What problem it solves

Teams need to capture, search, and analyze multiple observability signals without managing fragmented tools or complex infrastructure. OpenObserve addresses this with a unified platform, a single-binary deployment option, and familiar SQL and PromQL query languages.

Key features

• Unified observability — Logs, metrics, traces, RUM, dashboards, alerts, incidents, pipelines, and AI observability are available in one platform and web interface.

  Source: README

• OpenTelemetry-native ingestion — The platform is built on OpenTelemetry and supports tracing across services with waterfalls, flame graphs, and Gantt charts.

  Source: README

• SQL and PromQL querying — Logs and traces can be queried with SQL, while metrics support SQL or PromQL. Dashboards can also be built from any signal.

  Source: README

• Visual data pipelines — A visual editor can enrich, redact, reduce, or normalize data at ingestion time, including converting logs to metrics.

  Source: README

• Cloud-native storage and deployment — The platform uses Parquet columnar storage and an S3-native design, while the Rust implementation is distributed as a single binary for straightforward deployment.

  Source: README

Quick start

Copied verbatim from the project's README.

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

Who it may suit

Inference: may suit teams operating services and applications that need to correlate logs, metrics, traces, frontend behavior, and AI/LLM application signals.

Topics to discuss

• Which OpenTelemetry workflows or integrations would most improve the community experience?

• How should teams design pipeline transformations for logs, metrics, and traces?

• What deployment and observability use cases should receive more documentation and examples?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/openobserve/openobserve#readme

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