GreptimeCloud | User guide | API Docs | Roadmap 2024

GreptimeDB is an open-source time-series database focusing on efficiency, scalability, and analytical capabilities. Designed to work on infrastructure of the cloud era, GreptimeDB benefits users with its elasticity and commodity storage, offering a fast and cost-effective alternative to InfluxDB and a long-term storage for Prometheus.

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

• Easy horizontal scaling

  Seamless scalability from a standalone binary at edge to a robust, highly available distributed cluster in cloud, with a transparent experience for both developers and administrators.

• Analyzing time-series data

  Query your time-series data with SQL and PromQL. Use Python scripts to facilitate complex analytical tasks.

• Cloud-native distributed database

  Fully open-source distributed cluster architecture that harnesses the power of cloud-native elastic computing resources.

• 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 out the features of GreptimeDB right from your browser.

Start instantly with a free cluster.

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.

• Quickstart
• Write Data
• Query Data
• Operations

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

• The dashboard UI for GreptimeDB

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

Our official Grafana dashboard is available at grafana directory.

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]

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

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

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

• 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.