May 2017
Intermediate to advanced
270 pages
6h 18m
English
Content preview from Python High Performance - Second Edition
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Summary
Distributed processing can be used to implement algorithms capable of handling massive datasets by distributing smaller tasks across a cluster of computers. Over the years, many software packages, such as Apache Hadoop, have been developed to implement performant and reliable execution of distributed software.
In this chapter, we learned about the architecture and usage of Python packages, such as Dask and PySpark, which provide powerful APIs to design and execute programs capable of scaling to hundreds of machines. We also briefly looked at MPI, a library that has been used for decades to distribute work on supercomputers designed for academic research.
Throughout this book, we explored several techniques to improve the performance of ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.