دانلود کتاب پایتون سریع – تکنیک های عملکرد بالا برای مجموعه داده های بزرگ

A few years ago, a Python-based pipeline that my team was working on suddenly ground to a halt. A process just kept using CPU and was not finalizing. This function was critical to the company and we needed to solve the problem sooner rather than later. We looked at the algorithm and it seemed OK—in fact, it was quite a simple implementation. After many hours with several engineers looking at the problem, we found that it all boiled down to searching on a list—a very big list. The problem was trivially solved after converting the list into a set. We ended up with a much smaller data structure with search times in milliseconds, not hours. I had several epiphanies at that time: ■ It was a trivial problem, but our development process was not concerned with performance issues. For example, if we had routinely used a profiler, we would have discovered the performance bug in minutes, not hours. ■ This was a win-win situation: we ended up consuming less time and less memory. Yes, in many cases, there are tradeoffs to be made, but in others, there are some really effective results with no downsides. ■ From a larger perspective, this situation was also a win-win. First, faster results are great for the company’s bottom line. Second, a good algorithm uses less CPU time, which means less electricity, and the use of less electricity (i.e., resources) is better for the planet. ■ While our single case doesn’t do much to save energy, it dawned on me that many programmers are designing similar solutions. I decided to write this book so other programmers could benefit from my epiphanies. My objective is to help seasoned Python programmers to design and implement solutions that are more efficient, along with with an understanding of the potential tradeoffs. I wanted to take a holistic approach to the subject by discussing pure Python and important Python libraries, taking an algorithmic perspective and considering modern hardware architectures and their implications, and discussing CPU and storage performance. I hope this book helps you to be more confident in approaching performance problems while developing in the Python ecosystem.