Omkar Kesarkhaneandgithub-actions[bot]
fac24d59bb
## Problem
Four defects around traces and the trace↔log correlation flows:
1. **Trace Graph is clipped.** The service map renders inside a 200px
band, vertically centred in an otherwise empty pane, instead of using
the available height.
2. **A span row's View Logs goes nowhere.** In OSS builds it navigates
to `/logs?stream=&from=NaN&to=NaN`; the logs page drops the filter and
falls back to its default window, so the user gets an unfiltered result
set instead of that span's logs.
3. **The log detail drawer has no View Trace button.** Opening the trace
for a log record only works from the inline expanded row.
4. **View Trace lived inside the JSON tab**, so it disappeared when
switching to Table.
## Root causes
**Trace Graph.** The ancestor chain was already correct — the sibling
Waterfall tab fills the same box. Only the leaf was clamped: the chart
carried `h-50! min-h-50!` (12.5rem, `!important`), which beat its own
root `h-full w-full`. That clamp dates to when the map lived in a small
collapsible timeline strip, where 200px was the intended design. When it
was promoted to a full-height tab, the scoped rule `.trace-details
.trace-chart-height { height: 12.5rem }` was left behind and its
two-class specificity silently defeated the `h-full!` added at the time.
The later design-token migration deleted the rule but preserved the
rendered result by translating it into utilities, which made the clamp
look deliberate.
**Span View Logs.** `buildQueryDetails()` reads the raw API shape
(`start_time`/`end_time`, nanoseconds), but the waterfall passes
formatted nodes, which `getFormattedSpan()` rebuilds as a fresh object
literal — camelCase, times as `startTimeUs`/`endTimeUs` in microseconds,
snake_case originals dropped. Reading `start_time` off one yields
`undefined`, hence `NaN`. The helper already had a `spanId || span_id`
fallback for the id, which is why the query string was correct while the
window was not.
**Drawer View Trace.** `JsonPreview` decides whether to offer the action
once, in `onBeforeMount`. `DetailTable` fills `rowData` in an `async
created()` — the `await` defers assignment past the child's mount even
though the work is synchronous — so the preview mounted with an empty
record, the check failed, the traces streams were never fetched, and
nothing recomputed it.
## Changes
- Trace Graph chart uses `h-full`; the orphaned `trace-chart-height`
class is dropped (its rule no longer exists) and two nested wrappers
collapse into one. Matches the full-bleed pattern in `ServiceGraph.vue`.
- The span row resolves the raw span from `spanMap` before calling the
helper — the same lookup this component already does for span data, HTTP
status and events — rather than teaching the shared helper to accept two
shapes. Guarded, since a span missing `span_id` gets a synthetic id
absent from the map.
- The span row also warns instead of navigating when no logs stream is
selected. The header's own View Logs button is disabled in that state
but the row-level one is not, so it was a silent dead end.
- `JsonPreview` re-evaluates the View Trace gate when the previewed
record changes, so the flag tracks its input rather than a single
snapshot.
- The action moves to the drawer's header row beside the wrap toggle,
reachable from every tab, with `hide-view-trace` on `JsonPreview`
(mirroring the existing `hide-view-related`). The gate, stream fetch and
filter move into `useViewTraceAction` rather than being copied into
`DetailTable` — `service_streams_enabled` is already hand-rolled in
three files and drifted. `JsonPreview` keeps exposing the same names, so
its specs are untouched.
Two latent issues surfaced while moving the action:
- `hiddenMenus` is seeded as an array and only becomes a `Set` once
`MainLayout` has read `custom_hide_menus`, so `.has()` is not always
available. Both shapes are now accepted.
- `OSelect`'s root sets `w-full`, which won a plain `w-40` and pushed
the button outside the drawer — the same class-collision shape as defect
1. Pinned with `w-40!`.
## Tests
No behaviour is asserted by new specs; existing suites cover the touched
paths and pass unmodified:
- `JsonPreview.spec.ts` — 91 passed (unchanged, including the assertions
on `tracesStreams`)
- `DetailTable.spec.ts` — 70 passed, 3 skipped
- `TraceTree.spec.ts`, `useTraces.spec.ts`,
`TraceDetailsSidebar.spec.ts` — 297 passed
- `TraceDetails` suites — 234 passed
- `SearchResult.viewTrace.spec.ts`,
`CorrelatedLogsTable.viewTrace.spec.ts` — passed
Prettier and ESLint are clean on every touched file.
## Verification
Checked against live trace and log data sharing a trace id:
- Trace Graph fills its pane (measured 549px where it previously
measured 200px), survives a tab round-trip, and reflows on window
resize.
- A span row's View Logs lands on `/logs` with the span+trace filter and
a ±60s window whose bounds match the span's real timestamps exactly;
with no stream selected it warns and does not navigate.
- The drawer's header shows the picker and button on both the JSON and
Table tabs with no overflow, and opens the trace with the correct id and
a ±15 min window.
- Console clean across every navigation.
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Open source Datadog alternative for logs, metrics, traces, and frontend monitoring. Modern observability platform: 10x easier, 140x lower storage cost, high performance, petabyte scale.
Cloud · Documentation · Slack · Quickstart
OpenObserve (O2) is a cloud-native observability platform for logs, metrics, traces, analytics, Real User Monitoring (RUM), and AI/LLM observability. It's a cost-effective alternative to Datadog, Splunk, and Elasticsearch for teams that need full observability without the complexity or cost — with Parquet columnar storage and an S3-native design that cuts storage costs by up to 140x.
Table of Contents
- Why OpenObserve?
- Quick Start
- Product Tour
- Architecture
- Comparisons
- Production Ready
- Security & Compliance
- Enterprise Edition
- Community & Support
- Contributing
- FAQ
- License
- SBOM
Why OpenObserve?
A single platform for all of your observability signals. Here's why teams choose OpenObserve:
| Benefit | Description |
|---|---|
| 140x lower storage cost | Parquet columnar storage + S3-native architecture dramatically reduce costs vs Elasticsearch |
| Single binary deployment | Up and running in under 2 minutes — no complex cluster setup required |
| OpenTelemetry native | Built on the OpenTelemetry standard — no vendor lock-in |
| Unified platform | Logs, metrics, traces, RUM, dashboards, alerts, and incidents in one tool |
| High performance | Better query performance than Elasticsearch on a quarter of the hardware |
| SQL + PromQL | Query logs and traces with SQL, metrics with SQL or PromQL — no proprietary query language |
| Built in Rust | Memory-safe, high-performance, single binary |
Cost comparison: OpenObserve vs Elasticsearch
Quick Start
OpenObserve Cloud (fastest way)
Get started in minutes without managing infrastructure. The free tier includes up to 50 GB/day of ingestion.
🐳 Docker
docker run -d \
--name openobserve \
-v $PWD/data:/data \
-p 5080:5080 \
-e ZO_ROOT_USER_EMAIL="root@example.com" \
-e ZO_ROOT_USER_PASSWORD="Complexpass#123" \
public.ecr.aws/zinclabs/openobserve:latest
Then open http://localhost:5080 and log in with the credentials above.
For other installation methods, see the quickstart documentation. For clustered deployments, see the High Availability deployment guide.
Product Tour
OpenObserve ships with a powerful, unified web UI for every signal — logs, traces, metrics, dashboards, RUM, alerts, incidents, pipelines, and AI observability.
🏠 Unified Overview
A single home for your workspace — active incidents, service health (error rate, latency, requests), anomalies, and recent events at a glance.
📊 Logs
Centralized log management with full-text search, SQL queries, quick filters, and a visual query builder. Instantly search across all your logs, build dashboards from log data, and set up alerts — all on Parquet columnar storage for 140x lower storage cost than Elasticsearch. Read more →
🔍 Distributed Tracing
Powered by OpenTelemetry, tracing helps you follow requests across services and pinpoint performance bottlenecks. Explore the full request flow with waterfalls, flame graphs, and Gantt charts; click any span to drill into the trace. Read more →
🕸️ Service Graph
Visualize service-to-service dependencies and request flow across your system, with per-edge request counts and health-based coloring (healthy, degraded, warning, critical) to spot problem hotspots at a glance.
📈 Metrics
Explore metrics from your infrastructure and applications, then query them with SQL or PromQL. Browse thousands of metrics with faceted filters, preview them inline, combine multiple queries with formulae, and visualize the results with 19+ chart types. Read more →
📉 Dashboards
Build custom dashboards from any signal with 19+ built-in chart types and 200+ visualization variations, a drag-and-drop panel builder, template variables, and geo maps. Read more →
👀 Frontend Monitoring (RUM)
Real User Monitoring with Core Web Vitals, error tracking, performance analytics, and full session replay — so you can see exactly what your users experience. Read more →
🔔 Alerts
Get notified when something unusual happens on any signal — logs, metrics, or traces. Define thresholds, scheduled or real-time alerts, and notification channels, with alert history and anomaly detection to catch issues early. Read more →
🚨 Incidents
Correlate related alerts into incidents and track them through their lifecycle — open, acknowledged, and resolved — with severity and dimension context for faster response.
🔀 Pipelines
Enrich, redact, reduce, or normalize data at ingest time with a visual editor. Build stream-processing flows — including logs-to-metrics conversion — from source, transform (VRL functions and conditions), and destination nodes. No external tools required. Read more →
🤖 AI Observability
Monitor your GenAI and LLM applications: track cost, tokens, latency percentiles, and error rates across models, with agent graphs, session traces, and evaluation/quality scoring.
✨ O2 AI Assistant
An in-product assistant that writes your SQL, VRL, and PromQL and walks you through logs, traces, metrics, and incidents — turning natural-language questions into queries, dashboards, and alerts.
For the full feature list, see the documentation.
Architecture
OpenObserve achieves 140x lower storage costs and high performance through a modern, cloud-native architecture:
- Parquet columnar storage — efficient compression and fast analytical queries
- S3-native design — inexpensive object storage with intelligent caching
- Built in Rust — memory-safe, high-performance, single binary
- Partitioning, indexing, and smart caching — reduces search space by up to 99% for most queries
- Native multi-tenancy — organizations and streams as first-class concepts with complete data isolation
- Stateless architecture — rapid scaling and low RPO/RTO for disaster recovery
Scale & Deployment
- Thousands of concurrent users can query a single cluster simultaneously
- Single binary scales to terabytes — unique in the observability space
- High Availability mode scales to petabytes for the most demanding workloads
- Multi-region deployments with cluster federation via Super Cluster architecture (Enterprise)
- Federated search across regions and clusters (Enterprise)
High Availability & Disaster Recovery
Deploy in High Availability mode with clustering for mission-critical workloads requiring maximum uptime. OpenObserve's stateless architecture with S3-backed storage enables very low Recovery Point Objective (RPO) and Recovery Time Objective (RTO): stateless nodes restart rapidly, and durability is guaranteed by S3's 99.999999999% (11 nines).
Read the architecture documentation → · Read the enterprise deployment guide →
Comparisons
OpenObserve vs Datadog
| Aspect | OpenObserve | Datadog |
|---|---|---|
| Deployment | Self-hosted or Cloud | SaaS only |
| Pricing model | Per-GB (free up to 200 GB/day) | Per-host + per-GB |
| Open source | Yes (AGPL-3.0) | No |
| OpenTelemetry | Native OTLP | Supported |
| Query language | SQL + PromQL | Proprietary |
| Vendor lock-in | None | High |
OpenObserve vs Elasticsearch
| Aspect | OpenObserve | Elasticsearch |
|---|---|---|
| Storage cost | 140x lower | High (hot/warm/cold tiers) |
| Setup complexity | Single binary | Complex cluster management |
| Query language | SQL | Lucene/KQL |
| Hardware requirements | ~1/4 the resources | High memory/CPU |
OpenObserve vs Splunk
| Aspect | OpenObserve | Splunk |
|---|---|---|
| Licensing | Open source | Expensive enterprise licensing |
| Deployment | Single binary or HA cluster | Complex |
| Query language | SQL + PromQL | SPL (proprietary) |
| Cost | Predictable, low | Unpredictable, high |
OpenObserve vs Grafana/Loki/Prometheus Stack
| Aspect | OpenObserve | Grafana Stack |
|---|---|---|
| Components | Single platform | Multiple tools (Grafana + Loki + Prometheus + Tempo) |
| Management | One binary | Multiple deployments |
| High cardinality | Full support | Loki struggles with high cardinality |
| Query performance | Fast on large volumes | Loki slow on large data |
Production Ready
OpenObserve is battle-tested in production environments worldwide:
- Thousands of active deployments across diverse industries
- Largest deployment: 2+ PB/day ingestion
- Single binary scales to terabytes — unique in the observability space
Security & Compliance
Security Features
- Secure by design with hardened container images
- Data encryption at rest and in transit
- Sensitive Data Redaction (SDR) — automatically redact sensitive data at ingestion and query time (Enterprise)
- Single Sign-On (SSO) — OIDC, OAuth, SAML, LDAP/AD integration (Enterprise)
- Role-Based Access Control (RBAC) — granular permissions (Enterprise) — Learn more →
Compliance Certifications
- ✅ SOC 2 Type II certified
- ✅ ISO 27001 certified
- ✅ GDPR compliant
- ✅ HIPAA ready (BAA available with Enterprise contracts)
OpenObserve meets the stringent security and compliance requirements of regulated industries including finance, healthcare, and government.
Enterprise Edition
OpenObserve is a true open source project. The open source edition is feature-complete and production-ready — logs, metrics, traces, dashboards, alerts, pipelines, and everything you need to run observability at scale. It will always remain actively maintained and free to use without restrictions.
For organizations that need enterprise-grade features and support, an Enterprise edition adds:
Enterprise features
- Single Sign-On (SSO) — OIDC, OAuth, SAML 2.0, LDAP/AD, and major identity providers (Okta, Azure Entra, Google, GitHub, GitLab, Keycloak)
- Advanced RBAC — granular role-based access control with custom roles — Learn more →
- Audit trails — comprehensive immutable audit logs with configurable retention
- Federated search — query across multiple clusters and regions with Super Cluster
- Sensitive Data Redaction (SDR) — automatically redact PII at ingestion and query time
- Advanced encryption — AES-256 SIV cipher keys with Google Tink KeySet and Akeyless integration
- Query & workload management (QoS) — control query resource usage and priorities in multi-tenant environments
Support & SLAs
- Dedicated support with contractual SLA guarantees and priority response times
- Technical account management, architecture review, and deployment assistance
- Migration support from existing tools, plus training and onboarding
Pricing
- Free tier: up to 50 GB/day of ingestion (~1.5 TB/month), including full commercial use (registration required at 50 GB/day)
- Volume discounts and multi-year contracts available
- View the complete feature comparison →
For enterprise inquiries and custom deployments, contact our sales team.
Community & Support
The best way to get help, share ideas, and connect with other OpenObserve users is through our community channels.
🔗 Join us on Slack
Our Slack community is the most active place for installation and configuration help, sharing best practices, discussing the roadmap, and connecting with the core team.
📱 Join the OpenObserve community on WeChat
Other ways to connect
- 💬 GitHub Discussions — longer-form discussions and Q&A
- 🐛 GitHub Issues — report bugs or request features
- 📖 Documentation — guides, tutorials, and API references
Contributing
We welcome contributions from the community! Whether you're fixing bugs, adding features, improving documentation, or sharing feedback, your help makes OpenObserve better for everyone.
To get started, read our Contributing Guide, which covers setting up your development environment, code standards, submitting pull requests, and reporting issues.
FAQ
How does OpenObserve achieve 140x lower storage costs?
Through a combination of Parquet columnar storage (efficient compression) and an S3-native architecture (inexpensive object storage). See the cost comparison chart in the Why OpenObserve? section.
What are the limitations?
All data in OpenObserve is immutable — once ingested, it cannot be modified or deleted (only entire retention periods can be dropped). This is by design and is a feature for logs and compliance use cases, ensuring data integrity and audit trails.
Is this production-ready?
Yes. OpenObserve runs in production across thousands of deployments worldwide, including environments processing in excess of 2 PB/day. See our customer stories for real-world examples.
How does query performance compare to Elasticsearch?
OpenObserve delivers better performance than Elasticsearch for most workloads, with faster search and significantly faster analytics — while using about a quarter of the hardware. The columnar Parquet format is particularly effective for complex aggregations and analytics.
Is there a steep learning curve?
No. OpenObserve is designed to be intuitive from day one:
- Familiar query languages — SQL for logs and traces, PromQL for metrics; no proprietary query language to learn
- Easy-to-use GUI — an intuitive interface with a drag-and-drop dashboard builder
- No complex tuning — unlike Elasticsearch, there are no shards, replicas, or heap sizes to manage. Just install and go.
Most users are productive within hours, not weeks.
License
Open Source Edition — licensed under AGPL-3.0. We chose AGPL to ensure that improvements to OpenObserve remain open source and benefit the entire community, while still allowing free commercial use. Why AGPL, and why it's good for the community →
Enterprise Edition — licensed under a commercial Enterprise License Agreement (not AGPL), which provides additional flexibility for enterprise deployments.
SBOM
Software Bill of Materials for OpenObserve. You can analyze either SBOM with Dependency-Track.
Rust
The SBOM is available here. To regenerate it:
cargo install cargo-cyclonedx
cargo-cyclonedx cyclonedx
JavaScript
The SBOM is available here. To regenerate it:
npm install --global @cyclonedx/cyclonedx-npm
cd web
cyclonedx-npm > sbom.json