GreptimeDB

active

by Greptime · since 2022 · also known as Greptime, GreptimeCloud

Rust observability database that keeps metrics, logs and traces in one columnar engine on object storage.

time-seriescolumnar / olapsearch enginedatabase engineopen source (permissive)growing

Overview

GreptimeDB is an open-source database from Greptime that stores metrics, logs, events and traces in a single columnar engine built on Apache Arrow, DataFusion and Parquet, with compute separated from object storage. It speaks many dialects at once: SQL and PromQL for queries, and Prometheus remote write, InfluxDB line protocol, OpenTelemetry, Elasticsearch bulk, Loki push and the MySQL and PostgreSQL wire protocols for access. That lets one cluster replace several observability backends. It reached 1.0 in April 2026, runs from a single binary at the edge up to Kubernetes clusters, and is sold commercially as GreptimeDB Enterprise and the hosted GreptimeCloud.

Strengths & trade-offs

Strengths

  • +One engine for metrics, logs and traces instead of three backends
  • +Object storage keeps long retention cheap
  • +SQL and PromQL over the same data
  • +Wide protocol compatibility eases migration from Prometheus, InfluxDB, Elasticsearch and Loki
  • +Runs as a single binary on edge devices or as a Kubernetes cluster

Trade-offs

  • −Young compared with Prometheus-ecosystem or Elasticsearch stacks
  • −Breadth of protocols means some compatibility edges are thinner than the originals
  • −Smaller community and fewer public large-scale references
  • −Object-storage reads add latency for cold queries

Great for

  • +Unified observability backend for metrics, logs and traces
  • +Prometheus long-term storage on S3
  • +IoT and connected-vehicle telemetry
  • +Edge data collection synced to the cloud

Think twice for

  • −Transactional workloads
  • −Relevance-ranked document search
  • −Teams that need a decade-proven stack

Capabilities

Native or official first-party support · 10 of 25 tracked

✓Joins
✓Secondary indexes
✓Full-text search
✓JSON
✓Materialized views
✓TTL / expiry
✓Columnar storage
✓Durable persistence
✓Compression
✓Streaming ingest
–Transactions
–Vector search
–Geospatial
–Change data capture
–Triggers
–Stored procedures
–Pub/Sub
–In-memory
–Encryption at rest
–Role-based access
–Time travel
–Branching
–Online schema changes
–Offline sync
–Runs in WebAssembly

Scale & consistency

Comfortable data size
Large · ≤ 1 PB
Horizontal scaling
Native
Multi-region
No
Transactions
None
Consistency
Strong
Schema
Flexible
Architecture
Single node, Distributed, Shared storage
Query languages
SQL, PromQL

Frontend, datanode and metasrv roles scale independently; data lives in object storage with local caches.

Deployment

Self-hosted Vendor-managed cloud Serverless Embedded Edge Kubernetes operator

available on · AWS

Ecosystem

compatible withMySQL, PostgreSQL, Prometheus, InfluxDB, Elasticsearch
apis & protocolsMySQL wire protocol, PostgreSQL wire protocol, Prometheus remote write/read, InfluxDB line protocol, OTLP, Elasticsearch bulk API, Loki push API, gRPC, HTTP
client languagesGo, Java, Rust, JavaScript

Alternatives to GreptimeDB

Compare all→
InfluxDBTime-Series51.0

Purpose-built time-series database, rebuilt in Rust on Arrow, DataFusion and Parquet for version 3.

greptimedb vs influxdb →
VictoriaMetricsTime-Series26.8

Resource-frugal, Prometheus-compatible metrics database, run as a single binary or a sharded cluster.

greptimedb vs victoriametrics →
Grafana MimirTime-Series15.8

Grafana Labs' horizontally scalable, multi-tenant long-term store for Prometheus and OTLP metrics.

greptimedb vs grafana-mimir →
OpenObserveSearch Engine20.4

Single-binary Rust observability platform storing logs, metrics and traces as Parquet on object storage.

greptimedb vs openobserve →
ClickHouseColumnar / OLAP69.8

Open-source columnar OLAP database known for very fast aggregations over billions of rows.

greptimedb vs clickhouse →
TimescaleDBTime-Series47.1

PostgreSQL extension adding hypertables, columnar compression and continuous aggregates for time series.

greptimedb vs timescaledb →

GreptimeDB FAQ

What is GreptimeDB?

GreptimeDB is a time-series database by Greptime, first released in 2022. GreptimeDB is an open-source database from Greptime that stores metrics, logs, events and traces in a single columnar engine built on Apache Arrow, DataFusion and Parquet, with compute separated from object storage. It speaks many dialects at once: SQL and PromQL for queries, and Prometheus remote write, InfluxDB line protocol, OpenTelemetry, Elasticsearch bulk, Loki push and the MySQL and PostgreSQL wire protocols for access. That lets one cluster replace several observability backends. It reached 1.0 in April 2026, runs from a single binary at the edge up to Kubernetes clusters, and is sold commercially as GreptimeDB Enterprise and the hosted GreptimeCloud.

Is GreptimeDB open source?

Yes. GreptimeDB is open source. License: Apache License 2.0.

Does GreptimeDB support ACID transactions?

No. GreptimeDB does not provide ACID transactions; its consistency model is strong.

Does GreptimeDB scale horizontally?

GreptimeDB scales horizontally out of the box, with built-in sharding or partitioning. It is comfortable at large scale (≤ 1 PB).

How can I run GreptimeDB?

You can self-host it, use the vendor's managed cloud, run it serverless and run it at the edge. Managed offerings include GreptimeCloud. It is available on AWS.

What is GreptimeDB best for?

GreptimeDB is a strong fit for unified observability backend for metrics, logs and traces; prometheus long-term storage on S3; and IoT and connected-vehicle telemetry. Think twice for transactional workloads.

What are the best alternatives to GreptimeDB?

Common alternatives to GreptimeDB include InfluxDB, VictoriaMetrics, Grafana Mimir, OpenObserve and ClickHouse. The right choice depends on your workload, scale and hosting; WhichDB's smart match ranks them against your requirements.

How popular is GreptimeDB?

GreptimeDB ranks #221 of 844 databases on the WhichDB Score (15.1/100), and #12 among time-series databases. The score combines public adoption signals such as package downloads, GitHub activity, job posts and developer discussion.