dennis zhuang
8d9f24d1b6
* docs: add entity relationships and graph query RFC Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: revise entity-graph RFC after review - Zero-configuration declarations: the Prometheus-on-Kubernetes convention pack (job/instance per the OTel-Prometheus compatibility spec, k8s SD labels, *_info descriptors, target_info enrichment) alongside the OTLP trace auto-stamp, plus Remote Write 2.0 inline metadata. - Calls endpoints follow the service entity declaration; self-calls compare full endpoint ids; no silent identity fallback. - Strict time-window contract: the source window is never narrower than the query's observed_at range; unsafe-to-extract predicates error instead of silently defaulting. - scope removed from the relationship schema (kept on entities as a display property); entity row contract restated per projected observation; endpoint encoding documented as the v1 storage-level key with its known collision limitation. - Sampling caveats corrected (ratios are representative only under unbiased sampling); snapshot relation synthesizes endpoint-only vertices; shared attributes provide join keys while co-declaration provides relationship semantics. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: align entity-graph RFC contracts and tighten prose - Metric/trace service unification is promised only when service.namespace is empty and job is not relabeled (the compatibility spec renders job as <namespace>/<name>); otherwise alignment needs pipeline normalization or explicit declarations. - Entity row contract stated once (per projected observation); the calls defining SQL is marked as the single-column simplification of the declaration-derived endpoint ids; Remote Write 2.0 metadata intake and the Prometheus implicit declarations are listed as M1 work. - Compress survey/example/reference prose. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: pin down the k8s convention pack rules and edge directions - kube_pod_owner implicitly declares k8s.workload with id (namespace, owner_kind, owner_name) and derives pod part_of workload; target_info's non-job/instance labels are implicit service descriptive columns — fixed rules, no new declaration syntax. - One direction for pod placement: k8s.pod runs_on k8s.node (pod added to runs_on sources; node->pod removed from contains). - Drop the remaining 'canonical' wording for the v1 storage-level id. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: make contains/part_of a true inverse pair part_of covers service.instance->service and k8s.pod->k8s.workload with contains as its inverse; has_instance is dropped. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: rewrite entity-graph RFC as a design narrative Restructure for human review: mainline first, cases illustrate the design instead of specifying it. - New Architecture and Benefits and Drawbacks sections; the calls derivation stays expanded as the flagship example while schema enumerations, window-rule listings, and executor edge-case handling move out of the document. - The cross-signal promise is stated honestly: neighbours and their source tables are discovered first, their telemetry is the next query — one engine, two statements; the worked example shows the full declaration -> entity -> edge -> telemetry flow. - Default materialisation added as the most direct alternative to read-time derivation, with its costs. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: final wording pass on the entity-graph RFC Mark the property-graph DDL as illustrative rather than settled M2 syntax, credit standards as foundations rather than claiming wholesale alignment, and clean up punctuation-heavy prose. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: keep the RFC at design altitude Demote the convention-pack rule details and snapshot property-merge semantics to the implementing changes; correct the single-trace-table assumption (traces can be routed to multiple tables); record attribute-key participation in entity equality as an open question. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * docs: correct service graph terminology Signed-off-by: Dennis Zhuang <killme2008@gmail.com> --------- Signed-off-by: Dennis Zhuang <killme2008@gmail.com>
One database for metrics, logs, and traces
replacing Prometheus, Loki, and Elasticsearch
The unified OpenTelemetry backend — with SQL + PromQL on object storage.
- Introduction
- Overview
- Features
- How GreptimeDB Compares
- Architecture
- Try GreptimeDB
- Getting Started
- Build From Source
- Tools & Extensions
- Project Status
- Community
- License
- Commercial Support
- Contributing
- Acknowledgement
Introduction
GreptimeDB is an open-source observability database built for Observability 2.0 — treating metrics, logs, and traces as one unified data model (wide events) instead of three separate pillars.
Use it as the single OpenTelemetry backend — replacing Prometheus, Loki, and Elasticsearch with one database built on object storage. Query with SQL and PromQL, scale without pain, cut costs up to 50×.
Overview
A quick overview of what GreptimeDB ingests, how it connects to other systems, and what its distributed engine lets you do.
Features
| Feature | Description |
|---|---|
| Observability 2.0 native | Logs, metrics, and traces in one engine with SQL + PromQL. Native OpenTelemetry, Prometheus remote write, and Jaeger. Migrate one signal at a time, or use as a single backend. |
| Elastic compute-storage separation | Scale reads independently with horizontal replicas. Serve high-concurrency workloads from dashboards, alerting, and AI agents — without resharding or data migration. |
| Sub-second on PB–EB-scale data | Columnar engine with fulltext, inverted, and skipping indexes. Written in Rust. Designed for high-concurrency point queries, not just analytical scans. |
| 50× lower cost | Object storage (S3, GCS, Azure Blob) as primary storage, with a tiered cache (memory + local disk) to keep writes and queries fast. |
Perfect for:
- Replacing Prometheus + Loki + Elasticsearch with a single observability backend
- Scaling past Prometheus — high cardinality, long-term storage, no Thanos/Mimir overhead
- AI/agent workloads — store GenAI telemetry (OTel GenAI conventions), and serve high-concurrency reads from SRE/developer agents via horizontal read replicas
- Cutting observability costs with object storage (up to 50× savings on traces, 30% on logs)
- Edge-to-cloud observability with unified APIs on resource-constrained devices
Why Observability 2.0? Three separate databases for metrics, logs, and traces means three storage layers, three query languages, and three sets of dashboards. GreptimeDB stores all three as timestamped wide events in one columnar engine — JOIN across signals in SQL, run one stack instead of three, and ingest AI agent telemetry the same way. Read more: Observability 2.0 and the Database for It.
Learn more in Why GreptimeDB.
How GreptimeDB Compares
| Capability | GreptimeDB | Prometheus / Thanos / Mimir | Grafana Loki | Elasticsearch |
|---|---|---|---|---|
| Data types | Metrics, logs, traces | Metrics only | Logs only | Logs, traces |
| Query language | SQL + PromQL | PromQL | LogQL | Query DSL |
| Storage | Native object storage (S3, etc.) | Local disk + object storage (Thanos/Mimir) | Object storage (chunks) | Local disk |
| Scaling | Compute-storage separation, stateless nodes | Federation / Thanos / Mimir — multi-component, ops heavy | Stateless + object storage | Shard-based, ops heavy |
| Cost efficiency | Up to 50× lower storage cost | High at scale | Moderate | High (inverted index overhead) |
| OpenTelemetry | Native (metrics + logs + traces) | Partial (metrics only) | Partial (logs only) | Via instrumentation |
Benchmarks:
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, autopilot, 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 \
--rpc-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 — nightly, 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
- Grafana Data Source: GreptimeDB Grafana data source plugin
- Grafana Dashboard: Official Dashboard for monitoring
Project Status
GreptimeDB is at v1.0 GA 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.
Read the v1.0 highlights and 2026 roadmap, or browse the version reference.
If GreptimeDB is useful to you, please star the repo.
Community
We invite you to engage and contribute!
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
Running GreptimeDB in your organization? We offer enterprise add-ons, services, training, and consulting. 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.