discord9
f88af153ec
* feat(query): serialize remote dynamic membership Recover single-column UTF-8 build-key membership from InList and HashTableLookupExpr producers and send it to remote datanodes using the existing PhysicalInListNode. Non-monotone or unsupported subtrees widen to true; the receiver keeps the live filter authoritative. Signed-off-by: discord9 <discord9@163.com> * refactor(query): send lookup membership as a key list Serialize a HashTableLookupExpr as the existing PhysicalInListNode and preserve the rest of the expression tree. Remove per-subtree monotone and true rewrites; only an unrepresentable lookup or an over-budget payload falls back at the whole-payload level. Signed-off-by: discord9 <discord9@163.com> * docs: tighten narrow membership contract Clarify that the key-list transport is a positive pruning hint, not universal Boolean equivalence, and test that a NULL probe survives the IS NULL wrapper. Signed-off-by: discord9 <discord9@163.com> * fix(query): retain safe bounds on membership fallback When a key-list predicate cannot be shipped, keep top-level conjuncts that contain no membership and re-encode their conjunction. Drop membership-bearing conjuncts whole; fall back to true only when no safe bounds remain. Signed-off-by: discord9 <discord9@163.com> * fix(query): skip unrepresentable dynamic filter snapshots Do not synthesize a true predicate when a runtime hash-table lookup has no wire representation. Capture and fanout skip the affected snapshot, keep the query running, and report PushedDown::No so the parent filter is retained. Safe membership-free conjuncts still travel, and a producer-supplied true remains encodable. Cover restrictive-to-true updates, unsupported initial registrations with later supported updates, final unsupported cleanup, and NULL build keys. Signed-off-by: discord9 <discord9@163.com> * docs(query): clarify 642-key payload budget comment Signed-off-by: discord9 <discord9@163.com> * refactor(query): tighten dynamic membership encoding Preserve static IN conjuncts when only a runtime lookup is unrepresentable, apply the IN-list exclusion only on the over-budget path, and derive the membership count guard from the byte budget before materializing literals. Document that decoded dynamic filters are pruning-only, fix the NOT/CASE contract wording, deduplicate test helpers, and justify the schema-less InList proto encoding. Signed-off-by: discord9 <discord9@163.com> * fix(query): reset stale dynamic filters on unsupported updates Publish an explicit true reset when a producer generation cannot be carried, instead of leaving an older narrower predicate that could prune rows after widening. Keep the unsupported expression unrewritten; the reset is a transport-built invalidation. Type over-budget build-key memberships as DynFilterPayloadTooLarge, accept scalar-subquery IN lists under the conjunct-retention contract, and clarify the no-initial-predicate wording. Signed-off-by: discord9 <discord9@163.com> * docs(query): scope no-true rule to the encoder Clarify that the encoder never substitutes true for a producer node, while the long-lived fanout path publishes an explicit true reset to invalidate a stale region predicate. Point at the fanout caller instead of claiming producers always skip refused generations. Signed-off-by: discord9 <discord9@163.com> * docs(query): correct reset wording Fix the inverted conjunct-retention sentence and describe initial capture versus ongoing fanout resets consistently in the error docs. Signed-off-by: discord9 <discord9@163.com> --------- Signed-off-by: discord9 <discord9@163.com>
Metrics, logs, and traces.
One engine, on your infrastructure.
User Guide · API Docs · Live Demo · 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.
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
Or skip the install and try the Live Demo.
Next: Quickstart, the full Install Guide, Demo Scenes, and the FAQ.
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 (with native histograms over Remote Write 2.0), 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 | Pipelines that parse and transform logs at ingestion, 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, 168 investigations per interface across six models. 40% fewer wrong diagnoses, about 48% fewer input tokens, about 45% lower estimated cost (write-up)
- JSONBench billion-record cold run: first place as of March 2025
- 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 and region migration 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. For GreptimeDB Enterprise inquiries, contact us.
Architecture
GreptimeDB can run in two modes:
- Standalone — single binary for development and small deployments.
- Distributed — four components, each independently scalable:
- Frontend — protocol endpoints and the distributed query engine. Stateless, scales horizontally.
- Datanode — region engine with WAL, memtable, SST, cache, compaction, and indexes. Persists data to object storage.
- Metasrv — metadata, region routing, and cluster coordination (DDL procedures, failover, region migration, repartitioning). 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.
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.
Releases follow Semantic Versioning. For where the project is going, read the v1.0 highlights, the 2026 roadmap, and what we mean by an observability database.
Community
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.
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.