Software Architecture with Python

Anand Balachandran Pillai

• Éditeur: Packt Publishing
• Année: 2017
• ISBN:
  9781786467225
• Édition: 1
• Sujet: Computer Science
• Sous-sujet: Programming in Python

Architect and design highly scalable, robust, clean, and highly performant applications in PythonAbout This Book• Identify design issues and make the necessary adjustments to achieve improved performance• Understand practical architectural quality attributes from the perspective of a practicing engineer and architect using Python• Gain knowledge of architectural principles and how they can be used to provide accountability and rationale for architectural decisionsWho This Book Is ForThis book is for experienced Python developers who are aspiring to become the architects of enterprise-grade applications or software architects who would like to leverage Python to create effective blueprints of applications.What You Will Learn• Build programs with the right architectural attributes• Use Enterprise Architectural Patterns to solve scalable problems on the Web• Understand design patterns from a Python perspective• Optimize the performance testing tools in Python• Deploy code in remote environments or on the Cloud using Python• Secure architecture applications in PythonIn DetailThis book starts off by explaining how Python fits into an application architecture. As you move along, you will understand the architecturally significant demands and how to determine them. Later, you'll get a complete understanding of the different architectural quality requirements that help an architect to build a product that satisfies business needs, such as maintainability/reusability, testability, scalability, performance, usability, and security.You will use various techniques such as incorporating DevOps, Continuous Integration, and more to make your application robust. You will understand when and when not to use object orientation in your applications. You will be able to think of the future and design applications that can scale proportionally to the growing business.The focus is on building the business logic based on the business process documentation and which frameworks are to be used when. We also cover some important patterns that are to be taken into account while solving design problems as well as those in relatively new domains such as the Cloud.This book will help you understand the ins and outs of Python so that you can make those critical design decisions that not just live up to but also surpass the expectations of your clients.Style and approachFilled with examples and use cases, this guide takes a no-nonsense approach to help you with everything it takes to become a successful software architect.

Software Architecture with Python

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Table of Contents

Software Architecture with Python

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Principles of Software Architecture

Defining Software Architecture

Software Architecture versus design

Aspects of Software Architecture

Characteristics of Software Architecture

An architecture defines a structure

An architecture picks a core set of elements

An architecture captures early design decisions

An architecture manages stakeholder requirements

An architecture influences the organizational structure

An architecture is influenced by its environment

An architecture documents the system

An architecture often conforms to a pattern

Importance of Software Architecture

System versus enterprise architecture

Architectural quality attributes

Modifiability

Testability

Scalability

Performance

Availability

Security

Deployability

Summary

2. Writing Modifiable and Readable Code

What is modifiability?

Aspects related to Modifiability

Understanding readability

Python and readability

Readability - antipatterns

Techniques for readability

Document your code

Follow coding and style guidelines

Review and refactor code

Commenting the code

Fundamentals of Modifiability – Cohesion & Coupling

Measuring cohesion and coupling

Measuring cohesion and coupling – string and text processing

Exploring strategies for modifiability

Providing explicit interfaces

Reducing two-way dependencies

Abstract common services

Using inheritance techniques

Using late binding techniques

Metrics – tools for static analysis

What are code smells?

Cyclomatic complexity – the McCabe metric

Testing for metrics

Running Static Checkers

Refactoring Code

Refactoring code – fixing complexity

Refactoring code - fixing code smells

Refactoring code - fixing styling and coding issues

Summary

3. Testability – Writing Testable Code

Understanding testability

Software testability and related attributes

Testability – architectural aspects

Testability – strategies

Reduce system complexity

Improving predictability

Control and isolate external dependencies

White-box testing principles

Unit testing

Unit testing in action

Extending our unit test case

Nosing around with nose2

Testing with py.test

Code coverage

Measuring coverage using coverage.py

Measuring coverage using nose2

Measuring coverage using py.test

Mocking things up

Tests inline in documentation – doctests

Integration tests

Test automation

Test automation using Selenium Web Driver

Test-Driven Development

TDD with palindromes

Summary

4. Good Performance is Rewarding!

What is performance?

Software performance engineering

Performance testing and measurement tools

Performance complexity

Measuring performance

Measuring time using a context manager

Timing code using the timeit module

Measuring the performance of our code using timeit

Finding out time complexity – graphs

Measuring CPU time with timeit

Profiling

Deterministic profiling

Profiling with cProfile and profile

Prime number iterator class – performance tweaks

Profiling – collecting and reporting statistics

Third-party profilers

Line profiler

Memory profiler

Substring (subsequence) problem

Other tools

Objgraph

Pympler

Programming for performance – data structures

Mutable containers – lists, dictionaries, and sets

Lists

Dictionaries

Sets

Immutable containers – tuples

High performance containers – the collections module

deque

defaultdict

OrderedDict

Dropping duplicates from a container without losing the order

Implementing a Least Recently Used (LRU) cache dictionary

Counter

ChainMap

namedtuple

Probabilistic data structures – bloom filters

Summary

5. Writing Applications That Scale

Scalability and performance

Concurrency

Concurrency versus parallelism

Concurrency in Python – multithreading

Thumbnail generator

Thumbnail generator – producer/consumer architecture

Thumbnail generator – resource constraint using locks

Thumbnail generator – resource constraint using semaphores

Resource constraint – semaphore versus lock

Thumbnail generator – URL rate controller using conditions

Multithreading – Python and GIL

Concurrency in Python – multiprocessing

A primality checker

Sorting disk files

Sorting disk files – using a counter

Sorting disk files – using multiprocessing

Multithreading versus multiprocessing

Concurrecy in Python - Asynchronous Execution

Pre-emptive versus cooperative multitasking

The asyncio module in Python

Waiting for a future – async and await

Concurrent futures – high-level concurrent processing

Disk thumbnail generator

Concurrency options – how to choose?

Parallel processing libraries

Joblib

PyMP

Fractals – the Mandelbrot set

Fractals – Scaling the Mandelbrot set implementation

Scaling for the Web

Scaling workflows – message queues and task queues

Celery – a distributed task queue

The Mandelbrot set using Celery

Serving with Python on the Web—WSGI

uWSGI – WSGI middleware on steroids

Gunicorn – unicorn for WSGI

Gunicorn versus uWSGI

Scalability architectures

Vertical scalability architectures

Horizontal scalability architectures

Summary

6. Security – Writing Secure Code

Information Security architecture

Secure coding

Common security vulnerabilities

Is Python secure?

Reading input

Evaluating arbitrary input

Overflow errors

Serializing objects

Security issues with web applications

Server Side Template Injection

Server-Side Template Injection – mitigation

Denial of Service

Cross-Site Scripting(XSS)

Mitigation – DoS and XSS

Strategies for security – Python

Secure coding strategies

Summary

7. Design Patterns in Python

Design patterns - Elements

Categories of design patterns

Pluggable hashing algorithms

Summing up pluggable hashing algorithm

Patterns in Python – Creational

The Singleton pattern

The Singleton – do we need a Singleton?

State sharing – Borg versus Singleton

The Factory pattern

The Prototype pattern

Prototype – deep versus shallow copy

Prototype using metaclasses

Combining patterns using metaclasses

The Prototype factory

The Builder pattern

Patterns in Python – Structural

The Adapter pattern

The Facade pattern

Facades in Python

The Proxy pattern

An instance-counting proxy

Patterns in Python – Behavioral

The Iterator pattern

The Observer pattern

The State pattern

Summary

8. Python – Architectural Patterns

Introducing MVC

Model Template View (MTV) – Django

Django admin – automated model-centric views

Flexible Microframework – Flask

Event-driven programming

Chat server and client using I/O multiplexing with the select module

Event-drive...