GreptimeTeam/greptimedb

One engine, on your infrastructure.
> A columnar database for metrics, logs, and traces on object storage.
> Apache-2.0 licensed core.
User Guide  · 
API Docs  · 
Roadmap 2026  · 
Slack
stable for production  ·  canary includes pre-releases  ·  nightly is a weekly snapshot of main
Introduction
**GreptimeDB** is an open-source observability database. Metrics, logs, and traces run on one columnar engine over object storage and share one table model: tags, timestamp, and fields. When signals carry common identifiers such as service, host, or trace ID, you can correlate them in SQL without moving data between databases.
Ingest through OpenTelemetry, Prometheus Remote Write, Loki Push, or Elasticsearch Bulk. Use SQL across observability data and PromQL for metrics. Migrate ingestion one signal at a time without rebuilding your collectors.
One Query Across Signals
OpenTelemetry ingestion writes spans to opentelemetry_traces and log records to
opentelemetry_logs. Both tables carry trace_id, so correlating them is a join:
Metrics join the same way, on any tag the tables share, such as service,
host, or pod.
Why You Might Use It
- You run Prometheus plus Loki or Elasticsearch and want one backend instead of three
- You have outgrown Prometheus on cardinality or retention and don't want the Thanos/Mimir operational surface
- You are hitting Loki's query performance limits as log volume grows
- You need long retention on object storage without a separate analytics stack
- You want to query telemetry with SQL, not only a domain query language
- You are storing GenAI or agent telemetry (OTel GenAI conventions) alongside infrastructure signals
Learn more in Why GreptimeDB.
What's Supported
| | |
|---|---|
| **Ingest** | OpenTelemetry (OTLP), Prometheus Remote Write, Loki Push, Elasticsearch Bulk, InfluxDB line protocol, gRPC |
| **Query** | SQL, PromQL, Jaeger-compatible trace queries, MySQL and PostgreSQL wire protocols |
| **Storage** | S3, GCS, Azure Blob and S3-compatible endpoints as primary storage, with memory and local-disk caches |
| **Built in** | Retention policies, downsampling, continuous aggregation, explicit table partitioning, and inverted / skipping / fulltext indexes |
Compute and storage are disaggregated: object storage holds the data, while memory and local-disk caches keep recent and frequently queried data close to compute.
Benchmarks
* Agent RCA Bench: LLM agents doing root cause analysis
over GreptimeDB versus Prometheus + Loki + Tempo. 40% fewer wrong diagnoses, 48% fewer
input tokens, 45% lower cost
(write-up)
* GreptimeDB tops JSONBench's billion-record cold run test
* TSBS Benchmark
* More benchmark reports
Compatibility and Migration
Compatibility is per protocol, and query-side coverage is narrower than ingestion.
| | Compatible | Not compatible |
|---|---|---|
| **Prometheus** | Remote Write ingestion; PromQL queries | Gaps are listed in PromQL compatibility |
| **Loki** | Push ingestion; dual-write through Grafana Alloy makes the cutover gradual | LogQL and the rest of the Loki query API |
| **Elasticsearch** | _bulk ingestion in the open-source core; QueryDSL partially, in Enterprise | Most other Elasticsearch APIs |
Limitations and Edition Boundary
Cluster deployment, object storage, the Flow engine, and every ingestion protocol listed above are in the Apache-2.0 build. Repartitioning, region migration, and index creation are manual operations there.