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* refactor(mito2): improve compaction error handling and file removal Refactor compaction task execution to enhance error handling and robustness. - Implemented parallel execution of compaction tasks with proper error capture and logging for individual task failures. - Ensured JoinSnafu is no longer directly used in error propagation, instead handling errors within the task processing loop. - Adjusted file removal logic to correctly include expired SSTs after compaction merges. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * refactor(mito2): extract SstMerger trait for testability in compaction Extract SstMerger trait and DefaultSstMerger implementation to improve the testability of DefaultCompactor. The DefaultCompactor is now generic over SstMerger, allowing mock implementations to be injected for unit testing without relying on the full object storage access layer. This refactoring separates the concerns of SST file merging from the overall compaction orchestration logic. Additionally: - Updated CompactionScheduler to use DefaultCompactor::default(). - Added unit tests for DefaultCompactor using a MockMerger. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix(compaction): propagate join error during sst flush Correctly propagates the error when joining SST flush handles during compaction. Previously, the error was logged but not returned, leading to potential silent failures. Also reorders some imports for consistency. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * perf(compaction): pre-allocate capacity for compacted_inputs Pre-allocates capacity for the compacted_inputs vector based on the estimated total size of inputs and expired SSTs. This optimization aims to reduce vector reallocations during the compaction process. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/allow-partial-compaction: ### Commit Message Enhance `DefaultCompactor` and `MockMerger` for Improved Flexibility - **`compactor.rs`**: - Added `Clone` trait to `DefaultSstMerger` and `MockMerger` to allow cloning. - Removed `Arc` wrapping from `DefaultCompactor`'s `merger` field for direct usage. - Updated `merge_ssts` method to require `Clone` trait for `SstMerger`. - Modified `MockMerger` to use `Arc<Mutex>` for `results` and `call_idx` to ensure thread safety. - Adjusted error handling to use `error::InvalidMetaSnafu` directly. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> --------- Signed-off-by: Lei, HUANG <mrsatangel@gmail.com>
One database for metrics, logs, and traces
replacing Prometheus, Loki, and Elasticsearch
The unified OpenTelemetry backend — with SQL + PromQL on object storage.
- Introduction
- ⭐ Key Features
- How GreptimeDB Compares
- Architecture
- Try GreptimeDB
- Getting Started
- Build From Source
- Tools & Extensions
- Project Status
- Community
- License
- Commercial Support
- Contributing
- Acknowledgement
Introduction
GreptimeDB is an open-source observability database built for Observability 2.0 — treating metrics, logs, and traces as one unified data model (wide events) instead of three separate pillars.
Use it as the single OpenTelemetry backend — replacing Prometheus, Loki, and Elasticsearch with one database built on object storage. Query with SQL and PromQL, scale without pain, cut costs up to 50x.
Features
| Feature | Description |
|---|---|
| Drop-in replacement | PromQL, Prometheus remote write, Jaeger, and OpenTelemetry native. Use as your single backend for all three signals, or migrate one at a time. |
| 50x lower cost | Object storage (S3, GCS, Azure Blob etc.) as primary storage. Compute-storage separation scales without pain. |
| SQL + PromQL | Monitor with PromQL, analyze with SQL. One database replaces Prometheus + your data warehouse. |
| Sub-second at PB-EB scale | Columnar engine with fulltext, inverted, and skipping indexes. Written in Rust. |
✅ Perfect for:
- Replacing Prometheus + Loki + Elasticsearch with one database
- Scaling past Prometheus — high cardinality, long-term storage, no Thanos/Mimir overhead
- Cutting observability costs with object storage (up to 50x savings on traces, 30% on logs)
- AI/LLM observability — store and analyze high-volume conversation data, agent traces, and token metrics via OpenTelemetry GenAI conventions
- Edge-to-cloud observability with unified APIs on resource-constrained devices
Why Observability 2.0? The three-pillar model (separate databases for metrics, logs, traces) creates data silos and operational complexity. GreptimeDB treats all observability data as timestamped wide events in a single columnar engine — enabling cross-signal SQL JOINs, eliminating redundant infrastructure, and naturally supporting emerging workloads like AI agent observability. Read more: Observability 2.0 and the Database for It.
Learn more in Why GreptimeDB.
How GreptimeDB Compares
| Feature | GreptimeDB | Prometheus / Thanos / Mimir | Grafana Loki | Elasticsearch |
|---|---|---|---|---|
| Data types | Metrics, logs, traces | Metrics only | Logs only | Logs, traces |
| Query language | SQL + PromQL | PromQL | LogQL | Query DSL |
| Storage | Native object storage (S3, etc.) | Local disk + object storage (Thanos/Mimir) | Object storage (chunks) | Local disk |
| Scaling | Compute-storage separation, stateless nodes | Federation / Thanos / Mimir — multi-component, ops heavy | Stateless + object storage | Shard-based, ops heavy |
| Cost efficiency | Up to 50x lower storage | High at scale | Moderate | High (inverted index overhead) |
| OpenTelemetry | Native (metrics + logs + traces) | Partial (metrics only) | Partial (logs only) | Via instrumentation |
Benchmarks:
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, .NET
- Grafana Data Source: GreptimeDB Grafana data source plugin
- Grafana Dashboard: Official Dashboard for monitoring
Project Status
Status: RC — marching toward v1.0 GA! GA (v1.0): March 2026
- Deployed in production handling billions of data points daily
- Stable APIs, actively maintained, with regular releases (version info)
GreptimeDB v1.0 represents a major milestone toward maturity — marking stable APIs, production readiness, and proven performance.
Roadmap: v1.0 highlights and release plan and 2026 roadmap.
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)