discord9
abc5868073
`native/wire.rs` encoded `NativeTree` by hand: fixed node tags, fixed field layouts, both directions written out, and a `check_shape` that narrowed the transport to `Input -> Instant|Window -> Map(Math*)`. The serialization layer therefore decided which plans could exist. `abs(ceil(x))` compiled but could not be transported, and every new node kind cost a second codec path plus a new whitelist entry. Widening the whitelist to nested maps maintained that interpreter rather than removing it. The file is deleted. The plan types derive serde and the codec is postcard 1.1.3 -- bincode is unusable here because RUSTSEC-2025-0141 marks the whole crate unmaintained with no patched version. postcard is maintained, has a documented stable wire format since 1.0, and writes an f64 as its bit pattern in eight little-endian bytes, so NaN payloads, signed zero, infinities and subnormals survive exactly. What remains hand-written is framing only: magic `NPRQ`, a version, a payload length. The version is rejected before the payload is deserialized, so an unknown version is a clean error instead of a silent misparse, and oversize, trailing bytes and truncation are separate errors. Decoding ends at `NativeTree::try_new`, so a tree the compiler would not build cannot be decoded. Three types keep written impls because they hold invariants. `EvalGrid` serializes its `GridSpec` and rebuilds through `try_deferred` with an empty cache -- the grid is never serialized and never materialized on decode. `SeriesLabels` rebuilds through `try_new`, keeping the sorting and the duplicate / `__name__`-as-a-tag rejection. `LabelRewrite` travels as its parameters with the regex recompiled, and fails to decode if the pattern no longer compiles; compiled internals never travel. `FusedSeries` now carries its output schema and accepts `PlainRows` and `partial_fold`, which it used to refuse. A carried schema is only honest if it is checked, so decode validates every column role, the exact time type and nullability, missing or extra value columns, and the scalar-root tag rule; a `SeriesFrame` schema must equal the frame layout the tree derives, and a partial export must match `state_schema(binding.tags)` including tag order. Identity stops being display text. `FusedSeries` compared `tree.to_string()` with a field patch, `AggregateFinalize` hashed the rendering, and `LabelRewrite` compared its own output; all three are structural now, with floats compared by `to_bits()` so that signed zero and NaN payloads stay reflexive under `Eq`. `same_binding` compared `Arc::ptr_eq`, which put an allocation address into a transportable plan; it compares the ordered fields and the optional grid spec now, distinguishing raw from evaluated. Tests assert the properties rather than the byte layout: a non-canonical NaN payload round-trips by bits, an unknown version is rejected before decode even when the payload is garbage, non-prefix trees (binary, set, aggregate, scalar, evaluated input) round-trip, thirteen malformed schemas are each rejected with their own message, and a multi-chunk chain over a real region is checked against its exact sample count and contiguous timestamps. The multi-partition integration test is split in two and labelled honestly. It used to assert a failure, on the theory that coalescing interleaved multi-chunk chains; its fixture's grid has one position, so no chain has more than one chunk and that assertion was already failing at the base commit. One test now covers the single-position shape and says it does not exercise interleaving; the other covers a 8192-position grid that does. Deserialization is bounded by depth, not only by bytes. The derived recursion walks boxed nodes, and a payload under the 1 MiB limit can nest far deeper than the stack allows, which aborts the process instead of returning an error. A thread-local budget guards `NativeNode`'s deserializer, so a hostile payload is rejected before a complete tree exists. `NativeTree::Deserialize` also routes through `try_new`, so no path can materialize a tree the constructor would refuse. Measured under postcard with no `RUST_MIN_STACK` override: 128 nodes decode, 129 return a decode error. Postcard collapses custom serde errors to its unit variant, so that rejection does not name the nesting limit; the other envelope failures (version, size, trailing bytes, truncation) each keep their own message. 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.