OpenObserve — Rust-Based Petabyte-Scale Observability Platform
All-in-one Rust observability platform that ingests logs, metrics, traces and RUM into Parquet on object storage for 140x cheaper retention.
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What it is
OpenObserve is an all-in-one observability platform written in Rust. It ingests logs, metrics, traces, and Real User Monitoring (RUM) data, storing everything as Parquet files on object storage (S3, MinIO, GCS). This architecture enables petabyte-scale retention at a fraction of the cost of Elasticsearch or Datadog.
OpenObserve is built for DevOps teams and platform engineers who need unified observability without the operational complexity and cost of traditional solutions. It provides a web UI for querying, dashboards, and alerting.
How it saves time or tokens
OpenObserve replaces multiple observability tools (Elasticsearch for logs, Prometheus for metrics, Jaeger for traces) with a single binary. The Parquet-on-object-storage architecture can reduce storage costs by up to 140x compared to Elasticsearch because object storage is cheap and Parquet compresses efficiently. The single binary deployment eliminates the operational overhead of managing separate clusters for each telemetry type.
How to use
- Run the all-in-one Docker container:
docker run -d --name openobserve -p 5080:5080 -e ZO_ROOT_USER_EMAIL=admin@example.com -e ZO_ROOT_USER_PASSWORD=Complexpass#123 public.ecr.aws/zinclabs/openobserve:latest. - Open the web UI at
http://localhost:5080. - Ingest logs via HTTP API or configure log shippers (FluentBit, Vector, Filebeat).
- Build dashboards and set up alerts from the web interface.
Example
# Start OpenObserve
docker run -d --name openobserve \
-v $PWD/data:/data \
-p 5080:5080 \
-e ZO_ROOT_USER_EMAIL=admin@example.com \
-e ZO_ROOT_USER_PASSWORD=Complexpass#123 \
public.ecr.aws/zinclabs/openobserve:latest
# Ingest logs via HTTP
curl -u admin@example.com:Complexpass#123 \
-H 'Content-Type: application/json' \
-d '[{"level":"info","message":"hello","service":"demo"}]' \
http://localhost:5080/api/default/default/_json
Related on TokRepo
- AI tools for monitoring -- explore observability and monitoring tools on TokRepo.
- AI tools for DevOps -- browse DevOps infrastructure tools.
Common pitfalls
- Object storage configuration is required for production. Local disk mode works for testing but does not provide the cost benefits or durability of S3/MinIO.
- Query performance depends on proper indexing and partitioning. Configure stream partitions based on your most common query patterns.
- Migration from Elasticsearch requires changing your ingest pipeline. OpenObserve supports the Elasticsearch bulk API for easier migration, but query syntax differs.
常见问题
OpenObserve stores data as compressed Parquet files on object storage (S3, MinIO) instead of maintaining inverted indexes on SSDs. Object storage costs a fraction of SSD-backed compute nodes, and Parquet compression ratios further reduce storage volume.
OpenObserve ingests logs, metrics, traces, and Real User Monitoring (RUM) data in a single platform. It accepts data via HTTP API, OpenTelemetry, Prometheus remote write, FluentBit, Vector, and other common shippers.
Yes. OpenObserve is designed for production use with features like high availability, role-based access control, alerting, and dashboard management. The Rust implementation provides strong performance and memory safety.
OpenObserve covers the core observability features (logs, metrics, traces, dashboards, alerts) that Datadog provides. It does not include APM code instrumentation or some advanced SaaS features. For teams that primarily need log and metrics analysis, it is a viable self-hosted alternative.
OpenObserve supports SQL for querying logs and metrics. You write standard SQL queries against your data streams. It also provides a full-text search mode for log exploration similar to Elasticsearch query syntax.
引用来源 (3)
- OpenObserve GitHub— OpenObserve is a Rust-based observability platform
- Apache Parquet— Parquet columnar storage format for efficient analytics
- OpenTelemetry— OpenTelemetry integration for unified telemetry collection
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