greptimedb/README.md

Metrics, logs, and traces.
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:

-- The slowest failed spans in the last hour,
-- with the log lines emitted inside those same traces.
SELECT
    t.service_name,
    t.span_name,
    t.duration_nano / 1000000 AS duration_ms,
    l.timestamp AS log_time,
    l.severity_text,
    l.body
FROM opentelemetry_traces t
JOIN opentelemetry_logs l ON l.trace_id = t.trace_id
WHERE t.timestamp > now() - INTERVAL '1' HOUR
  AND t.span_status_code = 'STATUS_CODE_ERROR'
ORDER BY t.duration_nano DESC
LIMIT 20;

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.

Read replicas, workload isolation, and automated repartitioning are GreptimeDB Enterprise features, along with enterprise security and governance. The Enterprise overview has the current list, and pricing has the edition comparison.

Architecture

GreptimeDB can run in two modes:

• Standalone — single binary for development and small deployments.
• Distributed — four components, each independently scalable:
  • Frontend — protocol entry (OTel, Prometheus, MySQL/PostgreSQL, gRPC, ingestion APIs for Elasticsearch/InfluxDB/Loki) and the distributed query engine. Stateless, scales horizontally.
  • Datanode — region engine with WAL, memtable, SST, cache, compaction, and indexes. Persists data to object storage. Elastic.
  • Metasrv — metadata, routing, repartitioning, and security. Backed by a pluggable KV layer (etcd or RDS).
  • Flownode (optional) — continuous flow computation (streaming and materialized views).

For deeper coverage, see the architecture doc or DeepWiki.

Try GreptimeDB

For AI agents — paste this prompt into your agent:

Read https://docs.greptime.com/SKILL.md and follow the instructions
to deploy, configure, ingest, and query GreptimeDB.

docker run -p 127.0.0.1:4000-4003:4000-4003 \
  -v "$(pwd)/greptimedb_data:/greptimedb_data" \
  --name greptime --rm \
  greptime/greptimedb:latest standalone start \
  --http-addr 0.0.0.0:4000 \
  --grpc-bind-addr 0.0.0.0:4001 \
  --mysql-addr 0.0.0.0:4002 \
  --postgres-addr 0.0.0.0:4003

Dashboard: http://localhost:4000/dashboard

Read more in the full Install Guide.

Troubleshooting:

• Cannot connect to the database? Ensure that ports 4000, 4001, 4002, and 4003 are not blocked by a firewall or used by other services.
• Failed to start? Check the container logs with docker logs greptime for further details.

Getting Started

• Quickstart
• User Guide
• Demo Scenes
• FAQ

Build From Source

Prerequisites:

• Rust toolchain — stable, pinned by rust-toolchain.toml
• Protobuf compiler (>= 3.15)
• C/C++ building essentials: gcc / g++ / autoconf and the glibc dev package (libc6-dev on Ubuntu, glibc-devel on Fedora)
• Python toolchain (optional, only for some test scripts)

Build and run:

make                          # build greptime binary
cargo run -- standalone start # start in standalone mode

Common dev commands:

make fmt            # format Rust code
make clippy         # lint (fails on warnings)
make test           # unit + integration tests (uses cargo-nextest)
make sqlness-test   # SQL regression tests

See the Contribution Guidelines for the full developer workflow.

Tools & Extensions

• Kubernetes: GreptimeDB Operator
• Helm Charts: Greptime Helm Charts
• Dashboard: Web UI
• gRPC Ingester: Go, Java, C++, Erlang, Rust, .NET, TypeScript
• Grafana Data Source: GreptimeDB Grafana data source plugin
• Grafana Dashboard: Official Dashboard for monitoring

Project Status

GreptimeDB is generally available, with stable APIs and regular releases. It runs in production at scale — OceanBase Cloud operates 80+ GreptimeDB clusters managing 300 TB of logs, cutting log storage cost by 60%+ after migrating from Grafana Loki. See more in case studies.

Release lines and support windows are in the version reference. For where the project is going, read the v1.0 highlights and the 2026 roadmap.

Community

We invite you to engage and contribute!

• Slack
• Discussions
• Official Website
• Blog
• LinkedIn
• X (Twitter)
• YouTube

If GreptimeDB is useful to you, please star the repo.

License

GreptimeDB is an open-core project. Its core is licensed under the Apache License 2.0.

A small set of peripheral, enterprise-only features are gated behind the enterprise Cargo feature (not built by default) and are governed by the separate GreptimeDB Enterprise License. Source files under that license carry an explicit Enterprise License header.

Commercial Support

Scaling observability on your infrastructure? GreptimeDB Enterprise adds the operational, security, and support layer for production deployments. Contact us for details.

Contributing

• Read our Contribution Guidelines.
• Explore Internal Concepts and DeepWiki.
• Pick up a good first issue and join the #contributors Slack channel.

Acknowledgement

Special thanks to all contributors! See AUTHOR.md.

• Uses Apache Arrow™ (memory model)
• Apache Parquet™ (file storage)
• Apache DataFusion™ (query engine)
• Apache OpenDAL™ (data access abstraction)

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