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)
Other Books You May Enjoy

Search with Elasticsearch

We need more data in Elasticsearch to make the search and graph more interesting. I would recommend reloading a few of the lab devices to have the log entries for interface resets, BGP and OSPF establishments, as well as device boot up messages. Otherwise, feel free to use the sample data we imported at the beginning of this chapter for this section.

If we look back at the script example, when we did the search, there were two pieces of information that could potentially change from each query; the index and query body. What I typically like to do is to break that information into input variables that I can dynamically change at runtime to separate the logic of the search and the script itself. Let's make a file called query_body_1.json:

  "query": {
    "match_all": {}

We will create a script,, that uses argparse to take the user input at the command line: