Weny Xu
ebb7e22566
* ci: gate the integration CI long tail by diff scope `integration.yml` starts its long tail on every pull request: the three fuzz families, the chaos legs, the export/import E2E job and the two sqlness KvBackend legs. Most diffs cannot affect them, so a `scope` job now classifies the pull request and tells those jobs whether to start. The classifier is deterministic and fail-open. Forced modes (merge queue, `schedule`, `workflow_dispatch`, `ci:full`, `CI_SCOPE_FORCE_ALL`) and three floors -- always-run paths, an uncovered `src/` change, and a PromQL/flow filter -- can only widen the result; everything else is a glob match against the suite catalog. Every step of the job is `continue-on-error` and every gate reads `needs.scope.outputs.<key> != 'false'`, so a classifier crash runs the jobs instead of skipping them. The rules are checked out from the pull request base, so a pull request cannot relax its own gate. The policy is data: `.github/ci/scope-rules.toml` holds the path groups, the suite catalog and the always-run list, with a `reason` on every exclusion. `ci_scope_rules.py` loads it with the stdlib `tomllib` and validates the shape (unknown or cyclic `@group` references, a `family` that disagrees with the suite id, an empty reason) and that every glob's literal prefix still exists, so a renamed crate cannot leave a rule that matches nothing. `make check-ci-scope` runs that check locally; the `CI Scope Rules` job runs it on `ubuntu-slim`, the same image the classifier uses. `.github/ci/scope-cases.toml` holds golden cases that `test_ci_scope.py` replays through the real classifier. The PostgreSQL and MySQL sqlness legs move into their own `sqlness-kvbackend` job so they can be gated: a job-level `if:` cannot read the matrix, and gating them inside `sqlness` would skip required checks and block the merge. They run the same cases as the required Basic leg, so only the backend itself matters. The verdict is visible where triage looks: one log line per suite, the exact gate values, `::warning::` when a lookup fails and the gate widens, and a job summary that lists each suite with the rule that decided it and the changed files that matched. Measured on 200 merged and open pull requests: 27.5% always run, 41% decided by path rules, 31.5% skip every gated job; 995 of 2600 suite selections are skipped, with no selection that a path rule makes. The classifier itself runs in about ten seconds on a single-vCPU runner. Signed-off-by: WenyXu <wenymedia@gmail.com> * fix(ci): let the scope job read pull requests The `scope` job declared `permissions: contents: read`, and an explicit block sets every unlisted permission to `none`. `GET /pulls/{n}` therefore answered 403, `build_plan` widened to a full run as designed, and every gate read an absent output as "run": the gate could never skip anything. - `pull-requests: read` on the `scope` job. - `tests/runner/**` joins `@kv-backend`: the runner implements `--setup-pg`/`--setup-mysql`, which only the KV-backend legs pass, so a change there must not skip the only job that exercises it. The `UNMAPPED_CRATES` excuse is gone with it. - The README no longer claims a queue failure blocks the merge: the gated jobs are not required checks. Signed-off-by: WenyXu <wenymedia@gmail.com> * fix(ci): run every suite when the kind registry script changes The distributed and chaos fuzz jobs start their cluster through `.github/actions/setup-kind`, which runs `.github/scripts/kind-with-registry.sh`. `.github/actions/**` is in `ALWAYS_RUN` but the script is not, so a change confined to it selected no suite at all and every gate stayed `false`: the jobs that exercise the changed cluster setup were skipped. The script joins `ALWAYS_RUN`, consistent with the action that calls it, and a golden case pins it. Signed-off-by: WenyXu <wenymedia@gmail.com> * fix(ci): widen the gate for the fuzz corpus seeding script `fuzz-test/action.yaml` runs `.github/scripts/prepare-fuzz-corpus.sh` before the cache restore in every fuzz job, but the script was not in `ALWAYS_RUN`. A change confined to it therefore selected no suite: `any_fuzz` stayed `false` and every fuzz job that runs it was skipped. The script joins `ALWAYS_RUN` next to the other scripts the fuzz jobs execute, with a golden case for it. Both misses so far (this one and `kind-with-registry.sh`) came through a composite action, which the workflow never names. `WorkflowContractTests` now resolves `uses: ./.github/actions/<name>` transitively for the gated jobs and fails when one of the scripts they run is not in `ALWAYS_RUN`. Signed-off-by: WenyXu <wenymedia@gmail.com> --------- Signed-off-by: WenyXu <wenymedia@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 · 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.