discord9
d26e6d4725
* perf(servers): prototype ready-only Flight batch coalescing Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * fix(servers): bound ready Flight batch admission and cover lifecycle Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * test(servers): fix redundant error pattern assertion Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * refactor(servers): simplify Flight coalescing with append-then-check accumulator Remove the lookahead/pending machinery and coalesce with an append-then-check soft budget. Extract the group state into a private BatchAccumulator so the ordinary stream loop reads as a short control flow: append each ready batch, flush when a budget is reached. Budgets are flush thresholds, not memory limits, so a group may exceed a budget by the final appended batch. First-batch direct send and singleton passthrough for a batch that starts a group already at a budget are unchanged. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * refactor(servers): split Flight stream into dispatcher and per-path units FlightRecordBatchStream::flight_data_stream held two large side-by-side branches (the verbose/ANALYZE metrics path and the plain/coalescing path) inline, making the function hard to read. Split it into a small dispatcher that keeps only the shared prologue (schema send, metrics init) and the shared EOF final-metrics tail, and move each branch into a self-contained unit: - verbose path -> Self::verbose_metrics_stream - plain/coalescing path -> private CoalescingBatcher (owns the BatchAccumulator, sent_first_batch, and schema) Each path returns whether it reached normal EOF; the dispatcher skips the shared final-metrics tail on any early error/failed-send exit, preserving the exact pre-split behavior (an early return previously exited the whole function and bypassed the tail). No behavior, metrics ordering, or coalescing semantics change. Add a regression test that drives flight_data_stream directly and asserts the verbose error path does not invoke the final-metrics tail (producer-side metrics() call count), since the public message stream hides the tail after an error. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * docs(servers): describe Flight stream units by behavior, not refactor history Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * perf(servers): avoid copying over-budget batches and release merged inputs Three review-driven improvements to Flight batch coalescing: - After a successful merge, reuse the drained vector in place (clear + push the merged batch) so the source batches are dropped before the send loop instead of staying alive across a backpressured send. - A batch that is itself at/over a budget is forwarded as a singleton even when encountered inside an accumulation group: the accumulated group is flushed and sent first, then the over-budget batch. This avoids copying a large batch into an aggregate just to merge the small batches ahead of it. Under-budget append-then-check is unchanged (600+600 still merges). - Strengthen two tests: the first-batch test now drives the producer directly and asserts the upstream poll count is exactly one at the first-batch send; the dictionary test now decodes the merged dictionary keys to assert the logical values, so a key-remapping regression is caught. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> * perf(servers): raise Flight coalesce row budget to 4096 mito2 commonly emits ~2000-row batches (~32-94KiB), so a 1024-row budget marked every such batch oversized and the coalescer forwarded them unbatched. Measured on a local distributed cluster (2M-row table): - full selector: 1000 -> 337 batches, p50 latency 4133 -> 3316 ms (-20%) - range scan: 501 -> 206 batches, 1466 -> 1162 ms (-21%) - small/olap queries (single-series, top-k, group-by): unchanged Larger budgets (8192 rows, or 1-4MiB bytes) coalesce more batches but do not improve latency further and slightly regress it, so 4096 rows is the sweet spot; MAX_BYTES stays at 256KiB and MAX_BATCHES at 16. Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com> --------- Signed-off-by: discord9 <55937128+discord9@users.noreply.github.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 — 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.
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.