dennis zhuang
0f625a7e92
* fix(promql): correct counter reset accumulation in rate windows `prom_rate` and `prom_increase` reused the previous window's counter-reset correction when the next window slid forward by exactly one sample, adding the entering reset and subtracting the leaving one. Running a sum through addition and subtraction does not restore the earlier terms in f64: a large reset absorbs the smaller ones that must survive it, and an expired infinity leaves a NaN that no later window can clear. `prom_delta` shares the code but is not a counter function, so it never took that path. Index the reset positions of the value array once instead, and reduce each window over the resets it contains, in sample order. The result is bit-identical to scanning the window directly, so windows keep the direct reduction when they request fewer sample pairs than the input has. Also sweep the query step in the rate benchmarks: the cost of the reset correction depends on how much the windows overlap, which no existing case varied. Signed-off-by: Dennis Zhuang <xzhuang@greptime.com> * test(promql): cover counter reset precision over adjacent rate windows The unit tests build the range windows directly, so they do not show that a plain PromQL range query produces the window layout that lost the correction. This case does: with a query step equal to the sample interval, `increase` over the second window returns 1.333 before the fix and 2.667 after it. Signed-off-by: Dennis Zhuang <xzhuang@greptime.com> * perf(promql): advance the counter reset bounds instead of searching Locating a window's resets with two binary searches costs more than the reduction it replaces once a series resets often enough for the searches to get deep: on a 20k-sample counter resetting every 37 samples, stepping the windows by one sample was 2.7x slower than the previous code, against 1.2x for a counter that never resets. Windows normally advance, so walk the bounds forward from the previous window and only search when they move back. The cost then no longer depends on the reset density. Signed-off-by: Dennis Zhuang <xzhuang@greptime.com> * perf(promql): cut the per-window cost of the counter reset index Two costs the index added showed up on a one-sample query step, where the removed fast path used to answer each window with two comparisons. Cache the two reset positions that bound the active slice. A window that only advanced and reached neither of them covers the same resets as the previous one, so the common case is four integer comparisons and no lookup at all. Stop summing the requested sample pairs once they exceed one pass over the values. The sum only decides which side of that comparison the input falls on, and a query with a short lookback and a long step settles it after a few windows instead of after every key. Together these take the one-sample step from 25-32% slower than the previous code down to 6-11%, measured as before / after / before to bound drift. No other step value regresses, and a ten-sample step stays about 88% faster. Signed-off-by: Dennis Zhuang <xzhuang@greptime.com> * fix(promql): accumulate counter resets into the running result `prom_rate` and `prom_increase` summed a window's counter-reset corrections on their own and added that sum to `last - first`. Prometheus folds each reset into the running result instead, and so did this code before #7880. The two are not interchangeable in f64: over samples `[1e16, 1, 0, 1]` the isolated sum rounds `1e16 + 1.0` back to `1e16`, which then cancels against the first sample and reports no increase at all, where folding the resets in one at a time keeps the 1.0. Restore the original order. The reset index accumulates into the result the same way, so it still matches a direct scan of the window bit for bit, but a window's contribution can no longer be cached as a standalone value and is re-added from its own difference each time. The bounds are still cached, so a window that did not cross a reset skips the lookup, and one that holds no resets returns without touching the index at all. Signed-off-by: Dennis Zhuang <xzhuang@greptime.com> * test(promql): note which reset boundaries the stride of one walks Signed-off-by: Dennis Zhuang <xzhuang@greptime.com> --------- Signed-off-by: Dennis Zhuang <xzhuang@greptime.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.