Real-Time & Cloud-Native Observability Database
for metrics, logs, and traces

Delivers sub-second querying at PB scale and exceptional cost efficiency from edge to cloud.

User Guide | API Docs | Roadmap 2025

• Introduction
• ⭐ Key Features
• Quick Comparison
• Architecture
• Try GreptimeDB
• Getting Started
• Build From Source
• Tools & Extensions
• Project Status
• Community
• License
• Commercial Support
• Contributing
• Acknowledgement

Introduction

GreptimeDB is an open-source, cloud-native database purpose-built for the unified collection and analysis of observability data (metrics, logs, and traces). Whether you’re operating on the edge, in the cloud, or across hybrid environments, GreptimeDB empowers real-time insights at massive scale — all in one system.

Features

Feature	Description
Unified Observability Data	Store metrics, logs, and traces as timestamped, contextual wide events. Query via SQL, PromQL, and streaming.
High Performance & Cost Effective	Written in Rust, with a distributed query engine, rich indexing, and optimized columnar storage, delivering sub-second responses at PB scale.
Cloud-Native Architecture	Designed for Kubernetes, with compute/storage separation, native object storage (AWS S3, Azure Blob, etc.) and seamless cross-cloud access.
Developer-Friendly	Access via SQL/PromQL interfaces, REST API, MySQL/PostgreSQL protocols, and popular ingestion protocols.
Flexible Deployment	Deploy anywhere: edge (including ARM/Android) or cloud, with unified APIs and efficient data sync.

Learn more in Why GreptimeDB and Observability 2.0 and the Database for It.

Quick Comparison

Feature	GreptimeDB	Traditional TSDB	Log Stores
Data Types	Metrics, Logs, Traces	Metrics only	Logs only
Query Language	SQL, PromQL, Streaming	Custom/PromQL	Custom/DSL
Deployment	Edge + Cloud	Cloud/On-prem	Mostly central
Indexing & Performance	PB-Scale, Sub-second	Varies	Varies
Integration	REST, SQL, Common protocols	Varies	Varies

Performance:

• GreptimeDB tops JSONBench's billion-record cold run test!
• TSBS Benchmark

Read more benchmark reports.

Architecture

• Read the architecture document.
• DeepWiki provides an in-depth look at GreptimeDB:

Try GreptimeDB

docker pull greptime/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 \
  --rpc-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 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 (nightly)
• Protobuf compiler (>= 3.15)
• C/C++ building essentials, including gcc/g++/autoconf and glibc library (eg. libc6-dev on Ubuntu and glibc-devel on Fedora)
• Python toolchain (optional): Required only if using some test scripts.

Build and Run:

make
cargo run -- standalone start

Tools & Extensions

• Kubernetes: GreptimeDB Operator
• Helm Charts: Greptime Helm Charts
• Dashboard: Web UI
• SDKs/Ingester: Go, Java, C++, Erlang, Rust, JS
• Grafana: Official Dashboard

Project Status

Status: Beta. GA (v1.0): Targeted for mid 2025.

• Being used in production by early adopters
• Stable, actively maintained, with regular releases (version info)
• Suitable for evaluation and pilot deployments

For production use, we recommend using the latest stable release.

If you find this project useful, a ⭐ would mean a lot to us!

Community

We invite you to engage and contribute!

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

License

GreptimeDB is licensed under the Apache License 2.0.

Commercial Support

Running GreptimeDB in your organization? We offer enterprise add-ons, services, training, and consulting. 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 AUTHORS.md.

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