# openobserve/openobserve

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17,937 stars · 733 forks · TypeScript · agpl-3.0

## Links

- GitHub: https://github.com/openobserve/openobserve
- Homepage: https://openobserve.ai
- awesome-repositories: https://awesome-repositories.com/repository/openobserve-openobserve.md

## Topics

`analytics` `apm` `datadog` `elasticsearch` `grafana` `jaeger` `kibana` `log-analytics` `log-management` `log-search` `logs` `metrics` `monitoring` `observability` `openobserve` `opentelemetry` `prometheus` `rust-lang` `splunk` `traces`

## Description

OpenObserve is a unified observability data platform designed to ingest, store, and analyze logs, metrics, and traces. It functions as a cloud-native monitoring tool that centralizes telemetry from diverse sources, including standard collectors and cloud service providers, into a single, scalable system. By utilizing a columnar storage engine backed by object storage, the platform enables efficient long-term data retention and high-performance analytical querying.

The platform distinguishes itself through deep integration with artificial intelligence, allowing users to query data using natural language, generate dashboards via prompts, and automate incident analysis. It provides specialized monitoring for language model pipelines, including token usage cost analysis and performance tracking for AI agents. Furthermore, the system enforces strict multi-tenant resource isolation and zero-trust access, ensuring that organizational data remains secure and independent within shared infrastructure.

Beyond its core storage and AI capabilities, the platform includes a comprehensive suite of tools for incident management, infrastructure monitoring, and data pipeline orchestration. It supports real-time stream processing, schema-agnostic indexing, and automated data enrichment, allowing for flexible telemetry management without rigid pre-defined structures. The system also provides advanced diagnostic features such as production error deobfuscation, service dependency mapping, and user journey analysis to accelerate root cause investigation.

The software is designed for flexible deployment, running as a stateless, containerized service that supports high availability and horizontal scaling. It is distributed as a single binary or container image, with configuration managed through infrastructure-as-code templates.

## Tags

### Data & Databases

- [Columnar Storage Engines](https://awesome-repositories.com/f/data-databases/columnar-storage-engines.md) — Stores telemetry data in highly compressed, column-oriented formats to enable rapid analytical queries and efficient long-term storage. ([source](https://openobserve.ai/metrics/))
- [Observability Data Platforms](https://awesome-repositories.com/f/data-databases/data-collections-datasets/observability-data-platforms.md) — Aggregates metrics from diverse sources like remote-write endpoints and standard API integrations to create a single source of truth. ([source](https://openobserve.ai/metrics/))
- [Log Management Systems](https://awesome-repositories.com/f/data-databases/log-management-systems.md) — Provides a high-performance engine for indexing, searching, and visualizing massive volumes of log data.
- [Telemetry Data Pipelines](https://awesome-repositories.com/f/data-databases/telemetry-data-pipelines.md) — Ingests, transforms, and routes telemetry data streams from cloud services and standard collectors.
- [Stream Processing](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/stream-processing-systems/stream-processing.md) — Transforms, filters, and enriches telemetry data in real-time during ingestion before persisting it to the underlying storage layer.
- [Prompt-Based Dashboards](https://awesome-repositories.com/f/data-databases/data-visualization-dashboards/prompt-based-dashboards.md) — Constructs visual dashboards and monitoring panels based on natural language descriptions of desired metrics, error rates, or latency views. ([source](https://openobserve.ai/ai-assistant/))
- [Time-Series SQL Querying](https://awesome-repositories.com/f/data-databases/time-series-sql-querying.md) — Translates standard analytical query languages into optimized operations across distributed datasets for unified log, metric, and trace analysis.
- [Stream-Oriented Data Pipelines](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/stream-processing-systems/data-streaming/stream-oriented-data-pipelines.md) — Parses, filters, and enriches incoming data in real-time to convert raw logs into structured insights. ([source](https://openobserve.ai/pipelines/))
- [Distributed Query Engines](https://awesome-repositories.com/f/data-databases/distributed-query-engines.md) — Distributes search requests across multiple nodes to aggregate results from large datasets for faster retrieval. ([source](https://openobserve.ai/docs/architecture/))
- [Schema-Agnostic Ingestion](https://awesome-repositories.com/f/data-databases/schema-agnostic-ingestion.md) — Parses and indexes incoming telemetry fields dynamically to support flexible searching without requiring rigid, pre-defined data schemas.
- [Deployment Regression Detectors](https://awesome-repositories.com/f/data-databases/change-detection-engines/deployment-regression-detectors.md) — The platform evaluates log behavior across different time windows to detect regressions, new error signatures, or traffic shifts following a software release. ([source](https://openobserve.ai/ai-assistant/))
- [Data Lifecycle Management](https://awesome-repositories.com/f/data-databases/data-governance-modeling/data-management-governance/data-lifecycle-retention/data-lifecycle-management.md) — Manages the lifecycle, indexing, and physical storage of logs, metrics, and traces. ([source](https://openobserve.ai/docs))
- [Data Retention Managers](https://awesome-repositories.com/f/data-databases/data-governance-modeling/data-management-governance/data-lifecycle-retention/data-lifecycle-management/data-retention-managers.md) — Automates data retention, residency, and erasure processes to meet regulatory requirements. ([source](https://openobserve.ai/security-compliance/))
- [Data Normalization](https://awesome-repositories.com/f/data-databases/data-normalization.md) — Standardizes incoming data from diverse frameworks into a unified format for consistent analysis. ([source](https://openobserve.ai/llm-observability/))
- [User Behavior Analysis](https://awesome-repositories.com/f/data-databases/user-behavior-analysis.md) — Records user navigation paths and session replays to visualize workflows and identify friction points. ([source](https://openobserve.ai/frontend-monitoring/))
- [Data Storage](https://awesome-repositories.com/f/data-databases/data-engineering-infrastructure/data-persistence-storage/data-storage.md) — Maintains data sovereignty by storing telemetry in open file formats on user-managed cloud storage. ([source](https://openobserve.ai/newrelic-alternative/))
- [Data Enrichment](https://awesome-repositories.com/f/data-databases/data-enrichment.md) — Augments raw events with external metadata from lookup tables or real-time API calls. ([source](https://openobserve.ai/pipelines/))
- [Data Filtering](https://awesome-repositories.com/f/data-databases/data-filtering.md) — Directs data streams to specific destinations based on field values and business rules. ([source](https://openobserve.ai/pipelines/))
- [Database Performance Monitoring](https://awesome-repositories.com/f/data-databases/database-performance-monitoring.md) — The platform collects and visualizes metrics, query response times, and resource utilization across diverse database systems in a unified dashboard. ([source](https://openobserve.ai/database-monitoring/))

### DevOps & Infrastructure

- [Monitoring Tools](https://awesome-repositories.com/f/devops-infrastructure/cloud-native-infrastructure/monitoring-tools.md) — Provides a scalable observability solution that supports multi-cluster environments and object storage integration.
- [Telemetry Collectors](https://awesome-repositories.com/f/devops-infrastructure/telemetry-collectors.md) — Routes, transforms, and enriches logs, metrics, and traces from diverse sources into a centralized storage backend.
- [Object Storage Integration](https://awesome-repositories.com/f/devops-infrastructure/object-storage-integration.md) — Utilizes cloud-native object storage buckets as the primary data repository to ensure durability, scalability, and low-cost retention.
- [Stateless Services](https://awesome-repositories.com/f/devops-infrastructure/stateless-services.md) — Runs as a lightweight, stateless service that eliminates the need for complex indexers or search heads. ([source](https://openobserve.ai/splunk-alternative/))
- [Cluster Scaling Orchestrators](https://awesome-repositories.com/f/devops-infrastructure/cluster-scaling-orchestrators.md) — Distributes workloads across specialized nodes for routing, ingestion, and querying to support high-traffic environments. ([source](https://openobserve.ai/docs/architecture/))
- [High Availability Systems](https://awesome-repositories.com/f/devops-infrastructure/high-availability-systems.md) — Supports deployment across multiple nodes to ensure continuous data collection and system reliability. ([source](https://openobserve.ai/docs))
- [Self-Hosted Deployment Platforms](https://awesome-repositories.com/f/devops-infrastructure/self-hosted-deployment-platforms.md) — Supports flexible hosting options including managed cloud services or self-hosted infrastructure. ([source](https://openobserve.ai/pricing))
- [Containerized Deployments](https://awesome-repositories.com/f/devops-infrastructure/containerized-deployments.md) — Provides pre-built images for deployment in containerized environments to simplify installation. ([source](https://openobserve.ai/downloads/))
- [Capacity Planning](https://awesome-repositories.com/f/devops-infrastructure/devops/operational-reliability/capacity-planning.md) — The platform uses historical usage trends and workload patterns to predict future resource needs and inform infrastructure scaling decisions. ([source](https://openobserve.ai/database-monitoring/))
- [Storage Configurations](https://awesome-repositories.com/f/devops-infrastructure/storage-configurations.md) — Directs telemetry data to user-managed storage buckets to maintain control over residency and costs. ([source](https://openobserve.ai/blog/))
- [Telemetry Data Pipelines](https://awesome-repositories.com/f/devops-infrastructure/telemetry-data-pipelines.md) — Adjusts telemetry collection behavior using environment variables, dynamic updates, and custom exporter definitions. ([source](https://openobserve.ai/docs))

### System Administration & Monitoring

- [Monitoring and Observability](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability.md) — Collects and correlates logs, metrics, and traces into a single platform for comprehensive system visibility.
- [AI and Agent Observability](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/ai-agent-observability.md) — The platform tracks telemetry from language model pipelines and AI agents to analyze execution traces, token usage, and operational costs. ([source](https://openobserve.ai/blog/))
- [Observability Platforms](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms.md) — Acts as a unified platform for ingesting, storing, and analyzing logs, metrics, and traces.
- [Automated Root Cause Analysis](https://awesome-repositories.com/f/system-administration-monitoring/diagnostic-tools/diagnostics/failure-analysis-tools/automated-root-cause-analysis.md) — The platform uses natural language processing to query data, generate dashboards, summarize anomalies, and identify root causes for incidents automatically. ([source](https://openobserve.ai/))
- [Distributed Tracing Systems](https://awesome-repositories.com/f/system-administration-monitoring/distributed-tracing-systems.md) — Captures and correlates request spans across microservices to identify latency bottlenecks and system errors.
- [Distributed Tracing](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/distributed-tracing-execution-analysis/distributed-tracing.md) — Tracks request flows across microservices and language model pipelines to identify latency bottlenecks.
- [Telemetry Correlation](https://awesome-repositories.com/f/system-administration-monitoring/telemetry-correlation.md) — The platform links logs, metrics, and traces using shared identifiers to allow seamless navigation from high-level alerts to specific root causes. ([source](https://openobserve.ai/blog/))
- [Metric Dashboards](https://awesome-repositories.com/f/system-administration-monitoring/metric-dashboards.md) — Provides custom dashboards and standard querying for infrastructure and application metrics. ([source](https://cdn.jsdelivr.net/gh/openobserve/openobserve@main/README.md))
- [Infrastructure Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/infrastructure-monitoring.md) — Tracks resource utilization and system health across clusters to enable proactive alerting and capacity planning.
- [Distributed Tracing Instrumentation](https://awesome-repositories.com/f/system-administration-monitoring/distributed-tracing-instrumentation.md) — Instruments applications automatically to record request flows and performance data without requiring manual code changes. ([source](https://openobserve.ai/kubernetes-monitoring/))
- [Incident Management Systems](https://awesome-repositories.com/f/system-administration-monitoring/incident-management-systems.md) — Detects incidents automatically and provides root-cause correlation and remediation suggestions using artificial intelligence. ([source](https://openobserve.ai/pricing/))
- [Kubernetes Monitors](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/infrastructure-monitoring/kubernetes-monitors.md) — The platform tracks CPU, memory, storage, and network utilization across nodes, pods, and containers to optimize resource allocation and capacity planning. ([source](https://openobserve.ai/kubernetes-monitoring/))
- [Performance Trend Analysis](https://awesome-repositories.com/f/system-administration-monitoring/performance-trend-analysis.md) — Identifies latency bottlenecks and error trends using heatmaps and flame graphs. ([source](https://openobserve.ai/traces/))
- [System Audit Trails](https://awesome-repositories.com/f/system-administration-monitoring/system-audit-trails.md) — Records all system changes and access events into immutable logs to maintain a verifiable trail. ([source](https://openobserve.ai/security-compliance/))
- [Telemetry Ingestion](https://awesome-repositories.com/f/system-administration-monitoring/telemetry-ingestion.md) — Gathers logs, metrics, and traces from cloud services using native export sinks or standard collectors. ([source](https://openobserve.ai/gcp-monitoring/))
- [Stateless Ingestion Pipelines](https://awesome-repositories.com/f/system-administration-monitoring/telemetry-ingestion/stateless-ingestion-pipelines.md) — Processes incoming telemetry streams across independent, horizontally scalable nodes to eliminate bottlenecks and ensure high availability.
- [AI Cost Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/ai-cost-monitoring.md) — Tracks token consumption and associated costs for language model requests to optimize operational expenses. ([source](https://openobserve.ai/llm-observability/))
- [Alert Routing](https://awesome-repositories.com/f/system-administration-monitoring/alert-routing.md) — Routes alerts to external destinations while applying suppression rules and aggregation windows. ([source](https://openobserve.ai/alerts/))
- [Alert Management Systems](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/operational-health-alerting/alert-management-systems.md) — Groups related alerts and signals into single incidents automatically to reduce noise. ([source](https://openobserve.ai/incidents/))
- [Service Dependency Mapping](https://awesome-repositories.com/f/system-administration-monitoring/service-dependency-mapping.md) — Generates real-time maps showing how microservices connect and interact to reveal the architecture of distributed systems. ([source](https://openobserve.ai/traces/))
- [Trend Analysis](https://awesome-repositories.com/f/system-administration-monitoring/trend-analysis.md) — Triggers notifications based on custom thresholds and trend analysis to provide context for performance issues. ([source](https://openobserve.ai/metrics/))
- [Source Map Deobfuscators](https://awesome-repositories.com/f/system-administration-monitoring/error-tracking/source-map-deobfuscators.md) — The platform transforms minified client-side stack traces into readable code by applying source maps, revealing original filenames, line numbers, and function names. ([source](https://openobserve.ai/blog/))
- [Frontend and Backend Observability](https://awesome-repositories.com/f/system-administration-monitoring/frontend-and-backend-observability.md) — Logs client-side issues with full stack traces, browser context, and preceding user actions to facilitate debugging. ([source](https://openobserve.ai/frontend-monitoring/))
- [Incident Management](https://awesome-repositories.com/f/system-administration-monitoring/incident-management.md) — Creates comprehensive documentation for resolved incidents including root causes, timelines, and evidence. ([source](https://openobserve.ai/incidents/))
- [OpenTelemetry Exporters](https://awesome-repositories.com/f/system-administration-monitoring/opentelemetry-exporters.md) — Routes data from existing instrumentation to a central endpoint by pointing standard collectors to the destination. ([source](https://openobserve.ai/datadog-alternative/))

### Artificial Intelligence & ML

- [AI Data Query Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-data-query-interfaces.md) — The platform interprets natural language requests to generate queries and provide intelligent insights across logs, metrics, and traces. ([source](https://openobserve.ai/pricing))
- [AI-Powered Data Assistants](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-powered-data-assistants.md) — The platform connects AI agents to observability data via standardized protocols, enabling automated debugging and natural language querying. ([source](https://openobserve.ai/blog/))
- [Model Provider Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/ai-model-orchestration/model-provider-integrations.md) — The platform connects custom or third-party language model providers via API keys to maintain control over security, governance, and infrastructure costs. ([source](https://openobserve.ai/ai-sre/))

### Software Engineering & Architecture

- [Multi-tenant Isolation Policies](https://awesome-repositories.com/f/software-engineering-architecture/multi-tenant-isolation-policies.md) — Enforces logical boundaries between organizational data and user access to ensure secure, independent workspaces within a shared infrastructure.

### Security & Cryptography

- [Multi-Tenant Isolation Layers](https://awesome-repositories.com/f/security-cryptography/multi-tenant-isolation-layers.md) — Enforces strict multi-tenant boundaries to separate data, users, dashboards, and alerts between different teams or customers. ([source](https://openobserve.ai/))
- [Data Access Governance](https://awesome-repositories.com/f/security-cryptography/data-access-governance.md) — Enforces role-based access control, data redaction, and audit logging to maintain compliance across operational data.
- [Enterprise SSO Authentication](https://awesome-repositories.com/f/security-cryptography/oauth-authentication/enterprise-sso-security/enterprise-sso-authentication.md) — Connects to external identity providers using standard protocols to manage user access and permissions. ([source](https://openobserve.ai/))
- [Zero Trust Access Controls](https://awesome-repositories.com/f/security-cryptography/zero-trust-access-controls.md) — Verifies every user and device request continuously while applying context-aware policies to prevent unauthorized network access. ([source](https://openobserve.ai/security-compliance/))
- [Automatic Redaction](https://awesome-repositories.com/f/security-cryptography/sensitive-data-access-controls/automatic-redaction.md) — Masks or removes sensitive information from incoming observability data streams to ensure compliance and data privacy. ([source](https://openobserve.ai/pricing/))

### Development Tools & Productivity

- [Platform Script Execution](https://awesome-repositories.com/f/development-tools-productivity/platform-script-execution.md) — Applies user-defined transformations using a scripting language for complex data manipulation. ([source](https://openobserve.ai/pipelines/))