Book Image

Mastering Python Networking - Third Edition

By : Eric Chou
Book Image

Mastering Python Networking - Third Edition

By: Eric Chou

Overview of this book

Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In Mastering Python Networking, Third edition, you’ll embark on a Python-based journey to transition from traditional network engineers to network developers ready for the next-generation of networks. This new edition is completely revised and updated to work with Python 3. In addition to new chapters on network data analysis with ELK stack (Elasticsearch, Logstash, Kibana, and Beats) and Azure Cloud Networking, it includes updates on using newer libraries such as pyATS and Nornir, as well as Ansible 2.8. Each chapter is updated with the latest libraries with working examples to ensure compatibility and understanding of the concepts. Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security followed by Azure and AWS Cloud networking. Finally, you will use Jenkins for continuous integration as well as testing tools to verify your network.
Table of Contents (18 chapters)
16
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17
Index

Network Monitoring with Python – Part 2

In Chapter 7, Network Monitoring with Python – Part 1, we used SNMP to query information from network devices. We did this by using an SNMP manager to query the SNMP agent residing on the network device. The SNMP information is structured in a hierarchy format with a specific object ID as a way to represent the value of the object. Most of the time, the value we care about is a number, such as CPU load, memory usage, or interface traffic. It's something we can graph against time to give us a sense of how the value has changed over time.

We can typically classify the SNMP approach as a pull method as we are constantly asking the device for a particular answer. This particular method adds a burden to the device because it needs to spend a CPU cycle on the control plane to find answers from the subsystem, package the answer in an SNMP packet, and transport the answer back to the poller. If you have ever been to a family reunion...