ekuiper
eKuiper is a lightweight, open-source stream processing engine designed for IoT edge devices. It enables real-time data analytics and rule-based processing at the edge using SQL or graph-based rules, reducing latency and bandwidth costs.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | lf-edge/ekuiper |
| Owner | lf-edge |
| Primary language | Go |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.7k |
| Forks | 456 |
| Open issues | 43 |
| Latest release | v2.4.0 (2026-05-26) |
| Last updated | 2026-07-06 |
| Source | https://github.com/lf-edge/ekuiper |
What ekuiper is
Go-based event stream processor (~4.5 MB core) supporting SQL queries, 60+ functions, multiple time/count windows, and extensibility via Go/Python plugins for sources, sinks, and UDFs. Runs on constrained hardware (ARM, x86, PPC) and integrates with EMQX, Kubernetes frameworks (KubeEdge, OpenYurt, K3s, Baetyl), and EdgeX.
Get the ekuiper source
Clone the repository and explore it locally.
git clone https://github.com/lf-edge/ekuiper.gitcd ekuiper# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Verify target hardware compatibility (ARM/x86/PPC) and OS support (Linux variants, OpenWrt, macOS, Docker); cross-compilation may be needed.
- Plan plugin development in Go or Python for custom sources/sinks/UDFs; ensure build toolchain and runtime availability in deployment environment.
- Design rule lifecycle: rules can be managed via CLI, REST API, or Kubernetes ConfigMaps; choose governance model early.
- Monitor memory and CPU per-rule (300–500 KB per rule observed); stress-test with expected rule count and message throughput before production.
- Integrate with management dashboard (EMQX-managed or open-source ekuiper-manager) for operational visibility; REST API enables custom tooling.
When to avoid it — and what to weigh
- Complex Machine Learning Pipelines — While eKuiper supports UDF functions for ML invocation, it is not a feature-complete ML platform. For heavy model serving, use dedicated inference engines.
- High-Throughput Cloud Analytics — eKuiper is edge-focused. For cloud-scale stream processing (millions of events/sec), Apache Flink, Kafka Streams, or cloud-native services are more suitable.
- Strongly Consistent Distributed State — eKuiper is single-node or lightly distributed. Applications requiring strict ACID semantics or cross-node transactional consistency should use databases or enterprise platforms.
- Minimal Resource Constraints Are Non-Negotiable — While compact (~10 MB runtime), some embedded systems (sub-10 MB total memory) may find eKuiper still too large. Evaluate on target hardware.
License & commercial use
Licensed under Apache 2.0, a permissive open-source license. Permits commercial use, modification, and distribution with attribution and warranty disclaimer.
Apache 2.0 allows commercial use without royalty. No vendor lock-in or closed-source restrictions. Typical integration: deploy as embedded component or edge service in commercial IoT/IIoT products. Verify any plugin/extension licenses independently. Community support via Slack and GitHub; commercial support availability requires review.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
eKuiper runs on untrusted edge devices; no claims of end-to-end encryption or authentication are detailed in provided materials. Require review of: authentication/authorization for REST API and CLI, TLS for MQTT/broker connections, plugin sandboxing, and compliance needs (e.g., automotive, medical). Data processed at edge may be sensitive; isolation per rule or tenant is not clearly specified.
Alternatives to consider
Apache Flink
General-purpose distributed stream processor with stronger consistency guarantees and larger feature set, but heavier footprint (~100+ MB); better for cloud/on-prem scale.
Node-RED
Visual, low-code flow editor with similar graph-based rule paradigm and lighter weight; lacks SQL interface and advanced analytics functions; more workflow than analytics-focused.
Kafka Streams / Apache Storm
Production stream processing frameworks with distributed clustering and fault tolerance; require more infrastructure and resources; suited for cloud or large-scale deployments.
Build on ekuiper with DEV.co software developers
Evaluate eKuiper for your IoT/IIoT use case. Start with a 5-minute Docker quickstart, review integration with your brokers (EMQX, EdgeX, Kubernetes), and test plugin requirements. Contact us for deployment guidance.
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ekuiper FAQ
Can eKuiper run on a Raspberry Pi?
How do I add custom data sources or transformations?
Is eKuiper secure for production IoT deployments?
How many rules can eKuiper handle?
Custom software development services
Adopting ekuiper is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source databases software in production.
Ready to Deploy Edge Analytics?
Evaluate eKuiper for your IoT/IIoT use case. Start with a 5-minute Docker quickstart, review integration with your brokers (EMQX, EdgeX, Kubernetes), and test plugin requirements. Contact us for deployment guidance.