Question 1

How to install eKuiper on a Raspberry Pi?

Accepted Answer

You can install eKuiper on Raspberry Pi by downloading the prebuilt ARM binary from GitHub releases or using Docker with the lfedge/ekuiper image. The quick start guide provides detailed steps for both methods, including configuration for edge deployments.

Question 2

eKuiper vs Apache Flink for edge computing?

Accepted Answer

eKuiper is optimized for lightweight, resource-constrained edge devices with minimal footprint, while Apache Flink is designed for cloud-scale, distributed stream processing. Choose eKuiper for edge analytics with low latency and bandwidth savings, and Flink for complex, high-throughput data center applications.

Question 3

Can eKuiper handle real-time data from MQTT brokers?

Accepted Answer

Yes, eKuiper has native MQTT source and sink support, allowing real-time subscription and publishing to MQTT brokers like EMQX. It processes data with SQL or graph-based rules, reducing latency for IoT scenarios as shown in performance tests.

Question 4

How to add custom Python functions for AI in eKuiper?

Accepted Answer

Extend eKuiper by writing Python UDFs and registering them as plugins. The documentation guides you through creating a Python plugin, defining functions, and configuring them in eKuiper to invoke AI/ML models for edge analytics.

Question 5

What are the performance limits of eKuiper on AWS instances?

Accepted Answer

Performance tests indicate eKuiper handles up to 10k-11k messages per second on AWS t2.micro instances, with CPU usage around 25-75% and memory ~20-32MB. Scalability is capped by hardware, as seen in benchmark results for multiple rules.

Question 6

Does eKuiper support Kafka as a data source?

Accepted Answer

eKuiper does not have built-in Kafka support, but you can extend it by creating a custom source plugin in Go or Python. The extensible architecture allows integration with various data systems, though it requires development effort.

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