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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Review of TCP/IP Protocol Suite and Python

Review of TCP/IP Protocol Suite and Python

An overview of the internet

Client-server model

Network protocol suites

Python language overview

Free Chapter

Low-Level Network Device Interactions

Low-Level Network Device Interactions

The challenges of the CLI

Constructing a virtual lab

Python Pexpect library

The Python Paramiko library

The Netmiko library

The Nornir framework

APIs and Intent-Driven Networking

APIs and Intent-Driven Networking

Infrastructure-as-code

The Cisco API and ACI

Cisco Meraki controller

The Python API for Juniper Networks

The Arista Python API

Other libraries

The Python Automation Framework – Ansible Basics

The Python Automation Framework – Ansible Basics

Ansible – a more declarative framework

A quick Ansible example

The Advantages of Ansible

The Ansible architecture

Ansible networking modules

The Ansible Cisco example

Ansible 2.8 playbook example

The Ansible Juniper example

The Ansible Arista example

The Python Automation Framework – Beyond Basics

The Python Automation Framework – Beyond Basics

Lab preparation

Ansible conditionals

Group and host variables

The Ansible Vault

The Ansible include and roles

Writing your own custom module

Network Security with Python

Network Security with Python

The Syslog search

Further reading

Network Monitoring with Python – Part 1

Network Monitoring with Python – Part 1

Python for data visualization

Python for Cacti

Network Monitoring with Python – Part 2

Network Monitoring with Python – Part 2

Flow-based monitoring

ntop traffic monitoring

Building Network Web Services with Python

Building Network Web Services with Python

Comparing Python web frameworks

Flask and lab setup

Introduction to Flask

Network resource API

Network dynamic operations

Authentication and authorization

Running Flask in containers

AWS Cloud Networking

AWS Cloud Networking

AWS network overview

Virtual private cloud

Direct Connect and VPN

Network scaling services

Other AWS network services

Azure Cloud Networking

Azure Cloud Networking

Azure and AWS network service comparison

Azure administration and APIs

Azure global infrastructure

Azure virtual networks

Azure ExpressRoute

Azure Network Load Balancers

Other Azure network services

Network Data Analysis with Elastic Stack

Network Data Analysis with Elastic Stack

What is the Elastic Stack?

Elastic Stack as a Service

First End-to-End example

Elasticsearch with a Python client

Data ingestion with Logstash

Data ingestion with Beats

Search with Elasticsearch

Data visualization with Kibana

Working with Git

Working with Git

Content management considerations and Git

Introduction to Git

Git usage examples

Git with Python

Automating configuration backup

Collaborating with Git

Continuous Integration with Jenkins

Continuous Integration with Jenkins

The traditional change management process

An introduction to continuous integration

Installing Jenkins

Jenkins example

Jenkins with Python

Continuous integration for networking

Test-Driven Development for Networks

Test-Driven Development for Networks

Test-driven development overview

Topology as code

Writing tests for networking

pytest Integration with Jenkins

pyATS and Genie

Other Books You May Enjoy

Other Books You May Enjoy

Index

Customer Reviews

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Data visualization with Kibana

So far, we have used Kibana to discover data, manage indices in Elasticsearch, use developer tools to develop queries, and use a few other features. We also saw the pre-populated visualization charts from NetFlow, which gave us the top talker pair from our data. In this section, we will walk through the steps of creating our own graphs. We will start by creating a pie chart.

A pie chart is great at visualizing a portion of the component in relation to the whole. Let's create a pie chart based on the Filebeat index that graphs the top 10 source IP addresses based on the number of record counts. We will select Visualization -> New Visualization -> Pie:

Figure 21: Kibana pie chart

Then we will type netflow in the search bar to pick our [Filebeat NetFlow] indices:

Figure 22: Kibana pie chart source

By default, we are given the total count of all the records in the default time range. The time range can be dynamically changed...