314f2704d4
* chore: update datafusion * update sqlness case of time.sql Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix: adjust range query partition * fix: hisogram incorrect result Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix: ignore filter pushdown temporarily Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix: update limit sqlness result Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix: histogram with wrong distribution Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix: update negative ordinal sqlness case Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * feat: bump df to cd7a00b Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * resolve conflicts * ignore test_range_filter Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix promql exec panic * fix "select count(*)" exec error * re-enable the "test_range_filter" test since the filter push down seems not necessary to be removed * fix: range query schema error * update sqlness results Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * resolve conflicts * update datafusion, again * fix pyo3 compile error, and update some sqlness results * update decimal sqlness cases Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix: promql literal * fix udaf tests * fix filter pushdown sqlness tests * fix?: test_cast * fix: rspy test fail due to datafusion `sin` signature change * rebase main to see if there are any failed tests * debug ci * debug ci * debug ci * enforce input partition Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * debug ci * fix ci * fix ci * debug ci * debug ci * debug ci * fix sqlness * feat: do not return error while creating a filter * chore: remove array from error * chore: replace todo with unimplemented * Update src/flow/clippy.toml Co-authored-by: Yingwen <realevenyag@gmail.com> --------- Signed-off-by: Ruihang Xia <waynestxia@gmail.com> Co-authored-by: Ruihang Xia <waynestxia@gmail.com> Co-authored-by: WUJingdi <taylor-lagrange@qq.com> Co-authored-by: discord9 <discord9@163.com> Co-authored-by: evenyag <realevenyag@gmail.com> Co-authored-by: tison <wander4096@gmail.com>
Cloud-scale, Fast and Efficient Time Series Database
Introduction
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.
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:
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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.
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Analyzing time-series data
Query your time-series data with SQL and PromQL. Use Python scripts to facilitate complex analytical tasks.
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Cloud-native distributed database
Fully open-source distributed cluster architecture that harnesses the power of cloud-native elastic computing resources.
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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.
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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
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
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:
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
- 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.