One database for metrics, logs, and traces
replacing Prometheus, Loki, and Elasticsearch

The unified OpenTelemetry backend — with SQL + PromQL on object storage.

User Guide | API Docs | Roadmap 2026

• Introduction
• Overview
• Features
• How GreptimeDB Compares
• 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 built for Observability 2.0 — treating metrics, logs, and traces as one unified data model (wide events) instead of three separate pillars.

Use it as the single OpenTelemetry backend — replacing Prometheus, Loki, and Elasticsearch with one database built on object storage. Query with SQL and PromQL, scale without pain, cut costs up to 50×.

Overview

A quick overview of what GreptimeDB ingests, how it connects to other systems, and what its distributed engine lets you do.

Features

Feature	Description
Observability 2.0 native	Logs, metrics, and traces in one engine with SQL + PromQL. Native OpenTelemetry, Prometheus remote write, and Jaeger. Migrate one signal at a time, or use as a single backend.
Elastic compute-storage separation	Scale reads independently with horizontal replicas. Serve high-concurrency workloads from dashboards, alerting, and AI agents — without resharding or data migration.
Sub-second on PB–EB-scale data	Columnar engine with fulltext, inverted, and skipping indexes. Written in Rust. Designed for high-concurrency point queries, not just analytical scans.
50× lower cost	Object storage (S3, GCS, Azure Blob) as primary storage, with a tiered cache (memory + local disk) to keep writes and queries fast.

Perfect for:

• Replacing Prometheus + Loki + Elasticsearch with a single observability backend
• Scaling past Prometheus — high cardinality, long-term storage, no Thanos/Mimir overhead
• AI/agent workloads — store GenAI telemetry (OTel GenAI conventions), and serve high-concurrency reads from SRE/developer agents via horizontal read replicas
• Cutting observability costs with object storage (up to 50× savings on traces, 30% on logs)
• Edge-to-cloud observability with unified APIs on resource-constrained devices

Why Observability 2.0? Three separate databases for metrics, logs, and traces means three storage layers, three query languages, and three sets of dashboards. GreptimeDB stores all three as timestamped wide events in one columnar engine — JOIN across signals in SQL, run one stack instead of three, and ingest AI agent telemetry the same way. Read more: Observability 2.0 and the Database for It.

Learn more in Why GreptimeDB.

How GreptimeDB Compares

Capability	GreptimeDB	Prometheus / Thanos / Mimir	Grafana Loki	Elasticsearch
Data types	Metrics, logs, traces	Metrics only	Logs only	Logs, traces
Query language	SQL + PromQL	PromQL	LogQL	Query DSL
Storage	Native object storage (S3, etc.)	Local disk + object storage (Thanos/Mimir)	Object storage (chunks)	Local disk
Scaling	Compute-storage separation, stateless nodes	Federation / Thanos / Mimir — multi-component, ops heavy	Stateless + object storage	Shard-based, ops heavy
Cost efficiency	Up to 50× lower storage cost	High at scale	Moderate	High (inverted index overhead)
OpenTelemetry	Native (metrics + logs + traces)	Partial (metrics only)	Partial (logs only)	Via instrumentation

Benchmarks:

• GreptimeDB tops JSONBench's billion-record cold run test
• TSBS Benchmark
• More benchmark reports

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, autopilot, 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 \
  --rpc-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, and 4003 are not blocked by a firewall or used by other services.
• Failed to start? Check the container logs with docker logs greptime for further details.

Getting Started

• Quickstart
• User Guide
• Demo Scenes
• FAQ

Build From Source

Prerequisites:

• Rust toolchain — nightly, pinned by rust-toolchain.toml
• Protobuf compiler (>= 3.15)
• C/C++ building essentials: gcc / g++ / autoconf and the glibc dev package (libc6-dev on Ubuntu, glibc-devel on 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
• Grafana Data Source: GreptimeDB Grafana data source plugin
• Grafana Dashboard: Official Dashboard for monitoring

Project Status

GreptimeDB is at v1.0 GA 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!

• Slack
• Discussions
• Official Website
• Blog
• LinkedIn
• X (Twitter)
• YouTube

License

GreptimeDB is licensed under the Apache License 2.0.

Commercial Support

Running GreptimeDB in your organization? We offer enterprise add-ons, services, training, and consulting. 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)

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