Network and system analytics as a Docker microservice

Microservices describes why the industry standard sFlow instrumentation embedded within cloud infrastructure is uniquely able to provide visibility into microservice deployments.

The sFlow-RT analytics engine is well suited to deployment as a Docker microservice since the application is stateless and presents network and system analytics as a RESTful service.

The following steps demonstrate how to create a containerized deployment of sFlow-RT.

First, create a directory for the project and edit the Dockerfile:

mkdir sflow-rt
cd sflow-rt
vi Dockerfile

Add the following contents to Dockerfile:

FROM   centos:centos6
RUN    yum install -y java-1.7.0-openjdk
RUN    rpm -i http://www.inmon.com/products/sFlow-RT/sflow-rt-2.0-1072.noarch.rpm
EXPOSE 8008 6343/udp
CMD    /etc/init.d/sflow-rt start && tail -f /dev/null

Build the project:

build -t sflow-rt .

Run the service:

docker run -p 8008:8008 -p 6343:6343/udp -d sflow-rt

Access the API at http://docker_host:8008/ to verify that the service is running.

Now configure sFlow agents to send data to the docker_host on port 6343:

• Switch configurations
• Install Host sFlow agents to monitor hosts, hypervisors, Docker, etc.

The following articles provide examples of using the sFlow-RT REST API:

• RESTflow
• Cluster performance metrics

The diagram shows how new and existing cloud based or locally hosted orchestration, operations, and security tools can leverage sFlow-RT's analytics service to gain real-time visibility. The solution is extremely scaleable, a single sFlow-RT instance can monitor thousands of servers and the network devices connecting them.