dennis zhuang
4607c8f5bd
I hereby agree to the terms of the [GreptimeDB CLA](https://github.com/GreptimeTeam/.github/blob/main/CLA.md). ## Refer to a related PR or issue link (optional) Targets `narthex`. Stacked on #9673; review the commits after #9673's head. This PR also carries the `*_over_time` functions from the closed #9675. ## What's changed and what's your intention? ### Problem `RangeEvaluator` from #9673 needs window kernels for the float range functions PromQL defines. ### Change `range_eval::RangeFunction` lists the supported functions; `RangeEvaluator::try_new` takes one, and `FloatWindow`, `WindowKernel` and `with_kernel` are `pub(crate)`: - Rate family: `Rate`, `Increase`, `Delta`, `Irate`, `Idelta`. - Over time: `AvgOverTime`, `MinOverTime`, `MaxOverTime`, `SumOverTime`, `CountOverTime`, `LastOverTime`, `PresentOverTime`, `StddevOverTime`, `StdvarOverTime`, `QuantileOverTime(f64)`. - Change: `Resets`, `Changes`, `Deriv`, `PredictLinear(f64)`, `DoubleExponentialSmoothing { smoothing, trend }`. Parameters are constants. Each kernel follows the Prometheus function of the same name (`promql/functions.go`, v3.15.0), including the order of floating point operations. Arithmetic where GreptimeDB's range UDFs already agree with Prometheus is shared with them: `add_counter_resets`, `instant_delta`, `extremum` (moved out of `scan_extremum`) and `count_edges` (now taking a slice). Those UDFs keep their results. `double_exponential_smoothing` factors outside `(0, 1)` fail with `Execution` at the first window that holds a sample, where Prometheus checks them; input with no sample in any window evaluates to nothing. `predict_linear` regresses around `eval_ts + offset`, Prometheus' evaluation time on the shifted timeline, and `try_new` rejects a grid where that overflows `i64`. Review focus: - `range_eval/function.rs`: each kernel against its Prometheus counterpart, in particular `kahan_inc` (`kahansum.Inc`, which resets the compensation on infinity), `avg`, `variance`, `quantile`, `extrapolated_rate` and `linear_regression`. - `functions/aggr_over_time.rs`, `edge_count.rs`: the moved code keeps the same expression sequence. - Results drop `__name__`, `__type__` and `__unit__` (Prometheus `DropReserved(schema.IsMetadataLabel)`), except for `last_over_time`; the metric was passed through unchanged before. ### Non-goals - Histogram and mixed samples, `absent_over_time`, non-constant function parameters, planner or executor wiring. - A reset index for heavily overlapping `rate` windows. ### Constraints Where the ordinary range UDFs differ from Prometheus, the kernels follow Prometheus: - `rate`/`increase`/`delta` extrapolate in seconds as Prometheus does; the UDF computes the ratio in milliseconds. The two differ in the last bits. - `sum_over_time` and `avg_over_time` use Kahan summation (and the incremental mean on overflow for `avg`); the UDFs use Arrow's lane-wise sum. - `stdvar_over_time` uses the compensated variance `stddev_over_time` uses; the UDF uses an uncompensated one. - `quantile_over_time` sorts NaN first; the UDF sorts by `total_cmp`, which puts positive NaN last. - `deriv`/`predict_linear` return NaN for a constant infinite window, which the UDFs drop. `predict_linear` takes a fractional horizon, which the planner truncates to `Int64`. - `double_exponential_smoothing` computes two-sample windows, returns nothing for one sample or an empty window, and rejects factors outside `(0, 1)`. The UDF returns NaN in the first three cases and maps out-of-range factors to ±Inf. - Every kernel reads only each field's valid samples, so the UDFs' dependence on where null slots sit (`deriv`'s intercept, smoothing over null payloads, lane-wise sums) does not carry over. ### Verification - `cargo nextest run -p promql`: 314 passed, including the existing tests of every UDF touched. - `cargo clippy -p promql --all-targets --all-features -- -D warnings`, `cargo fmt --check`, `hawkeye check`: clean. - `range_functions_match_prometheus`: 400 cases (20 per function, 1,183 result points) from the Prometheus v3.15.0 engine built for amd64, over random single series with counters, gauges with special values, constants, large magnitudes, wide spreads, NaN-heavy windows and stale markers, random ranges and offsets. Values compare by bits, NaN by being NaN. The fixture is `range_eval/prometheus_cases.txt`. - `functions_match_range_udfs`: the nine functions whose UDF agrees with Prometheus, 300 random seeds each against the UDFs on brute-force windows, by bits. - `cargo sqlness bare` over the 34 case names that use these functions: 78 cases passed across standalone and distributed (run before the UDF code was narrowed back to the four shared helpers). - Mutation check: Kahan without the infinity reset, `avg` as a plain mean, uncompensated variance, NaN sorted last, millisecond extrapolation, dropping NaN slopes, truncating the `predict_linear` horizon and requiring three samples for smoothing each fail `range_functions_match_prometheus`; faults in the rate, `irate`, extremum and edge counting kernels fail the UDF comparison. - `range_functions_drop_metadata_labels`: `rate` drops the metadata labels and `last_over_time` keeps them. - `quantile_keeps_the_window_order_of_signed_zeros`: `quantile_over_time` sorts as Go does, +0 and -0 equal and in window order, so `1 / quantile_over_time(1, m[3s])` over +0, -0, -0 is -Inf. ## PR Checklist Please convert it to a draft if some of the following conditions are not met. - [x] I have written the necessary rustdoc comments. - [x] I have added the necessary unit tests and integration tests. - [ ] This PR requires documentation updates. - [x] API changes are backward compatible. - [x] Schema or data changes are backward compatible. - [ ] This PR needs to be backported to release branches, and I have added the `backport-<target>` labels (e.g. `backport-v1.3` targets `release/v1.3`).
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 · Slack
stable for production · canary includes pre-releases · nightly is a weekly snapshot of main
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.
One Query Across Signals
OpenTelemetry ingestion writes spans to opentelemetry_traces and log records to
opentelemetry_logs. Both tables carry trace_id, so correlating them is a join:
-- The slowest failed spans in the last hour,
-- with the log lines emitted inside those same traces.
SELECT
t.service_name,
t.span_name,
t.duration_nano / 1000000 AS duration_ms,
l.timestamp AS log_time,
l.severity_text,
l.body
FROM opentelemetry_traces t
JOIN opentelemetry_logs l ON l.trace_id = t.trace_id
WHERE t.timestamp > now() - INTERVAL '1' HOUR
AND t.span_status_code = 'STATUS_CODE_ERROR'
ORDER BY t.duration_nano DESC
LIMIT 20;
Metrics join the same way, on any tag the tables share, such as service,
host, or pod.
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 are hitting Loki's query performance limits as log volume grows
- 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
Learn more in Why GreptimeDB.
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.
Benchmarks
- Agent RCA Bench: LLM agents doing root cause analysis over GreptimeDB versus Prometheus + Loki + Tempo. 40% fewer wrong diagnoses, 48% fewer input tokens, 45% lower cost (write-up)
- GreptimeDB tops JSONBench's billion-record cold run test
- TSBS Benchmark
- More benchmark reports
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 |
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 — stable, 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.
Release lines and support windows are in the version reference. For where the project is going, read the v1.0 highlights and the 2026 roadmap.
Community
We invite you to engage and contribute!
If GreptimeDB is useful to you, please star the repo.
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.