Unified Time Series Database for Metrics, Events, and Logs

GreptimeCloud | User Guide | API Docs | Roadmap 2024

Introduction

GreptimeDB is an open-source unified time-series database for Metrics, Events, and Logs (also Traces in plan). You can gain real-time insights from Edge to Cloud at any scale.

Why GreptimeDB

Our core developers have been building time-series data platforms for years. Based on our best-practices, GreptimeDB is born to give you:

• Unified all kinds of time series

  GreptimeDB treats all time series as contextual events with timestamp, and thus unifies the processing of metrics and events. It supports analyzing metrics and events with SQL and PromQL, and doing streaming with continuous aggregation.

• Cloud-Edge collaboration

  GreptimeDB can be deployed on ARM architecture-compatible Android/Linux systems as well as cloud environments from various vendors. Both sides run the same software, providing identical APIs and control planes, so your application can run at the edge or on the cloud without modification, and data synchronization also becomes extremely easy and efficient.

• Cloud-native distributed database

  By leveraging object storage (S3 and others), separating compute and storage, scaling stateless compute nodes arbitrarily, GreptimeDB implements seamless scalability. It also supports cross-cloud deployment with a built-in unified data access layer over different object storages.

• Performance and Cost-effective

  Flexible indexing capabilities and distributed, parallel-processing query engine, tackling high cardinality issues down. Optimized columnar layout for handling time-series data; compacted, compressed, and stored on various storage backends, particularly cloud object storage with 50x cost efficiency.

• Compatible with InfluxDB, Prometheus and more protocols

  Widely adopted database protocols and APIs, including MySQL, PostgreSQL, and Prometheus Remote Storage, etc. Read more.

Try GreptimeDB

1. GreptimePlay

Try out the features of GreptimeDB right from your browser.

2. GreptimeCloud

Start instantly with a free cluster.

3. Docker Image

To install GreptimeDB locally, the recommended way is via Docker:

docker pull greptime/greptimedb

Start a GreptimeDB container with:

docker run --rm --name greptime --net=host greptime/greptimedb standalone start

Read more about Installation on docs.

Getting Started

• Quickstart
• Write Data
• Query Data
• Operations

Build

Check the prerequisite:

• Rust toolchain (nightly)
• Protobuf compiler (>= 3.15)
• Python toolchain (optional): Required only if built with PyO3 backend. More detail for compiling with PyO3 can be found in its documentation.

Build GreptimeDB binary:

make

Run a standalone server:

cargo run -- standalone start

Extension

Dashboard

• The dashboard UI for GreptimeDB

SDK

• GreptimeDB Go Ingester
• GreptimeDB Java Ingester
• GreptimeDB C++ Ingester
• GreptimeDB Erlang Ingester
• GreptimeDB Rust Ingester
• GreptimeDB JavaScript Ingester

Grafana Dashboard

Our official Grafana dashboard is available at grafana directory.

Project Status

The current version has not yet reached General Availability version standards. In line with our Greptime 2024 Roadmap, we plan to achieve a production-level version with the update to v1.0 in August. [Join Force]

Community

Our core team is thrilled to see you participate in any ways you like. When you are stuck, try to ask for help by filling an issue with a detailed description of what you were trying to do and what went wrong. If you have any questions or if you would like to get involved in our community, please check out:

• GreptimeDB Community on Slack
• GreptimeDB GitHub Discussions forum
• Greptime official website

In addition, you may:

• View our official Blog
• Connect us with Linkedin
• Follow us on Twitter

License

GreptimeDB uses the Apache License 2.0 to strike a balance between open contributions and allowing you to use the software however you want.

Contributing

Please refer to contribution guidelines and internal concepts docs for more information.

Acknowledgement

Special thanks to all the contributors who have propelled GreptimeDB forward. For a complete list of contributors, please refer to AUTHOR.md.

• GreptimeDB uses Apache Arrow™ as the memory model and Apache Parquet™ as the persistent file format.
• GreptimeDB's query engine is powered by Apache Arrow DataFusion™.
• Apache OpenDAL™ gives GreptimeDB a very general and elegant data access abstraction layer.
• GreptimeDB's meta service is based on etcd.
• GreptimeDB uses RustPython for experimental embedded python scripting.