jeremyhi
7397ab6b23
* perf(datanode): send the mito puts of one request to each worker at once The region server handled the puts of a request to several regions as separate requests. A Mito worker woke on the first of them and wrote it alone; in the durable object store WAL mode its append then blocked the worker until the next seal, so the rest of the request went into the next object, and clients writing a partitioned table locked into one phase that wastes a tick. When every put of a request goes to a ready mito region, the region server now hands them to `MitoEngine::put_regions_batch`, which submits the puts of each worker in one message, so the worker writes its regions of the request in one batch and one WAL append. Requests to one region and to other engines are handled as before. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): await each region of a batched mito put on its own task The batched path waited for the regions of a request in order and sent the worker messages one after another, so an error or a full worker queue of one region held back the others, and its metrics counted the request as a whole. `MitoEngine::put_regions_batch` now sends the worker messages concurrently on their own task and returns each region's result. The region server awaits and accounts every region on its own ingest task, as the per-region path does: the first error returns at once, and each region records its latency, changed rows or failure and is named in its error. A request with several puts to one region keeps the per-region path. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): spawn the accounting of batched mito puts from the batch task The request spawned the per-region accounting tasks only after the batch task returned, so a request dropped in between left accepted writes unaccounted. The batch task now spawns them and the request only joins their handles. Also state in the architecture invariants that the region server's batched puts to the metric and mito engines are the one request dispatch that does not go through the RegionEngine trait. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): batch mito puts through the RegionEngine trait Add `RegionEngine::handle_batch_put_requests`, which returns each region's response of the puts of one request. The default handles each put with `handle_request`, so other engines keep their behaviour; Mito overrides it with the one message per worker submission. The region server calls the trait method instead of downcasting to `MitoEngine`, and the architecture invariants no longer need an exception. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): trace each region of a batched mito put Attach the per-region `RegionServer::handle_region_request` span of the per-region path to each region's task of a batched mito put, so the region ids stay in distributed traces. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): let a batch put response answer for several regions `RegionEngine::handle_batch_put_requests` now returns each response with the regions it answers for. The default and Mito answer each region on its own; an engine that writes several puts together, such as the metric engine's batched puts, can answer them with one response. The region server accounts a response for each of its regions. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): batch any request with several mito puts Take the batch path for a request with at least two puts to ready mito regions, also when they all target one region: a request of many puts to one region otherwise still sends one worker message per put. Each put keeps its own response, affected rows and error. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * perf(datanode): account batched mito puts in one task Await the responses of a batched mito put in the batch task with a FuturesUnordered instead of one spawned task per put. The task accounts each response as it completes, hands the first error back to the caller at once and keeps accounting the rest, so a request of many puts no longer spawns a task per put. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * refactor(mito2): name the batched worker message BatchWrite Signed-off-by: jeremyhi <jiachun_feng@proton.me> * refactor(mito2): submit batched writes on the ingest runtime Grouping and submitting the writes of a request is part of ingestion, so the task runs on the ingest runtime and takes the write task class when the workload scheduler is enabled. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * refactor(datanode): share ingest result accounting across put paths The per-region path, the metric batch path and the mito batch path each recorded the changed rows or the failures of an ingest response. They now call one helper, so a metric or label change happens in one place. A batch put response lists the region of each put it answers for, so the failure count stays per put when two puts go to one region; the trait documentation now says so. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * fix(mito2): give batched puts of a stopped worker the worker's error A batched write message that could not reach a stopped worker was dropped, so its puts failed with "Failed to recv result" while a put submitted on its own fails with "Worker <id> is stopped". The worker now returns the message it could not take, and each of its puts receives the WorkerStopped error. Signed-off-by: jeremyhi <jiachun_feng@proton.me> * refactor(datanode): look up the batch latency histogram once The label of the request latency histogram is the same for every put of a batch, so the batch task looks it up once instead of per observation. Signed-off-by: jeremyhi <jiachun_feng@proton.me> --------- Signed-off-by: jeremyhi <jiachun_feng@proton.me>
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.