GreptimeDB
 | This article uses bare URLs, which may be threatened by link rot. (November 2024) (Learn how and when to remove this template message) |
| Developer(s) | Greptime Inc...[1] |
|---|---|
| Initial release | 15 November 2022 |
| Stable release | 0.9.5
/ 18 October 2024[2] |
| Repository | https://github.com/GreptimeTeam/greptimedb/ |
| Written in | Rust |
| Engine | |
| Operating system | Cross-platform |
| Type | Time series database |
| License | Apache 2.0 |
| Website | greptime.com |
Search GreptimeDB on Amazon.
GreptimeDB is an open-source, cloud-native time series database[3] designed to handle large-scale metrics, logs, and events. Developed by Greptime Inc., it is implemented in Rust and offers support for SQL and PromQL as its primary query languages.
GreptimeDB is compatible with widely used database protocols and APIs, such as InfluxDB, OpenTelemetry, Prometheus, Elasticsearch, and Loki. The database is mainly applied to time series data management[4] and analysis across diverse applications, including observability, IoT, and edge computing.
History
Greptime Inc.[5] was founded by Xiaodan Zhuang (CEO), Ning Sun (CTO), and Jiachun Feng. GreptimeDB was open-sourced on November 15, 2022[6]. It was specifically designed to address the challenges of managing large volumes of time-series data, particularly for IoT and observability applications[7]
GreptimeDB is available in both standalone (single-process) and cluster configurations, offering flexibility to users in different deployment environments. The open-source release has allowed the community to contribute to its ongoing development and adoption.
Technical Overview
GreptimeDB abstracts metrics and logs into events, organizing them through three column types: tags, fields, and timestamp. The database supports SQL in processing all types of time-series data (metrics, logs, and events). The architecture follows a storage-compute separation design, where data is stored in object storage to minimize storage costs.
Supported protocols
GreptimeDB can integrate with various protocols[8] for diverse time-series data ingestion[9] and query needs.
- MySQL: interaction using MySQL clients or libraries
- PostgreSQL: connecting via PostgreSQL tools and drivers
- InfluxDB Line Protocol: data ingestion from systems using InfluxDB
- OpenTelemetry: data collection and tracing using OpenTelemetry
- OpenTSDB: compatible with OpenTSDB
- gRPC: RPC (Remote Procedure Call) capabilities for advanced integrations and custom application development
- PromQL: native support for Prometheus Query Language (PromQL)
- HTTP API: REST APIs for data ingestion and management
References
- ↑ https://greptime.com
- ↑ "GreptimeDB v0.9.5 release notes". GitHub. Retrieved 18 Oct 2024.
- ↑ Struckov, Alexey; Yufa, Semen; Visheratin, Alexander A.; Nasonov, Denis (2019-01-01). "Evaluation of modern tools and techniques for storing time-series data". Procedia Computer Science. 8th International Young Scientists Conference on Computational Science, YSC2019, 24-28 June 2019, Heraklion, Greece. 156: 19–28. doi:10.1016/j.procs.2019.08.125. ISSN 1877-0509.
- ↑ CMU Database Group (2024-12-10). GreptimeDB: Implement, Integrate and Extend a Query Engine (Ruihang Xia). Retrieved 2025-01-15 – via YouTube.
- ↑ "Greptime Linkedin Profile".
- ↑ "【技术科普】从Greptime聊时序数据库 | DevmoreWork". Apple Podcasts. Retrieved 2024-12-13.
- ↑ "CNCF On demand webinar: Ingesting Data into GreptimeDB Using InfluxDB Line Protocol". CNCF. Retrieved 2024-12-13.
- ↑ "GreptimeDB Installation Guide".
- ↑ "Ingest MQTT Data into GreptimeDB | EMQX Docs". docs.emqx.com. Retrieved 2024-12-13.
This article "GreptimeDB" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:GreptimeDB. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
