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Python High Performance, Second Edition
Python High Performance, Second Edition

Python High Performance, Second Edition: Build high-performing, concurrent, and distributed applications , Second Edition

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Python High Performance, Second Edition

Pure Python Optimizations

As mentioned in the last chapter, one of the most effective ways of improving the performance of applications is through the use of better algorithms and data structures. The Python standard library provides a large variety of ready-to-use algorithms and data structures that can be directly incorporated in your applications. With the tools learned from this chapter, you will be able to use the right algorithm for the task and achieve massive speed gains.

Even though many algorithms have been around for quite a while, they are especially relevant in today's world as we continuously produce, consume, and analyze ever increasing amounts of data. Buying a larger server or microoptimizing can work for some time, but achieving better scaling through algorithmic improvement can solve the problem once and for all.

In this chapter, we will understand how to achieve better scaling using standard...

Useful algorithms and data structures

Algorithmic improvements are especially effective in increasing performance because they typically allow the application to scale better with increasingly large inputs.

Algorithm running times can be classified according to their computational complexity, a characterization of the resources required to perform a task. Such classification is expressed through the Big-O notation, an upper bound on the operations required to execute the task, which usually depends on the input size.

For example, incrementing each element of a list can be implemented using a for loop, as follows:

    input = list(range(10))
for i, _ in enumerate(input):
input[i] += 1

If the operation does not depend on the size of the input (for example, accessing the first element of a list), the algorithm is said to take constant, or O(1), time. This means that, no matter how much data we have, the...

Caching and memoization

Caching is a great technique used to improve the performance of a wide range of applications. The idea behind caching is to store expensive results in a temporary location, called cache, that can be located in memory, on-disk, or in a remote location.

Web applications make extensive use of caching. In a web application, it often happens that users request a certain page at the same time. In this case, instead of recomputing the page for each user, the web application can compute it once and serve the user the already rendered page. Ideally, caching also needs a mechanism for invalidation so that if the page needs to be updated, we can recompute it before serving it again. Intelligent caching allows web applications to handle increasing number of users with less resources. Caching can also be done preemptively, such as the later sections of the video get buffered when watching a video online...

Comprehensions and generators

In this section, we will explore a few simple strategies to speed up Python loops using comprehension and generators. In Python, comprehension and generator expressions are fairly optimized operations and should be preferred in place of explicit for-loops. Another reason to use this construct is readability; even if the speedup over a standard loop is modest, the comprehension and generator syntax is more compact and (most of the times) more intuitive.

In the following example, we can see that both the list comprehension and generator expressions are faster than an explicit loop when combined with the sum function:

    def loop(): 
res = []
for i in range(100000):
res.append(i * i)
return sum(res)

def comprehension():
return sum([i * i for i in range(100000)])

def generator():
return sum(i * i for i in range(100000))

%timeit...

Summary

Algorithmic optimization can improve how your application scales as we process increasingly large data. In this chapter, we demonstrated use-cases and running times of the most common data structures available in Python, such as lists, deques, dictionaries, heaps, and tries. We also covered caching, a technique that can be used to trade some space, in memory or on-disk, in exchange for increased responsiveness of an application. We also demonstrated how to get modest speed gains by replacing for-loops with fast constructs, such as list comprehensions and generator expressions.

In the subsequent chapters, we will learn how to improve performance further using numerical libraries such as numpy, and how to write extension modules in a lower-level language with the help of Cython.

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Key benefits

  • Identify the bottlenecks in your applications and solve them using the best profiling techniques
  • Write efficient numerical code in NumPy, Cython, and Pandas
  • Adapt your programs to run on multiple processors and machines with parallel programming

Description

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.

Who is this book for?

The book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expected

What you will learn

  • Write efficient numerical code with the NumPy and Pandas libraries
  • Use Cython and Numba to achieve native performance
  • Find bottlenecks in your Python code using profilers
  • Write asynchronous code using Asyncio and RxPy
  • Use Tensorflow and Theano for automatic parallelism in Python
  • Set up and run distributed algorithms on a cluster using Dask and PySpark
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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 24, 2017
Length: 270 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787282896
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Frequently bought together


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Table of Contents

9 Chapters
Benchmarking and Profiling Chevron down icon Chevron up icon
Pure Python Optimizations Chevron down icon Chevron up icon
Fast Array Operations with NumPy and Pandas Chevron down icon Chevron up icon
C Performance with Cython Chevron down icon Chevron up icon
Exploring Compilers Chevron down icon Chevron up icon
Implementing Concurrency Chevron down icon Chevron up icon
Parallel Processing Chevron down icon Chevron up icon
Distributed Processing Chevron down icon Chevron up icon
Designing for High Performance Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(2 Ratings)
5 star 50%
4 star 0%
3 star 50%
2 star 0%
1 star 0%
Dimitri Shvorob Feb 23, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Thinking how to rate this book has been a bit of a roller-coaster:Up: "Is this really a Packt book? It's very well written, I like it!"Down: "Hold on - there is another "High-performance Python" book, published by O'Reilly in 2014? The TOCs sure have more than a few similarities. Is this one a copycat?"Up: "No, it isn't. I see how this book is different, and see what it brings to the table. Oh, and first edition of this book came out in 2013, so this is actually the earliest book on the subject, it seems".In the end, a very positive evaluation. Yes, "High-performance Python" by by Gorelick and Ozsvald is an obvious competitor, and a strong one - a more advanced book than Lanaro's, I would guess. On the other hand, as a newbie to the world of high-performance Python, I know that I find Lanaro's explanation much easier to follow. This is the book I am actually going to use to learn the subject. Many thanks, Gabriele, five stars from me.
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Parijat Sengupta May 19, 2023
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
A neat compilation of online guides/tutorials. Convoluted explanations that are typical of short articles posted on multiple forums. The worst chapter is perhaps the one on implementing concurrency (chapter 6). A redeeming feature is a good explanatory section on the indexing patterns of Numpy. There isn’t much to expect from Packt books in general, as they seem to be put together in haste with quality control an obvious casualty - this title lives up to that billing.
Amazon Verified review Amazon
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