discord9
f235434b7f
* feat(promql): compile nested expressions instead of fixed query shapes The native compiler matched three shapes: a bare selector, `range_fn(x[r])`, and `range_fn(n, x[r])`. Every composition of those kernels was refused, so `abs(rate(x[5m]))` and `clamp_min(rate(x[5m]), 0)` had no path through it even though the kernels, the `Map` node and the `Sum` node already existed. `compile` is now a thin entry over one recursive rule, `compile_expr`: a parenthesis compiles its inner expression, a vector selector becomes the instant selection it already was, a call dispatches on its signature, and anything else is refused naming what was found. Composition follows from that rule rather than from a list of accepted query shapes -- `abs(rate(x[5m]))` is a map over whatever its argument compiles to, and `clamp_min(abs(rate(x[5m])), 0)` is two maps over one window because the inner call compiles once. Math functions lower onto the node that already computes them: one `Map` carrying one `MathOp`, over the node its vector argument compiles to. The 28 names and their parameters are one explicit table, so supporting another is a decision made in one place; a name outside it is refused as having no native kernel rather than silently treated as math. `round` without an argument binds the kernel's documented default. A scalar parameter is folded from the statement: number literals, parentheses, unary negation, and arithmetic whose two sides both fold. The operator goes through `ArithmeticOp::calculate`, the same function the kernels use, so `%`, `^` and `atan2` keep Prometheus' semantics instead of being redefined in the compiler -- `(-7) % 3` is `-1`, signed as Go's `math.Mod` is. A parameter the statement cannot decide, `time()` being the real case, is refused rather than bound to a value that would be wrong at every point but one. `ArithmeticOp::calculate` widens from `pub(crate)` to `pub` for the fold; its body is unchanged. Nothing else is added: the plan nodes, the kernels and the tree validation are the ones that were there, and validation still happens once per statement, at the root. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * fix(promql): match the arithmetic token by its id, not by its text `arithmetic_op` read the operator through the token's `Display` output. That is a second source of truth for what an operator is: the id is stable and is what the rest of the planner matches on (`prom_token_to_binary_expr_builder`), while the rendered text is a presentation detail. A formatting change would silently stop `%`, `^` and `atan2` from folding -- not wrong, but quietly less than it says it does. Also corrects the doc comment that claimed the parser keeps its token ids to itself. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * fix(promql): say what the validator compares, and make the fold test bite The map's doc claimed `NativeTree::validate` compares the map's output grid with its input's by identity. It compares them by `EvalGrid::spec`, and only an input node's output is compared by identity, so a map that allocated an equal grid of its own would validate the same way. The comment says that now, and the two assertions in the nested-map test compare spec and field names instead of `Arc::ptr_eq`, which failed a map that allocated an equal grid for no behavioural reason. The constant-fold test could not catch what its doc said it guarded: `go_math`'s modulo is `x % y` and its power agrees with `f64::powf` on `2 ^ 10`, so replacing the kernel arithmetic with a hand-written match still passed. The power case is now `1.0000006850274261 ^ 9.80019037e8`, where Go's repeated squaring accumulates error the standard library's `pow` does not, and where the two disagree above the last bit. Replacing the fold with `%`/`powf` fails it. `pi()` and `time()` have no vector to be pointwise over, so the "no arguments" branch explained them wrongly; without it they fall to "has no native kernel", which is accurate, and a listed math name called with no argument at all is covered by the signature's arity check. `math_op` now reads the name first and returns the vector it found, so both rejections come from one place. `argument_position` indexes a fixed array: its three callers pass literals, so there is no fourth position to default to. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * fix(promql): refuse an interleaved scan at the compiler entry Every accepted shape reads one series to its last chunk before the next, so the ordering has to be refused before the statement's shape is dispatched on; the check sat in the range function branch, which let the bare selector and a math function over it compile under Interleaved. The window branch of NativeTree::validate stays as the backstop for a tree built another way. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> --------- Signed-off-by: discord9 <55937128+discord9@users.noreply.github.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.