GreptimeTeam/greptimedb

The open-source observability database. One columnar engine for metrics, logs, and traces, on object storage.

What it solves

GreptimeDB is an open-source observability database designed to unify metrics, logs, and traces into a single columnar engine. It eliminates the need to run separate backends for different telemetry signals (such as Prometheus for metrics, Loki for logs, and Tempo for traces), allowing users to correlate these signals using SQL without moving data between different databases.

How it works

The database uses a columnar engine built over object storage (S3, GCS, Azure Blob), which disaggregates compute and storage. It employs a tiered storage model where memory and local-disk caches keep frequently accessed data close to the compute layer.

It supports multiple ingestion protocols including OpenTelemetry (OTLP), Prometheus Remote Write, Loki Push, and Elasticsearch Bulk. For querying, it provides SQL, PromQL, and Jaeger-compatible trace queries, as well as MySQL and PostgreSQL wire protocols.

Who it’s for

It is intended for developers and platform engineers who have outgrown the cardinality or retention limits of Prometheus, are hitting performance limits with Loki, or want a unified SQL-based approach to querying observability data and GenAI/agent telemetry.

Highlights

  • Unified Data Model: Uses a single table model (tags, timestamp, and fields) for all observability signals.
  • Object Storage Backend: Primary storage on object storage for long-term retention and cost-efficiency.
  • Multi-Protocol Support: Compatible with OTel, Prometheus, Loki, and Elasticsearch ingestion.
  • AI Agent Ready: Specifically optimized for LLM agents performing root cause analysis, reducing wrong diagnoses and token costs.
  • Disaggregated Architecture: Separates compute (Frontend, Datanode) from storage, allowing independent scaling.

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