dennis zhuang
ed1f2d9f4e
* fix(pipeline): coalesce concurrent pipeline cache misses The pipeline cache reads with a plain `moka::sync::Cache::get` and falls through to a distributed query on a miss, so when the 10s TTL expires every in-flight write request on a frontend issues its own scan of the single-region `greptime_private.pipelines` table. Concurrent scans per expiry scale with write QPS, and every frontend's burst lands on the same datanode. A user running high-throughput ingestion through a pipeline saw that datanode overloaded. Switch to `moka::future::Cache::try_get_with` so concurrent misses on the same key share one loader. This requires a single-key lookup, so cache entries are now keyed by the requested schema rather than the schema the pipeline is stored under; resolving a request to a stored schema stays in the loader, which is the authoritative path and already handles the empty-schema and multi-schema cases. A lookup for a schema not yet cached costs one extra read, now protected from amplification by the coalescing it enables. `remove_cache` previously only walked the compiled-pipeline cache, so an entry populated by `get_pipeline_str` alone (the pipeline read API) survived deletion until it expired. It now walks all three caches. Also make the TTL configurable as `pipeline.cache_ttl`, default unchanged at 10s. The TTL is what propagates a pipeline change to other frontends, so raising it trades staleness for fewer reads. Refs #9021 Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * fix(pipeline): restore cross-schema semantics broken by the new cache key Keying cache entries by the requested schema dropped two behaviours that the previous stored-schema key provided for free. Creating a new version only wrote the creating request's schema, so another schema on the same frontend kept serving its cached `latest` — an older version — until the entry expired. Since the whole point of making the TTL configurable is to let operators raise it, that window is not bounded by anything useful. Creation now invalidates every schema's `latest` alias for that name before priming the cache, leaving the version-pinned keys alone. The failover cache lost its reach across schemas the same way: a global pipeline (stored under the empty schema) loaded by schema A was cached under `A`, so schema B using it for the first time while the pipeline table was down missed and failed ingestion. The failover cache has no loader and so is not subject to the single-key model of `try_get_with`; it keeps the stored-schema key and the empty-schema-first resolution. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * refactor(pipeline): drop cache priming on create and fold the sweep helpers Priming the cache on create saved one read on a low-frequency operation and cost a concept: entries were written under the creating request's schema while `PipelineContent.schema` said empty, so the two schemas in play disagreed. Invalidating the `latest` aliases is required regardless — that is what makes a new version visible to other schemas — so dropping the priming loses only the saved read, which coalescing now protects anyway. `insert_and_compile` no longer needs the caller's schema. `remove_cache` and the create-time invalidation collapse into one `invalidate(name, version)`; `None` sweeps only the `latest` aliases, which is exactly what creation wants. That leaves `invalidate_by_suffixes` and `cache_keys` with a single caller each, so both are inlined. Drop the `PipelineOptions` humantime test: `load_config_test` loads both example TOMLs, which now carry `cache_ttl = "10s"`, and would fail the same way if the serde attribute were lost. The `toml` dev-dependency goes with it. The two invalidation tests are now checked to be orthogonal: removing the version suffix fails only the delete test, and sweeping just the compiled cache fails both. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> * fix(pipeline): keep failover populated across a create The `latest` sweep on create clears the failover cache along with the loaded ones, and after dropping the priming there was nothing writing it back. An outage between the create and the first read-back left neither `latest` nor the explicit version with anything to fall back on, failing ingestion — worse than before, since the previous version's failover entry was swept too. Creation now goes through `PipelineCache::on_pipeline_created`, which pairs the sweep with a failover write of the new empty-schema definition. The two must happen together, so they live behind one method rather than at the call site. Also commit the Cargo.lock entry for the dropped `toml` dev-dependency, and trim the comments added over the last few commits down to what the code does not already say. Signed-off-by: Dennis Zhuang <killme2008@gmail.com> --------- Signed-off-by: Dennis Zhuang <killme2008@gmail.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
stable for production · canary includes pre-releases · nightly is a weekly snapshot of main
- Introduction
- Why You Might Use It
- Overview
- What's Supported
- Compatibility and Migration
- Limitations and Edition Boundary
- 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. 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.
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 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
- You need the same engine and semantics on resource-constrained devices
Learn more in Why GreptimeDB.
Overview
A quick overview of what GreptimeDB ingests, how it connects to other systems, and what its distributed engine lets you do.
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.
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 |
Benchmarks:
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 — 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, 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.
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
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.