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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) Replaces the serialization introduced by #10029 (merged into `narthex`); this replaces rather than layers on top of that change. ## What's changed and what's your intention? ### Summary This replaces the hand-written `NativeTree` byte codec and shape whitelist with derived serde serialization encoded by postcard. The versioned `FusedSeries` envelope now serializes a payload containing the native tree and its plan binding, including the output Arrow `Schema`; Substrait continues to carry children separately. The former explicit `NativeTree` codec is removed. The output schema is serialized directly using Arrow Schema's serde representation and postcard; it is not inferred from the tree on decode. The payload no longer transports the separate `output_tags`, `output_columns`, or `plain_rows_schema` representations. Carrying Arrow `Schema` directly preserves column order, schema and field metadata, and custom scalar column names through the round trip. Decode reconstructs a `FusedSeries` node and checks format-specific schema constraints: `SeriesFrame` must match the evaluated frame layout; `PlainRows` must pass output-schema classification and consistency checks; partial-fold output must match the expected state schema, including tag order. These checks cover the time column, tags, value columns, and scalar-root constraints. ### Why The old codec duplicated native node kinds and admitted only a hand-maintained set of tree shapes. That made transport capability diverge from what the compiler could build. Using serde over the tree avoids a separate per-variant wire codec and lets native plan validation continue to define legal trees. The envelope has a version and payload length. Decode checks the version before deserializing, rejects length/truncation/trailing-byte errors, and validates the decoded plan's binding and output representation. `NativeTree` construction checks nesting and validates the root; its custom `Deserialize` likewise checks nesting and validates the decoded root. `EvalGrid`, `SeriesLabels`, and `LabelRewrite` retain custom serde handling for their invariants (deferred grid reconstruction, label validation, and regex recompilation). Postcard preserves floating-point bit patterns; tests cover a non-canonical NaN payload, signed zero, infinities, and a subnormal. The dependency adds `cobs` and `embedded-io`; postcard default features are disabled. ### Structural equality and binding Native plan equality and hashing use structural content rather than display strings; float-bearing leaves use `f64::to_bits()`. Binding comparison uses ordered field specifications and optional evaluation-grid specifications rather than `Arc` pointer identity. The repeated `validate_binding` native-tree depth check was removed: checked construction and custom deserialization already enforce the tree-depth invariant. ### Depth safety The limit is a maximum **nesting depth of 64 native nodes**, not a total-node limit. Checked native-tree construction and custom deserialization enforce the boundary; deserialization also uses a depth guard while recursively reading nodes. Over-limit input is rejected, although postcard reports the custom serde failure as its unit `SerdeDeCustom` variant. In the combined admission test, 63 selectors fit within the depth limit; 64 are rejected by the native route, and a comparison against the legacy fallback passes. This is not a broad fallback guarantee: a measured 70-term `time()` expression overflows the stack on the legacy path. ### Known limitation: schema depth is not bounded on decode The native tree is depth-bounded while it deserializes. The carried Arrow `Schema` is not: `DataType` is independently recursive, and a payload whose schema field nests `List` thousands deep aborts the decoding thread before any validation runs. A reviewer reproduced this with a hand-built 80 KB payload on an 8 MiB thread. This schema cannot grow that way from any query. Production builds every `FusedSeries` at two call sites in `planner/native.rs`, both taking their schema from `fused_output_schema`, which hardcodes `Timestamp(Millisecond, None)`, `Float64` values and `Utf8` tags and never constructs a nested type. The partial-fold form uses the fixed `state_schema`, and the frame form is checked against `series_frame_output_schema`, a constant layout. Only a payload written outside this planner can deepen it, and anything able to supply that payload can supply any other plan. It is still a real difference from the tree, so it is recorded rather than glossed: this transport is not depth-safe against crafted input. Bounding it would mean a depth guard inside the schema's own `Deserialize`, not a second schema representation. ### Verification - Serde branch at `7e8f31ab1bc`: targeted `fused_series` tests plus the named-stack `NativeTree` lifecycle test, 77 passed (not the full `promql` suite). - Combined with the admission branch at `209cb81ddae`: query and promql suites, 2,099 passed and 2 skipped, on the default stack. - The depth-fallback regression uses 64 selector terms: 63 fit the limit, 64 are rejected with `NotImplemented` and fall back to the legacy plan. It is run at `RUST_MIN_STACK=8388608` because the legacy planner, not the native route, overflows the default 8 MiB stack on a deep `time()` chain. - `PlanRewriter::new` now encodes the root once instead of twice; the `dist_plan` suite, 207 tests, passed after that change. - `cargo fmt --all -- --check` and `git diff --check`: passed. All Rust runs used a local `greptime-proto` path patch, which is why a `Cargo.lock` difference appears locally; it is not part of this PR. The earlier datatypes, integration, and clippy counts were not rerun at the current head and are not claimed as current verification. No `#10055` integration result is claimed. ## 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. - [ ] 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.