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* feat: flush region before close when skip-wal is enabled When closing a region with Noop WAL provider, the region is now flushed before closing to ensure data durability. This prevents data loss for regions configured with skip_wal. Changes: - Add `Closing` variant to `FlushReason` enum - Modify `handle_close_request` to trigger flush for Noop WAL regions - Pass flush reason through the flush pipeline - Add test to verify data persistence after close with skip-wal The flush-on-close flow completes the region cleanup after the flush finishes, ensuring the region is properly removed from all schedulers. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * refactor: extract region cleanup logic into dedicated method Extracts common region cleanup logic (stop, remove, and scheduler cleanup) into a new `remove_region` method to avoid duplication between `handle_close` and `handle_flush_request`. This improves code maintainability and reduces redundancy. Also updates `RegionMap::remove_region` to return the removed region reference, allowing the caller to perform cleanup operations. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * test: split skip-wal region close test into pending and no-pending cases Split the test_close_region_skip_wal test into two separate test cases: - test_close_region_skip_wal_with_pending_data: Tests the scenario where data is inserted before closing a region with skip-wal enabled - test_close_region_skip_wal_without_pending_data: Tests the scenario where a region with skip-wal is closed without any data insertion This improves test clarity and ensures both scenarios are properly covered. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix: skip request handling and compaction for flush-on-close regions When a region is flushed as part of the close operation (flush_on_close=true), the region is immediately removed from the server. Therefore, there's no need to handle pending requests or schedule compactions for such regions. This fix moves the on_flush_success listener call outside the conditional block and wraps all post-flush operations (request handling, compaction scheduling) in an else branch, ensuring they only execute for normal flush operations where the region remains active. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * test: add close follower region test with skip-wal Adds a test case for closing a follower region with skip-wal enabled. The test verifies that when a region transitions from Follower to Leader before closing, the flush mechanism works correctly even with WAL disabled. Also refactors flushable_region() to return Option instead of erroring when region is not operable, allowing more flexible handling of region states during flush operations. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix: fmt Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * revise test logic for closing a follower region Signed-off-by: Lei, HUANG <mrsatangel@gmail.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 that unifies metrics, logs, and traces, enabling real-time observability at any scale — across edge, cloud, and hybrid environments.
Features
| Feature | Description |
|---|---|
| All-in-One Observability | OpenTelemetry-native platform unifying metrics, logs, and traces. Query via SQL, PromQL, and Flow. |
| High Performance | Written in Rust with rich indexing (inverted, fulltext, skipping, vector), delivering sub-second responses at PB scale. |
| Cost Efficiency | 50x lower operational and storage costs with compute-storage separation and native object storage (S3, Azure Blob, etc.). |
| Cloud-Native & Scalable | Purpose-built for Kubernetes with unlimited cross-cloud scaling, handling hundreds of thousands of concurrent requests. |
| Developer-Friendly | SQL/PromQL interfaces, built-in web dashboard, REST API, MySQL/PostgreSQL protocol compatibility, and native OpenTelemetry support. |
| Flexible Deployment | Deploy anywhere from ARM-based edge devices (including Android) to cloud, with unified APIs and efficient data sync. |
✅ Perfect for:
- Unified observability stack replacing Prometheus + Loki + Tempo
- Large-scale metrics with high cardinality (millions to billions of time series)
- Large-scale observability platform requiring cost efficiency and scalability
- IoT and edge computing with resource and bandwidth constraints
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 | Custom/PromQL | Custom/DSL |
| Deployment | Edge + Cloud | Cloud/On-prem | Mostly central |
| Indexing & Performance | PB-Scale, Sub-second | Varies | Varies |
| Integration | REST API, SQL, Common protocols | Varies | Varies |
Performance:
Read more benchmark reports.
Architecture
GreptimeDB can run in two modes:
- Standalone Mode - Single binary for development and small deployments
- Distributed Mode - Separate components for production scale:
- Frontend: Query processing and protocol handling
- Datanode: Data storage and retrieval
- Metasrv: Metadata management and coordination
Read the architecture document. DeepWiki provides an in-depth look at GreptimeDB:
Try GreptimeDB
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
Read more in the 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
- gRPC Ingester: Go, Java, C++, Erlang, Rust
- Grafana Data Source: GreptimeDB Grafana data source plugin
- Grafana Dashboard: Official Dashboard for monitoring
Project Status
Status: Beta — marching toward v1.0 GA! GA (v1.0): January 10, 2026
- Deployed in production by open-source projects and commercial users
- Stable, actively maintained, with regular releases (version info)
- Suitable for evaluation and pilot deployments
GreptimeDB v1.0 represents a major milestone toward maturity — marking stable APIs, production readiness, and proven performance.
Roadmap: Beta1 (Nov 10) → Beta2 (Nov 24) → RC1 (Dec 8) → GA (Jan 10, 2026), please read v1.0 highlights and release plan for details.
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 DataFusion™ (query engine)
- Apache OpenDAL™ (data access abstraction)