Sunday, June 11, 2023

Raspberry Pi 4 real-time network analytics

CanaKit Raspberry Pi 4 EXTREME Kit - Aluminum
This article describes how build an inexpensive Raspberry Pi 4 based server for real-time flow analytics of industry standard sFlow streaming telemetry. Support for sFlow is widely implemented in datacenter equipment from vendors including: A10, Arista, Aruba, Cisco, Edge-Core, Extreme, Huawei, Juniper, NEC, Netgear, Nokia, NVIDIA, Quanta, and ZTE.

In this example, we will use an 8G Raspberry Pi 4 running Raspberry Pi OS Lite (64-bit).  The easiest way to format a memory card and install the operating system is to use the Raspberry Pi Imager (shown above).
Click on the gear icon to set a user and password and enable ssh access. These initial settings allow the Rasberry Pi to be accessed over the network without having to attach a screen, keyboard, and mouse.

Next, follow instruction for installing Docker Engine (Raspberry Pi OS Lite is based on Debian 11).

The diagram shows how the sFlow-RT real-time analytics engine receives a continuous telemetry stream from industry standard sFlow instrumentation build into network, server and application infrastructure and delivers analytics through APIs and can easily be integrated with a wide variety of on-site and cloud, orchestration, DevOps and Software Defined Networking (SDN) tools.
docker run -p 6343:6343/udp -p \
--name sflow-rt -d --restart unless-stopped sflow/prometheus
Run the pre-built sflow/prometheus Docker image. In this example access to the user interface is limited to local host in order prevent unauthorized access since no access controls are provided by sFlow-RT.
ssh -L 8008: pp@
Use ssh to connect to the Raspberry Pi ( and tunnel port 8008 to your laptop.
Access the web interface at See Getting Started for instructions for enabling monitoring and browsing metrics. Python is installed by default on Raspberry Pi OS, making it convenient to experiment with the sFlow-RT REST API, see Writing Applications.
If you don't have immediate access to a network and want to experiment, follow the instructions in Leaf and spine network emulation on Mac OS M1/M2 systems to emulate the 5 stage leaf and spine network shown above using Containerlab.
docker stop sflow-rt
Note: If you are going to try the examples, first run the command above to stop the sflow-rt image to avoid port contention when Containerlab starts an instance of sFlow-RT.
The screen capture shows a real-time view of traffic flowing across the the emulated leaf and spine network during a series iperf3 tests. The emulated results are very close to those you can expect when monitoring production traffic on a physical network.

The Raspberry Pi 4 is surprisingly capable, this pocket-sized server can easily monitor hundreds of high speed (100G+) links, providing up to the second visibility into network flows.

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