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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) Uses the selector requirements and independent binary compilation already merged in #9870 and #9871. This PR now targets `narthex` directly. ## What's changed and what's your intention? Run a compiled vector–vector expression from the real PromQL entry point with **two independent scans**, including different matchers and time bounds, instead of folding both operands onto one scan. - `FusedSeries` keeps `Vec<LogicalPlan>` children in source-index order. Each `BoundInput` names its child through `source_index`; its raw schema remains in the compiled tree rather than being duplicated in the binding. Logical identity includes source indices and ordered fields. - The planner prepares each selector in its own context, preserves its matcher filters, and adds its own compiler-derived scan bounds, including range lower-edge semantics, `@` anchors and offsets. The two scan children and source indices 0/1 remain distinct even for repeated selectors. - The physical adapter keeps the existing vector of `(physical plan, label source, ordering)` tuples. Each child's frames are decoded and labels materialized independently against that source's schema. A chunk goes only to the runtime input IDs allocated for its source. Input ends globally only after every source and partition has reached EOF; errors/drop release queued inputs and evaluator reservations. - Full raw tags remain available to bind scanners; one-to-one binary output projects labels according to its matching rule, so `on(host)` emits `host`, not an unrelated raw `kind` tag. - A narrow same-table pair can use the existing in-process region-local placement exception. Only that admitted root pair bypasses the unary column-requirement rewriter; the general multi-input and wire-encoding restrictions remain. This does not establish cross-region matching. ### Partitions and scope A single physical input retains **Scan N → Fused N**. With two inputs, **each scan retains N partitions**, while Fused reports one output partition and consumes each source's partitions sequentially with a separate decoder per partition. It does not pair left partition `i` with right partition `i`, and the gathered stream is treated as `Contiguous`, not globally `KeyThenTime`. This is an experimental, opt-in **standalone, single-region PoC** for ordinary Mito tables, millisecond timestamps, `Float64` fields, and same-table one-to-one arithmetic pairs with default matching or nonempty `on(...)`. It does not add cross-region routing, co-location, semantic series merging, Exchange, global label sets, group matching, or a general fallback-proof system. Metric-table pairs, comparisons, group matching, empty `on()`, aggregates and subqueries are outside this pair admission; existing single-selector admission is preserved. The experimental internal Rust plan interface changes from a single child to vectors; persisted schemas and wire formats do not change. No release backport or official documentation-site change is required for this default-off experimental extension. ### Verification Completed local checks: - `make fmt-check` and source-only `git diff --check`: passed. The sqlness runner-owned `.result` EOF blank line is excluded from the source whitespace check, not manually trimmed. - `cargo check -p promql -p query --all-targets`: passed. - `cargo nextest run -p promql -p query --retries 0 --no-fail-fast`: **1,897 passed, 2 skipped**. - `cargo clippy -p promql -p query --all-targets --all-features -- -D warnings`: passed. - `cargo check -p tests-integration --all-targets`: passed. - `cargo nextest run -p tests-integration --lib --retries 0 --no-fail-fast -E 'test(native_promql)'`: **15 passed, 251 filtered out**. - `cargo clippy -p tests-integration --all-targets --all-features -- -D warnings`: passed. The direct integration regression reads 4,608 hosts from two distinct `kind` matchers. It verifies the entire instant subtraction result (`-915` per host) and independent anchored/offset rate result (`-3` per host) against explicit numeric expectations and the legacy planner. Both real scan children have four partitions and each produces rows on multiple partitions; Fused has one output partition. Source indices, host-only output labels, and each window's raw input-sample count are asserted. Separate adapter tests cover distinct raw schemas, unequal partition counts, later keys in earlier partitions, empty inputs, ordering refusal and failure/drop cleanup. Single-child shared-input tests retain Scan N → Fused N and once-per-partition scan execution. - `cargo build -p cmd --bin greptime -p sqlness-runner`: passed; the SQL run used that freshly built binary. - `sqlness-runner bare -c "$CASE_DIR" -t fused_multi_standalone --bins-dir "$CARGO_TARGET_DIR/debug" -j1` (`CASE_DIR` selects only the checked-in standalone fixture): regenerated `.result` through the runner, then reran successfully with an unchanged artifact hash. All **13 TQL queries / 35 result rows** were checked independently for values, timestamps and labels, including matcher subtraction `-45/-90` and independently anchored rate subtraction `-9/-18`. This SQL case is a public-semantics regression, not evidence of native routing or partition activity; the direct integration test provides that evidence. ## PR Checklist - [x] I have written the necessary rustdoc comments. - [x] I have added the necessary unit tests and integration tests. - [ ] This PR requires documentation updates. - [ ] API changes are backward compatible. - [x] 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.