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* fix/filter-empty-batch-in-bulk-insert-api: **Add Early Return for Empty Record Batches in `bulk_insert.rs`** - Implemented an early return in the `Inserter` implementation to handle cases where `record_batch.num_rows()` is zero, improving efficiency by avoiding unnecessary processing. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix/filter-empty-batch-in-bulk-insert-api: **Improve Bulk Insert Handling** - **`handle_bulk_insert.rs`**: Added a check to handle cases where the batch has zero rows, immediately returning and sending a success response with zero rows processed. - **`bulk_insert.rs`**: Enhanced logic to skip processing for masks that select none, optimizing the bulk insert operation by avoiding unnecessary iterations. These changes improve the efficiency and robustness of the bulk insert process by handling edge cases more effectively. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix/filter-empty-batch-in-bulk-insert-api: ### Refactor and Error Handling Enhancements - **Refactored Timestamp Handling**: Introduced `timestamp_array_to_primitive` function in `timestamp.rs` to streamline conversion of timestamp arrays to primitive arrays, reducing redundancy in `handle_bulk_insert.rs` and `bulk_insert.rs`. - **Error Handling**: Added `InconsistentTimestampLength` error in `error.rs` to handle mismatched timestamp column lengths in bulk insert operations. - **Bulk Insert Logic**: Updated `handle_bulk_insert.rs` to utilize the new timestamp conversion function and added checks for timestamp length consistency. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix/filter-empty-batch-in-bulk-insert-api: **Refactor `bulk_insert.rs` to streamline imports** - Simplified import statements by removing unused timestamp-related arrays and data types from the `arrow` crate in `bulk_insert.rs`. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> --------- Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> Signed-off-by: evenyag <realevenyag@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)