442ca09688
Replaces the `skip` band-aids that PR #13156 ("workflows v1") left on the **Functions** tests with real, running coverage of the current behaviour — on the correct edition — plus new tests for behaviour #13156 introduced but never covered. Test files only, no feature-code churn. Clean branch off `main`. ## Background — what #13156 actually changed #13156 bundled a Functions backend change unrelated to Workflows, and the old tests still asserted the *pre*-#13156 rules, so they were silenced with `pytest.skip(...)` / `test.skip(...)`. Two rules changed: 1. **JS is no longer `_meta`-only at the backend.** `save_function` / `test_run_function` have **no org gate on any edition** — JS (`transType:1`) is accepted in every org, including `default`, on **both OSS and Enterprise**. The `_meta`-only rule now lives **only in the frontend** (`AddFunction.vue` gates the JS radio on `isEnterprise || isCloud || org === "_meta"`, baked at build time by `VITE_OPENOBSERVE_ENTERPRISE`). 2. **The compile-check reports only *syntax* errors, not *runtime* errors.** A syntax error still returns **400**; code that compiles but throws at runtime now returns **200** with the error surfaced **per-event** in `results[].message` — for **both VRL and JS**. Because the API tests hit the backend directly, there is **no edition difference to assert for them** — the earlier "OSS asserts 400 / ENT asserts 200" framing was incorrect. Only the **e2e** (frontend JS-radio) is genuinely edition-dependent. ## API tests (pytest · `tests/api-testing`) **`test_functions.py`** - Keep the compile-error → 400 test (`test_test_endpoint_with_invalid_vrl_returns_400`) — it was never actually failing; `test`/`=====` are genuine VRL *compile* errors (`undefined variable` / syntax) and still 400. - Add `test_test_endpoint_with_js_runtime_error_returns_200` and `test_test_endpoint_with_vrl_runtime_error_returns_200` — runtime errors now return 200 with a per-event `results[].message`. **`test_functions_meta_org_restriction.py`** - Flip the 3 previously-skipped "blocked in default" tests → assert **200** (JS allowed in every org): `..._blocked` → `..._allowed`. - Repurpose `TestErrorMessages` (its old restriction-message assertions no longer exist) into per-event runtime-error coverage: JS `ReferenceError` content, VRL + JS surfacing consistency, partial-failure (only the failing event carries a message), and a syntax-error → 400 regression guard. - Update the class/module docstrings to the real contract. **`test_js_enrichment.py`** - Flip `test_js_function_in_default_org_returns_400` → `test_js_function_in_default_org_allowed` (200, transform runs). ## e2e (Playwright · `tests/ui-testing/Functions/js-transform-type.spec.js`) The JS-radio rule *is* edition-dependent. Instead of a skip (or an env-var hack), the two tests now detect edition at runtime via `EditionFeaturesPage.detectEdition()` — which reads the header edition button, driven by the **same** build flag that gates the JS radio, so it's the exact source of truth and needs **no CI change**: - **OSS build** → assert JS radio **hidden** in a non-meta org. - **Enterprise / cloud build** → assert JS radio **visible** in every org. The edition-agnostic steps that were trapped inside the skipped tests now **always run**: VRL-function create in a non-meta org (a P0 smoke path), and JS-visible in `_meta`. Added `P1: VRL runtime error surfaces in error-details` (mirrors the existing JS `ReferenceError` case; covers the VRL half of the per-event-error change). ## Skips removed (before → after) | Old (skipped) | New (runs & passes) | |---|---| | `test_create_js_function_in_default_org_blocked` | `..._default_org_allowed` | | `test_test_js_function_in_default_org_blocked` | `..._default_org_allowed` | | `test_update_vrl_to_js_in_default_org_blocked` | `..._default_org_allowed` | | `test_error_message_content` / `_consistency` | per-event runtime-error tests | | `test_js_function_in_default_org_returns_400` (enrichment) | `..._default_org_allowed` | | e2e `P0: Non-meta org - JS hidden, VRL works` | `P0: Non-meta org - JS radio edition-gated, VRL works` | | e2e `P1: Radio visibility across orgs` (skipped) | same name, edition-aware, no `test.skip` | `grep -rn "JS fns are now allowed" tests/` and `grep -rn "JS is allowed in all orgs on enterprise" tests/` → **both empty**. (The 7 remaining pytest skips are the unrelated `JS_HELPER_UNAVAILABLE` enrichment-helper cases — a separate pre-existing limitation, not part of this change.) ## Verification (local, against current `main` source) Built the current-`main` OSS binary and ran everything live: - **API:** `32 passed`, `0` target skips (7 unrelated `JS_HELPER_UNAVAILABLE` skips remain). - **e2e OSS build:** `7/7` — `detectEdition=opensource`, JS hidden in non-meta. - **e2e Enterprise-frontend build:** `3/3` edition tests — `detectEdition=enterprise`, JS visible everywhere, VRL runtime error renders in error-details. All backend behaviour was confirmed by probing the live `/functions` + `/functions/test` endpoints, not assumed. ## What CI proves, and where - **This PR (openobserve):** `api-testing.yml` runs the API tests; `playwright.yml` (`Functions` shard) runs the e2e on the **OSS** edition (JS-hidden branch). - **Enterprise edition:** dispatch `o2-enterprise` → `playwright.yml` (workflow_dispatch) with `o2_opensource_repo = test/functions-skipped-tests-13318` to run the same specs on the **enterprise** build (JS-visible branch). Pairs with the ENT Functions work in #13318 / o2-enterprise#2217. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Co-authored-by: Hengfei Yang <hengfei.yang@gmail.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