Wednesday, March 16, 2016

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
EXPOSE 8008 6343/udp
CMD    /etc/init.d/sflow-rt start && tail -f /dev/null
Build the project:
docker 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.

Update July 22, 2016: sFlow-RT can now be run from Docker Hub, see sflow/sflow-rt for instructions.

Now configure sFlow agents to send data to the docker_host on port 6343:
The following articles provide examples of using the sFlow-RT REST API:
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.

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