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Python for DevOps

Learn Ruthlessly Effective Automation

Paperback Engels 2019
Verkooppositie 6055
Verwachte levertijd ongeveer 8 werkdagen


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


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Over Noah Gift

Noah Gift is lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years’ experience programming in Python and is a member of the Python Software Foundation. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture, and is doing CTO-level consulting via Noah Gift Consulting. He has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps. He is also a certified AWS Solutions Architect. Noah has an MBA from University of California, Davis; an MS in computer information systems from California State University, Los Angeles; and a BS in nutritional science from Cal Poly, San Luis Obispo. You can find more about Noah by following him on Github (https://github.com/noahgift/), visiting http://noahgift.com, or connecting with him on https://www.linkedin.com/in/noahgift/.

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What Does DevOps Mean to the Authors?
How to Use This Book
Conceptual Topics
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us

1. Python Essentials for DevOps
Installing and Running Python
The Python Shell
Jupyter Notebooks
Procedural Programming
Basic Math
Built-in Functions
Execution Control
for Loops
while Loops
Handling Exceptions
Built-in Objects
What Is an Object?
Object Methods and Attributes
Anatomy of a Function
Functions as Objects
Anonymous Functions
Using Regular Expressions
Character Sets
Character Classes
Named Groups
Find All
Find Iterator
Lazy Evaluation
Generator Comprehensions
More IPython Features
Using IPython to Run Unix Shell Commands

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
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

4. Useful Linux Utilities
Disk Utilities
Measuring Performance
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
How Fast Is this Snippet?
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
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
Naming Conventions
Why Is It Hard?
The basicconfig
Deeper Configuration
Common Patterns
The ELK Stack
Elasticsearch and Kibana
Case Study Question

8. Pytest for DevOps
Testing Superpowers with pytest
Getting Started with pytest
Testing with pytest
Differences with unittest
pytest Features
The Amazing assert
Getting Started
Built-in Fixtures
Infrastructure Testing
What Is System Validation?
Introduction to Testinfra
Connecting to Remote Nodes
Features and Special Fixtures
Testing Jupyter Notebooks with pytest
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
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
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

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

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

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

14. MLOps and Machine learning Engineering
What Is Machine Learning?
Supervised Machine Learning
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
Tune Scaled GBM
Fit Model
JSON Workflow
Scale Input
adhoc_predict from Pickle
Scale Input
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
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
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
Glenn Solomon
Andrew Nguyen
Gabriella Roman
Rigoberto Roche
Jonathan LaCour
Ville Tuulos
Joseph Reis
Teijo Holzer
Matt Harrison
Michael Foord
Capstone Project


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