Python for DevOps Learn Ruthlessly Effective Automation 1st Edition by Noah Gift, Kennedy Behrman, Alfredo Deza, Grig Gheorghiu 149205769X 9781492057697

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ISBN 10: 149205769X 

ISBN 13: 9781492057697

Author: Noah Gift, Kennedy Behrman, Alfredo Deza, Grig Gheorghiu

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to “get stuff done” in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project

Python for DevOps Learn Ruthlessly Effective Automation 1st Table of contents:

1. Python Essentials for DevOps

Installing and Running Python

The Python Shell

Jupyter Notebooks

Procedural Programming

Variables

Basic Math

Comments

Built-in Functions

Print

Range

Execution Control

if/elif/else

for Loops

while Loops

Handling Exceptions

Built-in Objects

What Is an Object?

Object Methods and Attributes

Sequences

Functions

Anatomy of a Function

Functions as Objects

Anonymous Functions

Using Regular Expressions

Searching

Character Sets

Character Classes

Groups

Named Groups

Find All

Find Iterator

Substitution

Compiling

Lazy Evaluation

Generators

Generator Comprehensions

More IPython Features

Using IPython to Run Unix Shell Commands

Exercises

2. Automating Files and the Filesystem

Reading and Writing Files

Using Regular Expressions to Search Text

Dealing with Large Files

Encrypting Text

Hashing with Hashlib

Encryption with Cryptography

The os Module

Managing Files and Directories Using os.path

Walking Directory Trees Using os.walk

Paths as Objects with Pathlib

3. Working with the Command Line

Working with the Shell

Talking to the Interpreter with the sys Module

Dealing with the Operating System Using the os Module

Spawn Processes with the subprocess Module

Creating Command-Line Tools

Using sys.argv

Using argparse

Using click

fire

Implementing Plug-ins

Case Study: Turbocharging Python with Command-Line Tools

Using the Numba Just-in-Time (JIT) Compiler

Using the GPU with CUDA Python

Running True Multicore Multithreaded Python Using Numba

KMeans Clustering

Exercises

4. Useful Linux Utilities

Disk Utilities

Measuring Performance

Partitions

Retrieving Specific Device Information

Network Utilities

SSH Tunneling

Benchmarking HTTP with Apache Benchmark (ab)

Load Testing with molotov

CPU Utilities

Viewing Processes with htop

Working with Bash and ZSH

Customizing the Python Shell

Recursive Globbing

Searching and Replacing with Confirmation Prompts

Removing Temporary Python Files

Listing and Filtering Processes

Unix Timestamp

Mixing Python with Bash and ZSH

Random Password Generator

Does My Module Exist?

Changing Directories to a Module’s Path

Converting a CSV File to JSON

Python One-Liners

Debuggers

How Fast Is this Snippet?

strace

Exercises

Case Study Question

5. Package Management

Why Is Packaging Important?

When Packaging Might Not Be Needed

Packaging Guidelines

Descriptive Versioning

The changelog

Choosing a Strategy

Packaging Solutions

Native Python Packaging

Debian Packaging

RPM Packaging

Management with systemd

Long-Running Processes

Setting It Up

The systemd Unit File

Installing the Unit

Log Handling

Exercises

Case Study Question

6. Continuous Integration and Continuous Deployment

Real-World Case Study: Converting a Poorly Maintained WordPress Site to Hugo

Setting Up Hugo

Converting WordPress to Hugo Posts

Creating an Algolia Index and Updating It

Orchestrating with a Makefile

Deploying with AWS CodePipeline

Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build

Real-World Case Study: NFSOPS

7. Monitoring and Logging

Key Concepts in Building Reliable Systems

Immutable DevOps Principles

Centralized Logging

Case Study: Production Database Kills Hard Drives

Did You Build It or Buy It?

Fault Tolerance

Monitoring

Graphite

StatsD

Prometheus

Instrumentation

Naming Conventions

Logging

Why Is It Hard?

The basicconfig

Deeper Configuration

Common Patterns

The ELK Stack

Logstash

Elasticsearch and Kibana

Exercises

Case Study Question

8. Pytest for DevOps

Testing Superpowers with pytest

Getting Started with pytest

Testing with pytest

Differences with unittest

pytest Features

conftest.py

The Amazing assert

Parametrization

Fixtures

Getting Started

Built-in Fixtures

Infrastructure Testing

What Is System Validation?

Introduction to Testinfra

Connecting to Remote Nodes

Features and Special Fixtures

Examples

Testing Jupyter Notebooks with pytest

Exercises

Case Study Question

9. Cloud Computing

Cloud Computing Foundations

Types of Cloud Computing

Types of Cloud Services

Infrastructure as a Service

Metal as a Service

Platform as a Service

Serverless Computing

Software as a Service

Infrastructure as Code

Continuous Delivery

Virtualization and Containers

Hardware Virtualization

Software Defined Networks

Software Defined Storage

Containers

Challenges and Opportunities in Distributed Computing

Python Concurrency, Performance, and Process Management in the Cloud Era

Process Management

Manage Processes with Subprocess

Using Multiprocessing to Solve Problems

Forking Processes with Pool()

Function as a Service and Serverless

High Performance Python with Numba

Using Numba Just in Time Compiler

Using High-Performance Servers

Conclusion

Exercises

Case Study Questions

10. Infrastructure as Code

A Classification of Infrastructure Automation Tools

Manual Provisioning

Automated Infrastructure Provisioning with Terraform

Provisioning an S3 Bucket

Provisioning an SSL Certificate with AWS ACM

Provisioning an Amazon CloudFront Distribution

Provisioning a Route 53 DNS Record

Copying Static Files to S3

Deleting All AWS Resources Provisioned with Terraform

Automated Infrastructure Provisioning with Pulumi

Creating a New Pulumi Python Project for AWS

Creating Configuration Values for the Staging Stack

Provisioning an ACM SSL Certificate

Provisioning a Route 53 Zone and DNS Records

Provisioning a CloudFront Distribution

Provisioning a Route 53 DNS Record for the Site URL

Creating and Deploying a New Stack

Exercises

11. Container Technologies: Docker and Docker Compose

What Is a Docker Container?

Creating, Building, Running, and Removing Docker Images and Containers

Publishing Docker Images to a Docker Registry

Running a Docker Container with the Same Image on a Different Host

Running Multiple Docker Containers with Docker Compose

Porting the docker-compose Services to a New Host and Operating System

Exercises

12. Container Orchestration: Kubernetes

Short Overview of Kubernetes Concepts

Using Kompose to Create Kubernetes Manifests from docker-compose.yaml

Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

Launching a GKE Kubernetes Cluster in GCP with Pulumi

Deploying the Flask Example Application to GKE

Installing Prometheus and Grafana Helm Charts

Destroying the GKE Cluster

Exercises

13. Serverless Technologies

Deploying the Same Python Function to the “Big Three” Cloud Providers

Installing Serverless Framework

Deploying Python Function to AWS Lambda

Deploying Python Function to Google Cloud Functions

Deploying Python Function to Azure

Deploying a Python Function to Self-Hosted FaaS Platforms

Deploying Python Function to OpenFaaS

Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

Exercises

14. MLOps and Machine learning Engineering

What Is Machine Learning?

Supervised Machine Learning

Modeling

Python Machine learning Ecosystem

Deep Learning with PyTorch

Cloud Machine learning Platforms

Machine learning Maturity Model

Machine Learning Key Terminology

Level 1: Framing, Scope Identification, and Problem Definition

Level 2: Continuous Delivery of Data

Level 3: Continuous Delivery of Clean Data

Level 4: Continuous Delivery of Exploratory Data Analysis

Level 5: Continuous Delivery of Traditional ML and AutoML

Level 6: ML Operational Feedback Loop

Sklearn Flask with Kubernetes and Docker

Sklearn Flask with Kubernetes and Docker

EDA

Modeling

Tune Scaled GBM

Fit Model

Evaluate

adhoc_predict

JSON Workflow

Scale Input

adhoc_predict from Pickle

Scale Input

Exercises

Case Study Question

Learning Assessments

15. Data Engineering

Small Data

Dealing with Small Data Files

Write a File

Read a File

Generator Pipeline to Read and Process Lines

Using YAML

Big Data

Big Data Tools, Components, and Platforms

Data Sources

Filesystems

Data Storage

Real-Time Streaming Ingestion

Case Study: Building a Homegrown Data Pipeline

Serverless Data Engineering

Using AWS Lambda with CloudWatch Events

Using Amazon CloudWatch Logging with AWS Lambda

Using AWS Lambda to Populate Amazon Simple Queue Service

Wiring Up CloudWatch Event Trigger

Creating Event-Driven Lambdas

Reading Amazon SQS Events from AWS Lambda

Conclusion

Exercises

Case Study Question

16. DevOps War Stories and Interviews

Film Studio Can’t Make Film

Game Studio Can’t Ship Game

Python Scripts Take 60 Seconds to Launch

Putting Out a Fire with a Cache and Intelligent Instrumentation

You’ll Automate Yourself Out of a Job!

DevOps Antipatterns

No Automated Build Server Antipattern

Flying Blind

Difficulties in Coordination as an Ongoing Accomplishment

No Teamwork

Interviews

Glenn Solomon

Andrew Nguyen

Gabriella Roman

Rigoberto Roche

Jonathan LaCour

Ville Tuulos

Joseph Reis

Teijo Holzer

Matt Harrison

Michael Foord

Recommendations

Exercises

Challenges

Capstone Project

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Noah Gift,Kennedy Behrman,Alfredo Deza,Grig Gheorghiu,Python