openobserve

name

openobserve

description

OpenObserve (O2) — open-source observability platform for logs, metrics, traces, dashboards, alerts, and Real User Monitoring. Use when setting up OpenObserve (Docker, binary, Kubernetes), ingesting logs/metrics/traces via HTTP API, OpenTelemetry, Fluent Bit, Vector, or Prometheus remote_write, configuring streams and organizations, building dashboards, setting up alerts, integrating with OTEL Collector, or migrating from Elasticsearch, Datadog, or the Grafana/Loki/Prometheus stack. Fetch live documentation for up-to-date details.

OpenObserve

CRITICAL: Your training data for OpenObserve is unreliable. APIs change between versions and memorized patterns may be wrong or deprecated. Before writing any code, you MUST use WebFetch to read the live docs:

WebFetch("https://openobserve.ai/docs/quickstart/")

For specific topics (ingestion, alerts, pipelines, OTEL integration), fetch the relevant page from https://openobserve.ai/docs/. Do not proceed without fetching first.

OpenObserve (O2) is a cloud-native observability platform built in Rust for logs, metrics, traces, dashboards, alerts, and Real User Monitoring — a self-hostable alternative to Datadog, Elasticsearch/Kibana, Splunk, and the Grafana/Loki/Prometheus stack. It achieves 140x lower storage costs than Elasticsearch through Parquet columnar storage and S3-native architecture.

Key Capabilities

OpenObserve consolidates what typically requires multiple tools into a single binary:

• Unified observability: Logs, metrics, traces, RUM, dashboards, alerts, and pipelines — no separate Loki, Prometheus, Tempo, and Grafana to stitch together
• 140x lower storage cost: Parquet columnar format + S3-native design vs Elasticsearch hot/warm/cold tiers
• SQL + PromQL: Query logs and traces with SQL, metrics with SQL or PromQL — no proprietary language
• OpenTelemetry native: Built-in OTLP support for traces, metrics, and logs — no vendor lock-in
• Stream auto-creation: Streams (equivalent to indices/tables) are created automatically on first ingest — no schema pre-definition required
• Ingest pipelines: Enrich, redact, reduce, or transform data at ingest time — no external stream processor needed
• Multi-tenancy via organizations: Organizations are first-class concepts with complete data isolation — every API endpoint is scoped to an org
• Single binary deployment: Runs in under 2 minutes with no cluster configuration — scales to terabytes in single-node mode, petabytes in HA mode
• Immutable data: Ingested records cannot be modified or deleted individually — only full retention periods can be dropped (by design, for audit integrity)

Ingestion Quick Reference

HTTP JSON (curl)

# Endpoint pattern: /api/{organization}/{stream_name}/_json
curl -u '[e-mail]:Complexpass#123' \
  -H 'Content-Type: application/json' \
  http://localhost:5080/api/default/my_stream/_json \
  -d '[{"level":"info","message":"hello","_timestamp":1697000000000000}]'

Fluent Bit (HTTP output plugin)

[OUTPUT]
  Name             http
  Match            *
  URI              /api/{organization}/{stream}/_json
  Host             localhost
  Port             5080
  tls              Off
  Format           json
  Json_date_key    _timestamp
  Json_date_format iso8601
  HTTP_User        [e-mail]
  HTTP_Passwd      password

Fluent Bit (Elasticsearch-compatible output)

[OUTPUT]
  Name              es
  Match             *
  Path              /api/{organization}
  Host              localhost
  index             {stream}
  Port              5080
  tls               Off
  Suppress_Type_Name On
  HTTP_User         [e-mail]
  HTTP_Passwd       password

Prometheus remote_write

remote_write:
  - url: http://localhost:5080/api/default/prometheus/api/v1/write
    queue_config:
      max_samples_per_send: 10000
    basic_auth:
      username: [e-mail]
      password: password

OpenTelemetry traces (OTLP HTTP)

POST /api/{organization}/traces

Configure your OTEL exporter to target http://localhost:5080/api/{org}/ with basic auth headers.

Docker Quick Start

docker run -d \
  --name openobserve \
  -v $PWD/data:/data \
  -p 5080:5080 \
  -e ZO_ROOT_USER_EMAIL="[e-mail]" \
  -e ZO_ROOT_USER_PASSWORD="Complexpass#123" \
  public.ecr.aws/zinclabs/openobserve:latest

UI is available at http://localhost:5080. Default org is default.

Key Environment Variables

Variable	Default	Purpose
ZO_ROOT_USER_EMAIL	—	Root user email (required on first start)
ZO_ROOT_USER_PASSWORD	—	Root user password (required on first start)
ZO_DATA_DIR	./data/openobserve/	Local data directory
ZO_S3_PROVIDER	—	Object storage provider: aws, gcs, minio, swift
ZO_S3_SERVER_URL	—	S3-compatible endpoint (required for MinIO/GCS)
ZO_S3_BUCKET_NAME	—	Storage bucket name
ZO_S3_ACCESS_KEY	—	Object storage access key
ZO_S3_SECRET_KEY	—	Object storage secret key
ZO_S3_REGION_NAME	—	Cloud region
ZO_MEMORY_CACHE_ENABLED	true	In-memory file caching
ZO_DISK_CACHE_ENABLED	true	Disk-based query caching

Best Practices

• The _timestamp field must be microseconds (not milliseconds or seconds). OpenObserve expects the _timestamp field in microseconds since Unix epoch. Sending milliseconds produces incorrect time ordering (data appears 1000x too old) without any error or warning. If omitted, OpenObserve auto-generates _timestamp at ingest time — but if your log already has a time field, map it explicitly and convert to microseconds. In Fluent Bit, set Json_date_format iso8601 and Json_date_key _timestamp and let OpenObserve parse ISO 8601, which avoids manual epoch conversion.

• Streams are created automatically on first ingest — schema is inferred, not enforced. Sending data to /api/default/my_stream/_json creates the my_stream stream automatically if it does not exist. There is no PUT /stream step. The downside: if your first batch has inconsistent field types (e.g., level as integer in one record, string in another), the inferred schema may cause later query failures. Send a consistent payload on first ingest or define the schema explicitly in the UI before ingesting.

• Every API endpoint is scoped to an organization — omitting or misspelling the org name creates a second isolated org. The URL pattern /api/{organization}/{stream}/_json requires the correct org slug. The default org created on first start is default. If you typo the org (e.g., /api/Default/ vs /api/default/), OpenObserve silently creates a new org and the data is invisible in the intended org's UI. Verify the org slug in the UI under IAM before configuring collectors.

• Do not use local disk storage in production. The default ZO_DATA_DIR stores all stream data on local disk with no replication. A single disk failure loses all ingested data. For production, configure an S3-compatible backend (ZO_S3_PROVIDER, ZO_S3_BUCKET_NAME, etc.) before ingesting any data — there is no migration path from local disk to S3 after the fact. MinIO is a common self-hosted alternative if AWS S3 is not available.

• Stream and organization names are case-sensitive and must use only lowercase alphanumeric characters and underscores. Names like MyApp-Logs or prod.errors will either be rejected or silently normalized, causing mismatch between your collector config and the UI. Stick to my_app_logs and prod_errors patterns. Org names follow the same rule — the default org is default (all lowercase).

• Basic auth credentials must be base64-encoded for the Authorization header but most integrations support plain username/password fields. When using OpenTelemetry Collector or other integrations that require explicit HTTP headers, use Authorization: Basic <base64(user:pass)>. However, Fluent Bit's HTTP_User/HTTP_Passwd and Prometheus basic_auth block handle encoding automatically — do not double-encode. Mixing approaches is a common source of 401 errors.