dennis zhuang
12e07cf727
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 for `GridSeriesBuilder` and the test reference; review only `feat(promql): evaluate time, date and label rewriting functions over grid series`. ## What's changed and what's your intention? ### Problem The native path has no kernels for `time()`, the date functions (`minute`, `hour`, `day_of_month`, `day_of_week`, `day_of_year`, `days_in_month`, `month`, `year`) or `label_replace`/`label_join`. ### Change - `map_time::time` and `map_time::date_of_eval_time` produce one unlabelled series over the schema's grid from the evaluation timestamps (`ms / 1000` for `time()`, whole seconds truncated towards zero for the date functions, as Prometheus `funcTime`/`dateWrapper`). - `map_time::map_date` reads every float value as Unix seconds and drops `__name__`, `__type__` and `__unit__`. The value goes through Go's `int64(f)` as on amd64 (NaN and out-of-range values become `i64::MIN`) and a port of Go's `time` absolute-date arithmetic, including its wrapping for instants Go cannot represent, which decides the result for those inputs. All-float series are mapped chunk by chunk without copying the time axis; histogram samples are dropped and the series is trimmed again. - `map_label::label_replace` anchors the regex as `^(?s:...)$`, expands `$1`/`${name}`/`$$` with Go's `regexp.Expand` rules, and leaves unmatched series unchanged. `map_label::label_join` joins source values, an absent label joining as `""`. An empty result removes the label in both. Empty source or destination label names and invalid regexes are `Plan` errors with Prometheus' messages. - `grid_series::merge_same_labelset` follows Prometheus `mergeSeriesWithSameLabelset`: series that share a label set (empty values counting as absent) become one series, merged field by field; a field with values from two of them at one point fails with "vector cannot contain metrics with the same labelset". The label functions call it; `map_date` leaves it to the caller, as other per-series kernels that drop `__name__` do. - `promql` now depends on the workspace `regex` crate, which is already in the build graph. Review focus: - `map_time::DateFunction::apply` and `civil`: the port of Go's `absSeconds`/`absDays` arithmetic; `days_in_month` uses the proleptic Gregorian leap rule on the computed year. - `map_time::go_int64`: NaN and values outside `[-2^63, 2^63)` map to `i64::MIN`. - `merge_same_labelset`: the fast path returns the input untouched when no label sets collide; colliding groups are rebuilt row by row and fail on any shared point. - `label_replace` expands its template with Go's `regexp.Expand` rules: a reference with a leading zero (`$01`) or of more than nine digits names a group, unlike `regex::Captures::expand`. ### Non-goals - `timestamp()` (EVAL-TS), which needs sample timestamps from instant selection. - Merging histogram series that collide after a label function (`NotImplemented`). - Planner wiring. ### Constraints - Every per-series kernel that drops `__name__` (range functions, date functions, math) can leave two series with one label set. Prometheus merges or rejects them per step; on the native path that is `merge_same_labelset`, which the caller must apply after such kernels. - Regexes compile with the Rust `regex` crate, as the ordinary planner does. Perl classes (`\d`, `\w`, `\s`, `\b`) are Unicode-aware there and ASCII-only in Go's RE2, so non-ASCII label values can match differently. The dialects also differ in what compiles: `\Q...\E` is rejected, `(?x)` and repeat counts above 1000 are accepted, and `\<`, `\>` are word boundaries instead of literal characters. The rustdoc of `label_replace` lists the same differences. - `go_int64` follows amd64. On arm64 Go saturates (`NaN` becomes 0), so Prometheus' own results for non-finite inputs differ by architecture. ### Verification - `cargo nextest run -p promql`: 315 passed. - `cargo clippy -p promql --all-targets --all-features -- -D warnings`, `cargo fmt --check`, `hawkeye check`: clean. - `time_functions_match_prometheus`: 200 fixtures produced by the Prometheus v3.15.0 engine built for amd64 (`map_time/prometheus_cases.txt`): `time()` and argument-less date functions over grids from 1685 to 2254, and date functions over values including NaN, ±Inf, ±1e300, ±9.2e18, leap-day edges of 1600/1900/2000/2100/2400 and years 0 and 10000. Values compare bit for bit after `merge_same_labelset`; 6 cases expect the duplicate-labelset error. - `label_functions_match_prometheus`: 200 fixtures (`map_label/prometheus_cases.txt`) with random destinations including `__name__`, absent sources, templates with valid and invalid `$` references, anchoring-sensitive regexes and invalid ones; 25 expect errors, the rest compare label sets and value bits. - Mutation check: each of these fails a test: Go's NaN and out-of-range conversion replaced with a saturating cast or NaN-to-0; floor instead of truncating division of the evaluation time; whole-second `time()`; dropping the 400-year rule; leap day counted in January and February; hour from the Unix seconds instead of Go's absolute seconds; no leap February in `days_in_month`; leap year taken from the March-based year; keeping `__name__`; mapping only the first chunk; regex without the `(?s:...)` group or without anchors; setting an empty label instead of removing it; skipping absent `label_join` sources; skipping the merge; skipping the duplicate check; comparing empty label values as present. - `replacements_expand_like_go`: 16 templates with expectations taken from Go, including `$1²`, where Go's Unicode classes end the name before `²`. - `merge_combines_disjoint_fields_at_one_point`: different fields at one point merge, one field present twice fails. ## 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.