Amazon Timestream
Serverless time-series database for IoT, metrics, and operational telemetry
openGemini
Scalable, high-performance time-series DB for massive telemetry
openGemini delivers cloud-native, distributed storage and analysis of massive telemetry data with high performance, scalability, high-cardinality handling, and native compatibility with InfluxDB tools.
Overview
Overview
openGemini is a cloud-native distributed time-series database designed for massive telemetry workloads. It combines an LSM-based storage engine with automatic time-based partitioning, delivering sub-millisecond write latency and fast query response even at high cardinality.
Capabilities & Deployment
The MPP architecture lets you scale horizontally by adding nodes, while the columnar format and dedicated compression achieve up to 15:1 storage reduction. openGemini natively understands InfluxDB v1.x line protocol, InfluxQL, and Prometheus remote write/read, enabling seamless integration with existing observability toolchains. Deployment is flexible: you can run a single-node instance, a clustered setup on VMs or bare metal, or use the openGemini-operator for one-click Kubernetes installation, with the gemix tool simplifying standalone installs.
Audience
Target users include DevOps engineers, IoT platform developers, and observability teams that require real-time analytics on high-cardinality metrics. By providing native PromQL support and OpenTelemetry ingestion, openGemini fits into modern cloud-native monitoring stacks while offering the performance needed for large-scale data pipelines.
Highlights
High performance with LSM storage and automatic partitioning
MPP architecture for linear horizontal scalability
High‑cardinality engine with up to 15:1 columnar compression
Flexible deployment via operator, one‑click tools, and InfluxDB compatibility
Pros
- High throughput and low latency for write and query operations
- Seamless scaling across distributed clusters
- Efficient storage for high‑cardinality time‑series data
- Drop‑in compatibility with existing InfluxDB toolchains
Considerations
- Maturing project as a CNCF sandbox, ecosystem still growing
- Limited native query language beyond InfluxQL and PromQL
- Build requires Go 1.22+ and Python, adding setup complexity
- Fewer third‑party integrations compared to older time‑series databases
Managed products teams compare with
When teams consider openGemini, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Real-time IoT telemetry ingestion at massive scale
- Cloud-native observability stacks needing Prometheus backend storage
- Edge data aggregation with KubeEdge integration
- Teams seeking InfluxDB compatibility while requiring higher performance
Not ideal when
- Small, single-node deployments with minimal data volume
- Workloads requiring extensive SQL analytics beyond time‑series
- Organizations that need mature commercial support guarantees
- Use cases demanding native relational database features
How teams use it
Industrial sensor data collection
Ingest millions of sensor readings per second with sub-10 ms query latency, enabling real-time monitoring and anomaly detection.
Kubernetes cluster monitoring
Store Prometheus remote write data, query with PromQL, and visualize metrics without additional storage layers.
Edge device telemetry aggregation
Leverage KubeEdge integration to sync edge data to a central openGemini cluster for unified analysis.
OpenTelemetry backend for microservices
Capture distributed traces and metrics, persisting them efficiently for downstream observability pipelines.
Tech snapshot
Go97%
Python1%
Yacc1%
C1%
C++1%
Shell1%
Frequently asked questions
What storage engine does openGemini use?
It uses an LSM-based storage engine with a high-cardinality columnar format and dedicated compression algorithms.
Is openGemini compatible with existing InfluxDB tools?
Yes, it supports InfluxDB v1.x line protocol, InfluxQL, and the same read/write APIs, allowing reuse of familiar tooling.
How does openGemini achieve scalability?
Through a Massively Parallel Processing (MPP) architecture that lets you add nodes to a cluster for linear performance growth.
Can openGemini be deployed in containers?
Yes, the openGemini-operator provides one-click container deployment on Kubernetes, and the gemix tool supports standalone installations.
What license is openGemini released under?
It is released under the Apache-2.0 license.
Project at a glance
Active- Stars
- 1,141
- Watchers
- 1,141
- Forks
- 175
LicenseApache-2.0
Repo age3 years old
Last commit2 months ago
Primary languageGo
Last synced 36 minutes ago