Lei, HUANG
ed8a4990fe
* feat(flow): handle time_ranges in DirtyWindowRequest Bump greptime-proto to include the new `time_ranges` field on DirtyWindowRequest (GreptimeTeam/greptime-proto#330) and mark the corresponding aligned time windows as dirty in the batching engine, in addition to the existing per-timestamp dirty marking. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * style(flow): fix doc comment spacing in align_time_window Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * test(flow): cover time_ranges in handle_mark_dirty_time_window Verify a valid [start_inclusive, end_exclusive) range is aligned to time window boundaries and stored with an explicit end, and that empty or reversed ranges are skipped. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * fix(flow): union merged dirty windows with the larger end Merging a bounded dirty range with a window contained in it (e.g. [0s, 15s) with nested [5s, 10s), or an unbounded dirty window inside a bounded range) previously assigned the contained window's upper bound, shrinking the merged window and permanently dropping the tail range from re-computation. Keep max(prev_upper, cur_upper) instead. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * fix(flow): clip bounded dirty ranges at the expire bound merge_dirty_time_windows dropped every window whose start is before expire_lower_bound, so a bounded dirty range crossing the bound (e.g. [0s, 15s) with expire 10s) lost its still-live suffix [10s, 15s). Now bounded ranges are dropped only when their end is at/before the expire bound, and crossing ranges are clipped to the bound (which the caller aligns to the time window boundary). Unbounded windows keep the existing start-based behavior. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * fix(flow): fall back to full dirty on dirty-window alignment failure An eval/alignment error previously aborted the per-task dirty-marking closure, losing every dirty timestamp and range accumulated for that task, while the RPC still returned Ok so the producer would not retry. On alignment failure now log a warning and mark the whole task dirty (set_dirty) instead, so the affected data is conservatively recomputed. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * style(flow): apply rustfmt to new dirty-window merge tests Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * test(flow): cover time index units in dirty window marking Document that DirtyWindowRequest timestamps/time_ranges are bare i64s interpreted in the source table's time index native unit, and add a test expressing the same [3s, 11s) range in second/millisecond/ microsecond/nanosecond units across four tables, asserting all align to the same dirty window [0s, 15s). Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * chore: bump greptime-proto to 8127f179 Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * fix(flow): return dirty-window alignment errors to callers Do not acknowledge a DirtyWindowRequest when a time-windowed task cannot align a timestamp or range. The previous conservative fallback used set_dirty(), but that marker only represents a single epoch-start window for time-windowed flows, so it could still lose the affected dirty range. Propagate task errors through the join loop instead so producers can retry. Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> * chore: update proto to commits on main Signed-off-by: Lei, HUANG <ratuthomm@gmail.com> --------- Signed-off-by: Lei, HUANG <ratuthomm@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.