Lei, HUANG
c3ea022de5
* perf(mito2): optimize flat merge heap and primary-key interleave Replace the per-row BinaryHeap pop/push cycle in FlatMerge with an in-place root mutation plus a single sift-down repair on a custom RootHeap, keeping the cold heap and direct-batch fast path unchanged. Fallible or awaiting batch transitions move the hot node out of the heap first, preserving error and cancellation semantics. Exploit the globally sorted merge output to build the internal Dictionary<UInt32, Binary> primary-key column with a one-pass ordered gather: append a Binary value only when the PK changes and reuse the current key for adjacent equal PKs, bypassing Arrow dictionary masks, hash interning and key remapping. Non-PK columns still use Arrow interleave. Also cache the current primary-key byte range in RowCursor to avoid repeated dictionary range decoding during comparisons, and add a setup-free Criterion benchmark with exact output-row assertions. 32-way/1 row-per-series/40-tag improves 955.79ms -> 562.36ms (-41.2%); 0-tag -39.5%, 64 rows/series -79.8%, 8-way -56.1%, single-iterator control +0.3%. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * test(mito2): add rows-per-series sweep to flat merge bench Add 32-way/40-tag shapes for 1, 10, 100, 1000 and 10000 rows per series, and allow FLAT_MERGE_BENCH_SHAPE to match shape name prefixes so the whole sweep can run in one invocation. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * test(mito2): add oracle-based correctness tests for RootHeap Drive RootHeap and a std BinaryHeap oracle with the same seeded op sequence (push / pop / mutate-root + repair) and assert peek, len, best_child and the full drain order after every operation. A second run with a tiny value range makes duplicates dominate, covering the equal-key branches of sift_up/sift_down and best_child. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * perf(mito2): replace hot heap with a tournament tree in flat merge Replace the hot RootHeap with a fixed-capacity tournament (winner) tree over per-node slots: every internal node caches the champion of its subtree, so advancing the winner only replays the ~log2(k) nodes on its leaf-to-root path with one compare per level, instead of the heap's two-compares-per-level sift that also re-compares the same node pairs on every row. Two fast paths keep dense shapes at O(1) per row: - champion retention: after mutating the winner in place, skip the replay entirely when it still beats the runner-up (its path caches are unchanged by construction); - a second-best slot cache, invalidated on any structural change, so the retention check costs a single compare without walking the tree. The cold heap, hot/cold overlap window, direct-batch fast path and the remove-before-fallible-fetch batch transition semantics are unchanged. Vs the RootHeap version: 1rps/32way/40tag -19.7%, 0tag -34.4%, 8way -15.9%, 64rps -30.5%, sweep 10/100/1000/10000rps -29~32%; vs the original BinaryHeap baseline the main shape is -52.8%. The single-iterator control is +8% (+50ns one-time construction allocation, no merge work). Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * fix(mito2): support generic schemas in flat merge Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * fix(mito2): satisfy clippy in flat merge benchmark Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * perf(mito2): cache flat merge primary key index Compute the internal primary-key column index once when constructing BatchBuilder and reuse it for every output batch. Preserve the column-name gate for generic schemas. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * test(mito2): benchmark high-fan-in flat merges Add sparse 64, 128, 256, and 512-way merge shapes while keeping the total input fixed at 3.2 million rows. Compared with the merge-base heap implementation, median time improves by 56.0%, 60.8%, 55.6%, and 56.1%, respectively. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> --------- Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
Metrics, logs, and traces.
One engine, on your infrastructure.
A columnar database for metrics, logs, and traces on object storage. Apache-2.0 licensed core.
User Guide | API Docs | Roadmap 2026
stable for production · canary includes pre-releases · nightly is a weekly snapshot of main
- Introduction
- Why You Might Use It
- Overview
- What's Supported
- Compatibility and Migration
- Limitations and Edition Boundary
- 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. Metrics, logs, and traces run on one columnar engine over object storage and share one table model: tags, timestamp, and fields. When signals carry common identifiers such as service, host, or trace ID, you can correlate them in SQL without moving data between databases.
Ingest through OpenTelemetry, Prometheus Remote Write, Loki Push, or Elasticsearch Bulk. Use SQL across observability data and PromQL for metrics. Migrate ingestion one signal at a time without rebuilding your collectors.
Why You Might Use It
- You run Prometheus plus Loki or Elasticsearch and want one backend instead of three
- You have outgrown Prometheus on cardinality or retention and don't want the Thanos/Mimir operational surface
- You need long retention on object storage without a separate analytics stack
- You want to query telemetry with SQL, not only a domain query language
- You are storing GenAI or agent telemetry (OTel GenAI conventions) alongside infrastructure signals
- You need the same engine and semantics on resource-constrained devices
Learn more in Why GreptimeDB.
Overview
A quick overview of what GreptimeDB ingests, how it connects to other systems, and what its distributed engine lets you do.
What's Supported
| Ingest | OpenTelemetry (OTLP), Prometheus Remote Write, Loki Push, Elasticsearch Bulk, InfluxDB line protocol, gRPC |
| Query | SQL, PromQL, Jaeger-compatible trace queries, MySQL and PostgreSQL wire protocols |
| Storage | S3, GCS, Azure Blob and S3-compatible endpoints as primary storage, with memory and local-disk caches |
| Built in | Retention policies, downsampling, continuous aggregation, explicit table partitioning, and inverted / skipping / fulltext indexes |
Compute and storage are disaggregated: object storage holds the data, while memory and local-disk caches keep recent and frequently queried data close to compute.
Compatibility and Migration
Compatibility is per protocol, and query-side coverage is narrower than ingestion.
| Compatible | Not compatible | |
|---|---|---|
| Prometheus | Remote Write ingestion; PromQL queries | Gaps are listed in PromQL compatibility |
| Loki | Push ingestion; dual-write through Grafana Alloy makes the cutover gradual | LogQL and the rest of the Loki query API |
| Elasticsearch | _bulk ingestion in the open-source core; QueryDSL partially, in Enterprise |
Most other Elasticsearch APIs |
Benchmarks:
Limitations and Edition Boundary
Cluster deployment, object storage, the Flow engine, and every ingestion protocol listed above are in the Apache-2.0 build. Repartitioning, region migration, and index creation are manual operations there.
Read replicas, workload isolation, and automated repartitioning are GreptimeDB Enterprise features, along with enterprise security and governance. The Enterprise overview has the current list, and pricing has the edition comparison.
Architecture
GreptimeDB can run in two modes:
- Standalone — single binary for development and small deployments.
- Distributed — four components, each independently scalable:
- Frontend — protocol entry (OTel, Prometheus, MySQL/PostgreSQL, gRPC, ingestion APIs for Elasticsearch/InfluxDB/Loki) and the distributed query engine. Stateless, scales horizontally.
- Datanode — region engine with WAL, memtable, SST, cache, compaction, and indexes. Persists data to object storage. Elastic.
- Metasrv — metadata, routing, repartitioning, and security. Backed by a pluggable KV layer (etcd or RDS).
- Flownode (optional) — continuous flow computation (streaming and materialized views).
For deeper coverage, see the architecture doc or DeepWiki.
Try GreptimeDB
For AI agents — paste this prompt into your agent:
Read https://docs.greptime.com/SKILL.md and follow the instructions
to deploy, configure, ingest, and query 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 \
--grpc-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, pinned by
rust-toolchain.toml - Protobuf compiler (>= 3.15)
- C/C++ building essentials:
gcc/g++/autoconfand the glibc dev package (libc6-devon Ubuntu,glibc-develon Fedora) - Python toolchain (optional, only for some test scripts)
Build and run:
make # build greptime binary
cargo run -- standalone start # start in standalone mode
Common dev commands:
make fmt # format Rust code
make clippy # lint (fails on warnings)
make test # unit + integration tests (uses cargo-nextest)
make sqlness-test # SQL regression tests
See the Contribution Guidelines for the full developer workflow.
Tools & Extensions
- Kubernetes: GreptimeDB Operator
- Helm Charts: Greptime Helm Charts
- Dashboard: Web UI
- gRPC Ingester: Go, Java, C++, Erlang, Rust, .NET, TypeScript
- Grafana Data Source: GreptimeDB Grafana data source plugin
- Grafana Dashboard: Official Dashboard for monitoring
Project Status
GreptimeDB is generally available, with stable APIs and regular releases. It runs in production at scale — OceanBase Cloud operates 80+ GreptimeDB clusters managing 300 TB of logs, cutting log storage cost by 60%+ after migrating from Grafana Loki. See more in case studies.
Read the v1.0 highlights and 2026 roadmap, or browse the version reference.
If GreptimeDB is useful to you, please star the repo.
Community
We invite you to engage and contribute!
License
GreptimeDB is an open-core project. Its core is licensed under the Apache License 2.0.
A small set of peripheral, enterprise-only features are gated behind the
enterprise Cargo feature (not built by default) and are governed by the
separate GreptimeDB Enterprise License. Source files under
that license carry an explicit Enterprise License header.
Commercial Support
Scaling observability on your infrastructure? GreptimeDB Enterprise adds the operational, security, and support layer for production deployments. 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 AUTHOR.md.
- Uses Apache Arrow™ (memory model)
- Apache Parquet™ (file storage)
- Apache DataFusion™ (query engine)
- Apache OpenDAL™ (data access abstraction)
All trademarks, logos, and brand names referenced in this README and in the Overview diagram are the property of their respective owners. Their use is for identification purposes only and does not imply endorsement or affiliation.