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* feat(metric-engine): support bulk inserts Implement `RegionRequest::BulkInserts` to support efficient columnar data ingestion in the metric engine. Key changes: - Implement `bulk_insert_region` to handle logical-to-physical region mapping and dispatch writes. - Add `batch_modifier` for `RecordBatch` transformations, specifically for `__tsid` generation and sparse primary key encoding. - Integrate `BulkInserts` into the `MetricEngine` request handling logic. - Provide a row-based fallback mechanism if the underlying storage doesn't support bulk writes. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: ### Update `bulk_insert.rs` to Support Partition Expression Version - **Enhancements**: - Added support for `partition_expr_version` in `RegionBulkInsertsRequest` and `RegionPutRequest`. - Modified the handling of `partition_expr_version` to be dynamically set from the `request` object. Files affected: - `src/metric-engine/src/engine/bulk_insert.rs` Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * fix: cargo lock revert Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * add doc for conversions Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * chore: simplify test Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: ### Refactor `bulk_insert.rs` in `metric-engine` - **Refactor Functionality**: - Replaced `resolve_tag_columns` with `resolve_tag_columns_from_metadata` to streamline tag column resolution. - Moved logic for resolving tag columns directly into `resolve_tag_columns_from_metadata`, removing the need for an external function call. - **Enhancements**: - Improved error handling and context provision for missing physical regions and columns. - Optimized tag column sorting and index management within the batch processing logic. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: ### Refactor `record_batch_to_rows` Function in `bulk_insert.rs` - Simplified the `record_batch_to_rows` function by removing the `logical_metadata` parameter and directly validating column types within the function. - Enhanced error handling for timestamp, value, and tag columns by checking their data types and providing detailed error messages. - Replaced the use of `Helper::try_into_vector` with direct downcasting to `TimestampMillisecondArray`, `Float64Array`, and `StringArray` for improved type safety and clarity. - Updated the construction of `api::v1::Rows` to directly handle null values and construct `api::v1::Value` objects accordingly. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: ## Commit Message Refactor `bulk_insert.rs` to optimize state access - Moved the state read operation inside a new block to limit its scope and improve code clarity. - Adjusted logic for processing `tag_columns` and `non_tag_indices` to work within the new block structure. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: ### Refactor `compute_tsid_array` Function - **Refactored `compute_tsid_array` function**: Modified the function signature to accept `tag_arrays` as a parameter instead of building it internally. This change affects the following files: - `src/metric-engine/src/batch_modifier.rs` - **Updated test cases**: Adjusted test cases to accommodate the new `compute_tsid_array` function signature by passing `tag_arrays` explicitly. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * docs: add doc for bulk_insert_region Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: ### Commit Message Refactor `bulk_insert.rs` in `metric-engine`: - Removed error handling for unsupported status codes in `write_data` method. - Eliminated `record_batch_to_rows` function, simplifying the data insertion process. - Streamlined the `write_data` method by removing fallback logic for unsupported operations. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: - **Optimize Primary Key Construction**: Refactored `modify_batch_sparse` in `batch_modifier.rs` to use `BinaryBuilder` for more efficient primary key construction. - **Add Fallback for Unsupported Bulk Inserts**: Updated `bulk_insert.rs` to handle unsupported bulk inserts by converting record batches to rows and using `RegionPutRequest`. - **Implement Record Batch to Rows Conversion**: Added `record_batch_to_rows` function in `bulk_insert.rs` to convert `RecordBatch` to `api::v1::Rows` for fallback operations. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: Add test for handling null values in `record_batch_to_rows` - Added a new test `test_record_batch_to_rows_with_null_values` in `bulk_insert.rs` to verify the handling of null values in the `record_batch_to_rows` function. - The test checks the conversion of a `RecordBatch` with null values in various fields to ensure correct row creation and schema handling. Signed-off-by: Lei, HUANG <mrsatangel@gmail.com> * feat/metric-engine-bulk-insert: Add fallback path for unsupported status and improve error context handling - **`bulk_insert.rs`**: - Added a fallback path for `PartitionTreeMemtable` in case of unsupported status code. - Enhanced error handling by using `with_context` for better error messages when timestamp and value columns are not found in `RecordBatch`. 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)