Python Library

In this lesson, you will learn:

• How to build a Suzieq query in Python.
• About Pandas DataFrames.
• How to build Python scripts to:
  • Check a NOS version at a previous point in time.
  • Query the total interfaces that are down.
  • Validate that OSPF is correctly operating.
• How to build a Python script with Pytest to validate NOS versions and OSPF state.

You can find the scripts and code for this lesson within the Suzieq repo under the directory: 003_suzieq_interaction_methods/001_python.

Suzieq Python Intro

Suzieq lets you interact and work with the tables and stored data in Python using the Suzieq Python library. The library exposes the various table operations (show, assert etc), and filtering options (end_time, columns etc) that we have previously seen in this course.

The first thing we need to learn is how to construct a Suzieq query from within Python. Once we know how to do this, we can apply our knowledge to some real-world examples.

Suzieq Python Fundamentals

Environment

First of all, we will need to enter our Python environment within one of our containers:

$ docker-compose exec suzieq_poller python3
Python 3.7.11 (default, Jul 22 2021, 15:59:35) 
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

The reason that we are using this environment over creating a local environment (with virtualenv etc), is that the Suzieq Python library requires access to the DB (Parquet files).

Note: You can locate the Parquet files outside of your local environment by using a form of remote storage, such as NFS or S3, which you can expose to your container using Docker.

Python Imports

Now we are in our environment, we can perform the required import:

from suzieq.sqobjects import get_sqobject

Create Table Instance

We can now create an instance by providing the table name:

interface_tbl = get_sqobject('interfaces')

To get a list of all the available tables that we can use with get_sqobject, we can perform the following: