高性能Python

Preface

1. Understanding Performant Python

The Fundamental Computer System

Computing Units

Memory Units

Communications Layers

Putting the Fundamental Elements Together

Idealized Computing Versus the Python Virtual Machine

So Why Use Python？

2. Profiling to Find Bottlenecks

Profiling Efficiently

Introducing the Julia Set

Calculating the Full Julia Set

Simple Approaches to Timing——print and a Decorator

Simple Timing Using the Unix time Command

3. Lists and Tuples

A More Efficient Search

Lists Versus Tuples

Lists as Dynamic Arrays

Tuples As Static Arrays

Wrap-Up

4. Dictionaries and Sets

How Do Dictionaries and Sets Work？

Inserting and Retrieving

Deletion

Resizing

Hash Functions and Entropy

Dictionaries and Namespaces

Wrap-Up

5. Iterators and Generators

Iterators for Infinite Series

Lazy Generator Evaluation

Wrap-Up

6. Matrix and Vector Computation

Introduction to the Problem

Aren't Python Lists Good Enough？

Problems with Allocating Too Much

Memory Fragmentation

Understanding perf

Making Decisions with perf's Output

Enter numpy

Applying numpy to the Diffusion Problem

Memory Allocations and In-Place Operations

Selective Optimizations： Finding What Needs to Be Fixed

numexpr： Making In-Place Operations Faster and Easier

A Cautionary Tale： Verify “Optimizations” （scipy）

Wrap-Up

7. Compiling to C

What Sort of Speed Gains Are Possible？

JIT Versus AOT Compilers

Why Does Type Information Help the Code Run Faster？

Using a C Compiler

Reviewing the Julia Set Example

Cvthon

Compiling a Pure-Python Version Using Cython

Cython Annotations to Analyze a Block of Code

Adding Some Type Annotations

Shed Skin

Building an Extension Module

The Cost of the Memory Copies

Cython and numpy

ParaUelizing the Solution with OpenMP on One Machine

Numba

Pythran

PyPy

Garbage Collection Differences

Running PyPy and Installing Modules

When to Use Each Technology

Other Upcoming Projects

A Note on Graphics Processing Units （GPUs）

A Wish for a Future Compiler Project

Foreign Function Interfaces

ctypes

cffi

f2py

CPython Module

Wrap-Up

8. Concurrency

Introduction to Asynchronous Programming

Serial Crawler

gevent

tornado

AsyncIO

Database Example

Wrap-Up

9. lhe multiprocessing Module

An Overview of the Multiprocessing Module

Estimating Pi Using the Monte Carlo Method

Estimating Pi Using Processes and Threads

Using Python Objects

Random Numbers in Parallel Systems

Using numpy

Finding Prime Numbers

Queues of Work

Verifying Primes Using Interprocess Communication

Serial Solution

Naive Pool Solution

A Less Naive Pool Solution

Using Manager.Value as a Flag

Using Redis as a Flag

Using RawValue as a Flag

Using mmap as a Flag

Using mmap as a Flag Redux

Sharing numpy Data with multiprocessing

Synchronizing File and Variable Access

File Locking

Locking a Value

Wrap-Up

10. Clusters and Job Queues

Benefits of Clustering

Drawbacks of Clustering

$462 Million Wall Street Loss Through Poor Cluster Upgrade Strategy

Skype's 24-Hour Global Outage

Common Cluster Designs

How to Start a Clustered Solution

Ways to Avoid Pain When Using Clusters

Three Clustering Solutions

Using the Parallel Python Module for Simple Local Clusters

Using IPython Parallel to Support Research

NSQ for Robust Production Clustering

Queues

Pub/sub

Distributed Prime Calculation

Other Clustering Tools to Look At

Wrap-Up

11. Using Less RAM

Objects for Primitives Are Expensive

The Array Module Stores Many Primitive Objects Cheaply

Understanding the RAM Used in a Collection

Bytes Versus Unicode

Efficiently Storing Lots of Text in RAM

Trying These Approaches on 8 Million Tokens

Tips for Using Less RAM

Probabilistic Data Structures

Very Approximate Counting with a 1-byte Morris Counter

K-Minimum Values

Bloom Filters

LogLog Counter

Real-World Example

12. Lessons from the Field

Adaptive Lab's Social Media Analytics （SOMA）

Python at Adaptive Lab

SoMA's Design

Our Development Methodology

Maintaining SoMA

Advice for Fellow Engineers

Making Deep Learning Fly with RadimRehurek.com

The Sweet Spot

Lessons in Optimizing

Wrap-Up

Large-Scale Productionized Machine Learning at Lyst.com

Pythons Place at Lyst

Cluster Design

Code Evolution in a Fast-Moving Start-Up

Building the Recommendation Engine

Reporting and Monitoring

Some Advice

Large-Scale Social Media Analysis at Smesh

Pythons Role at Smesh

The Platform

High Performance Real-Time String Matching

Reporting， Monitoring， Debugging， and Deployment

PyPy for Successful Web and Data Processing Systems

Prerequisites

The Database

The Web Application

OCR and Translation

Task Distribution and Workers

Conclusion

Task Queues at Lanyrd.com

Python's Role at Lanyrd

Making the Task Queue Performant

Reporting， Monitoring， Debugging， and Deployment

Advice to a Fellow Developer

Index

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