters, and enriches incoming data in real-time to convert raw logs into structured insights.
• Distributed Query Engines - Distributes search requests across multiple nodes to aggregate results from large datasets for faster retrieval.
• Schema-Agnostic Ingestion - Parses and indexes incoming telemetry fields dynamically to support flexible searching without requiring rigid, pre-defined data schemas.
• Cluster Scaling Orchestrators - Distributes workloads across specialized nodes for routing, ingestion, and querying to support high-traffic environments.
• High Availability Systems - Supports deployment across multiple nodes to ensure continuous data collection and system reliability.
• Data Access Governance - Enforces role-based access control, data redaction, and audit logging to maintain compliance across operational data.
• Enterprise SSO Authentication - Connects to external identity providers using standard protocols to manage user access and permissions.
• Distributed Tracing Instrumentation - Instruments applications automatically to record request flows and performance data without requiring manual code changes.
• Incident Management Systems - Detects incidents automatically and provides root-cause correlation and remediation suggestions using artificial intelligence.
• Kubernetes Monitors - The platform tracks CPU, memory, storage, and network utilization across nodes, pods, and containers to optimize resource allocation and capacity planning.
• Performance Trend Analysis - Identifies latency bottlenecks and error trends using heatmaps and flame graphs.
• System Audit Trails - Records all system changes and access events into immutable logs to maintain a verifiable trail.
• Telemetry Ingestion - Gathers logs, metrics, and traces from cloud services using native export sinks or standard collectors.
• Stateless Ingestion Pipelines - Processes incoming telemetry streams across independent, horizontally scalable nodes to eliminate bottlenecks and ensure high availability.
• Deployment Regression Detectors - The platform evaluates log behavior across different time windows to detect regressions, new error signatures, or traffic shifts following a software release.
• Data Lifecycle Management - Manages the lifecycle, indexing, and physical storage of logs, metrics, and traces.
• Data Retention Managers - Automates data retention, residency, and erasure processes to meet regulatory requirements.
• Data Normalization - Standardizes incoming data from diverse frameworks into a unified format for consistent analysis.
• User Behavior Analysis - Records user navigation paths and session replays to visualize workflows and identify friction points.
• Self-Hosted Deployment Platforms - Supports flexible hosting options including managed cloud services or self-hosted infrastructure.
• Zero Trust Access Controls - Verifies every user and device request continuously while applying context-aware policies to prevent unauthorized network access.
• AI Cost Monitoring - Tracks token consumption and associated costs for language model requests to optimize operational expenses.
• Alert Routing - Routes alerts to external destinations while applying suppression rules and aggregation windows.
• Alert Management Systems - Groups related alerts and signals into single incidents automatically to reduce noise.
• Service Dependency Mapping - Generates real-time maps showing how microservices connect and interact to reveal the architecture of distributed systems.
• Trend Analysis - Triggers notifications based on custom thresholds and trend analysis to provide context for performance issues.
• Data Storage - Maintains data sovereignty by storing telemetry in open file formats on user-managed cloud storage.
• Data Enrichment - Augments raw events with external metadata from lookup tables or real-time API calls.
• Data Filtering - Directs data streams to specific destinations based on field values and business rules.
• Database Performance Monitoring - The platform collects and visualizes metrics, query response times, and resource utilization across diverse database systems in a unified dashboard.
• Platform Script Execution - Applies user-defined transformations using a scripting language for complex data manipulation.
• Containerized Deployments - Provides pre-built images for deployment in containerized environments to simplify installation.
• Capacity Planning - The platform uses historical usage trends and workload patterns to predict future resource needs and inform infrastructure scaling decisions.
• Storage Configurations - Directs telemetry data to user-managed storage buckets to maintain control over residency and costs.
• Telemetry Data Pipelines - Adjusts telemetry collection behavior using environment variables, dynamic updates, and custom exporter definitions.
• Automatic Redaction - Masks or removes sensitive information from incoming observability data streams to ensure compliance and data privacy.
• Source Map Deobfuscators - The platform transforms minified client-side stack traces into readable code by applying source maps, revealing original filenames, line numbers, and function names.
• Frontend and Backend Observability - Logs client-side issues with full stack traces, browser context, and preceding user actions to facilitate debugging.
• Incident Management - Creates comprehensive documentation for resolved incidents including root causes, timelines, and evidence.
• OpenTelemetry Exporters - Routes data from existing instrumentation to a central endpoint by pointing standard collectors to the destination.

Star history