Lei, HUANG
17ed988f49
* fix(mito): preserve SST lifetime across index replacement
Share retirement and final purge across immutable index metadata views. Keep version-specific GC references until readers release them and match Parquet liveness by file identity.
Fixes #9944
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* refactor(mito): list-based SST purge and single FilePurger::remove_file
The final purge of a retired SST no longer depends on FileMeta: index
files of every version are discovered by listing
({region_dir}/index/{file_id}. prefix), so even when the last live view
is a stale one, the purge still removes all newer index files.
- SstLifetime is slimmed down to { deleted, file_purger }; the purge now
runs in the last FileHandleInner::drop (Arc::strong_count == 1, which
is race-free here since only inners of the same file hold the lifetime).
- FilePurger is merged back into a single remove_file(file_meta,
is_delete) method; is_delete means the file was retired AND this is the
last live view.
- LocalFilePurger deletes the SST and all its index files via the new
delete_file_and_all_indexes helper (list before delete, so a listing
failure leaves the file untouched).
- Remove the dead index_outdated flag and its accessors, which had no
callers in the entire repo.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* fix(mito): elect final SST view release atomically
Track metadata views with an atomic counter instead of inspecting the lifetime Arc strong count. Concurrent destructors now elect exactly one final releaser while preserving the single remove_file interface.
Add a deterministic concurrent-drop regression covering retired and live files.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* refactor(mito): restore Arc-owned SST cleanup and clarify reference release
Restore the lifetime design from 835227dcbc, using the final Arc drop and latest metadata instead of manual view counting and per-SST index listing.
Rename FilePurger::remove_file to release_file_ref while retaining purge_file for final cleanup. Preserve and adapt the concurrent view-release regression test.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* refactor(mito): track SST references by file lifetime
Register one reference guard per logical SST lifetime and release it on final drop, including normal region close. Remove per-view release_file_ref and index_outdated handling.
Protect all index generations while their SST is referenced. Preserve the existing GC snapshot format by expanding the recorded index generation upper bound for older version-aware workers.
Update coverage for independent references, normal close, cross-region ownership, concurrent final cleanup and both GC listing modes.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* refactor(mito): release SST references directly on lifetime drop
Remove the redundant FileReferenceGuard and let SstLifetime register and unregister its file reference directly. Preserve unconditional reference release on normal close and retirement-only purge.
Expose the reference manager through FilePurger and adapt reference manager tests without changing GC or snapshot behavior.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* test(mito): verify SST cleanup with real storage and isolate GC policy cases
Replace the recording-only concurrent purge test with filesystem assertions using LocalFilePurger and shared fixtures. Keep intermediate-view retention and retired versus live file cleanup coverage without claiming exhaustive thread interleavings.
Separate GC reference-policy cases from reader-release ordering and reuse their setup. Production code remains unchanged.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* style(mito): format SST lifetime changes
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* fix(mito): reclaim obsolete indexes by reader version
Track exact index-version references per metadata view and restore version-specific Puffin liveness in both GC paths. Keep file-level Parquet protection and shared SST lifetime cleanup, without expanding snapshots into unreferenced index generations.
Include old readers of imported files even when the source region is not open locally. Add real-storage GC coverage for reader release and lingering while retaining Parquet and the current index.
Validated workspace checks with default and all features, all-target all-feature Clippy with warnings denied, and all 1853 mito2 tests.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
---------
Signed-off-by: Lei, HUANG <ratuthomm@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 · 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.