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* fix/file-group-in-compaction: ### Enhance Compaction Logic with File Grouping - **`run.rs`**: Introduced `FileGroup` struct to manage groups of `FileHandle` objects, allowing for more efficient compaction operations. Updated `Ranged` and `Item` trait implementations to work with `FileGroup`. - **`test_util.rs`**: Added `new_file_handle_with_sequence` function to support file handles with sequence numbers, enhancing test utilities. - **`twcs.rs`**: Modified `TwcsPicker` to utilize `FileGroup` for managing files within windows, improving compaction logic. Updated `Window` struct to use `HashMap` for storing `FileGroup` objects. - **`version_util.rs`**: Updated version control utilities to handle sequence numbers in file metadata, aligning with new compaction logic. Signed-off-by: Lei, HUANG <lhuang@greptime.com> * fix/file-group-in-compaction: ### Add Test for File Group Assignment in TWCS - **Enhancements in `twcs.rs`:** - Added a new test `test_assign_file_groups_to_windows` to verify the correct assignment of file groups to windows. - Enhanced `test_assign_compacting_to_windows` with a new case to ensure files with overlapping time ranges and the same sequence are treated as one `FileGroup`. Signed-off-by: Lei, HUANG <lhuang@greptime.com> * fix/file-group-in-compaction: **Enhance Compaction Task Documentation and Initialization** - **`run.rs`**: Added documentation for `FileGroup` to clarify its role in representing a group of files created by the same compaction task. - **`twcs.rs`**: Introduced comments in the `Window` struct to explain the mapping of file sequences to file groups, indicating files created from the same compaction task. Simplified the initialization of the `files` hashmap using `HashMap::from`. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> --------- Signed-off-by: Lei, HUANG <lhuang@greptime.com> Signed-off-by: Lei, HUANG <mrsatangel@gmail.com>
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.
- 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:
Read more benchmark reports.
Architecture
- Read the architecture document.
- DeepWiki provides an in-depth look at GreptimeDB:
Try GreptimeDB
1. Live Demo
Experience GreptimeDB directly in your browser.
2. GreptimeCloud
Start instantly with a free cluster.
3. Docker (Local Quickstart)
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, and4003are not blocked by a firewall or used by other services. - Failed to start? Check the container logs with
docker logs greptimefor further details.
Getting Started
Build From Source
Prerequisites:
- Rust toolchain (nightly)
- Protobuf compiler (>= 3.15)
- C/C++ building essentials, including
gcc/g++/autoconfand glibc library (eg.libc6-devon Ubuntu andglibc-develon 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!
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)