discord9
23afa9697e
* feat(query): place the canonical merge above the regions
A canonical producer merges the pieces of one label set that its own input
partitions hold, and the canonical router FusedSeries::to_physical_plan inserts
above it collects them from exactly those partitions. In process the producer
reads the region's own scans, so the region holds every piece its router has to
see and that placement is the only one it has.
Over the wire the producer reads RawSeriesSource children instead - the wrapper
that reconciles fragments several regions hold. Classifying that shape
commutative shipped producer -> router as the region sub-plan, one producer and
one router per region, each collecting only its own region's pieces, so a label
set a time partition spread over several regions was answered from one region's
share. Classify a canonical producer by its own contract only: commutative when
every child is a bare scan, non-commutative otherwise, and never fall through to
the per-series rules, which describe what a node computes and say nothing about
where the pieces of one label set are. The analyzer then cuts at the raw source,
so the frontend holds
consumer -> SeriesRepartitionExec(CanonicalLabels) -> producer
-> [raw router] -> MergeScan(RawSeriesSource(TableScan))
and the canonical router collects each label set from the producers of every
region, while the fragments of each series are still reconciled below the
producer by the raw-primary-key router the interleaved raw input asks for.
Admission follows the placement: the in-process route still declines a canonical
merge over a partitioned table, because a region-local producer is the only
placement it has, and the remote route now admits it. Acceptance is a real
two-datanode test over a three-partition metric table: the rows equal an
unpartitioned control's, the producer and its canonical router are frontend
nodes above the merge scans, and a collision two regions hold reports the
Prometheus duplicate-labelset error that no single region can see.
Signed-off-by: discord9 <discord9@163.com>
* test(promql): partition the canonical acceptance table on a tag
GreptimeDB has no implicit time partitioning: a table has more than one
region only from a declared partition, and metric tables are normally
partitioned on a tag. The acceptance fixture partitioned on the time index
instead, which made the test read as if time partitioning were the everyday
case. Partition on `host` in three ranges - `early`/`first`/`late`/`second`
still land in different regions, so the merge pair and the collision pair keep
their cross-region geometry - and rename the test accordingly.
A partition on a primary-key column that excludes the time index also proves
the regions hold disjoint series, so the two assertions that described
fragmented series state what this fixture actually proves instead: the merge
scan publishes whole series (`contiguous`), and no raw-key repartition sits
below the frontend producer because only the label set crosses regions.
Signed-off-by: discord9 <discord9@163.com>
* refactor(query): ask the transport where a canonical merge runs
`canonical_boundary_region_node` inferred "this execution reads the whole
table" from the node's children: a canonical producer whose children were all
bare scans was admitted inside a region sub-plan. Over the wire every selector
input is a `RawSeriesSource`, so the inference happened to agree with the rule
that matters, but a wire plan that ever held a bare scan under a producer would
have been admitted silently and answered a label set from one region's share.
The analyzer already holds the fact. `in_process_region_transport` is what it
was built with, and it is exactly the condition under which a region read is
the whole table. Pass it down to `Categorizer::check_plan` and
`check_extension_plan` and require it instead of inspecting child shapes.
Guard the hole the inference hid: a canonical producer over a bare table scan
classifies `Commutative` in process and `NonCommutative` on the wire.
Signed-off-by: discord9 <discord9@163.com>
* test(promql): cover a fragmented canonical merge too
A partition on a primary-key column proves the regions hold disjoint series,
so the tag-partitioned fixture could not exercise the other geometry the
frontend merge has to read: fragments. In a metric table a tag is always a
primary-key column and a field must be Float64, so the only declared partition
that proves nothing about where a series lives is the time index.
Run the same two fixture rows over both geometries in one loop. The tag case
still publishes whole series and has no router below the producer; the time
case declares interleaved and asserts exactly one RawPrimaryKey reconciling
router between the merge scans and the frontend producer. Both keep the
unpartitioned control's rows, the frontend placement of the producer and its
canonical router, and the cross-region duplicate-labelset error.
Signed-off-by: discord9 <discord9@163.com>
* Revert "test(promql): cover a fragmented canonical merge too"
This reverts commit 8ff9f43031.
Signed-off-by: discord9 <discord9@163.com>
---------
Signed-off-by: discord9 <discord9@163.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.