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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

Preface 1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Cache-friendly layout of data in memory

In order to understand how data structures can be laid out in a cache-friendly manner, let's have a brief look at how caches work in general. Memory on a modern computer system is addressed using 64 bit addresses pointing to 64 bit memory blocks. Remember, Tungsten tries to always use 8-byte Datasets which perfectly fit into these 64-bit memory blocks.

So between your CPU cores and main memory, there is a hierarchical list of L1, L2, and L3 caches-with increasing size. Usually, L3 is shared among all the cores. If your CPU core requests a certain main memory address to be loaded into the CPU core's register (a register is a memory area in your CPU core) - this happens by an explicit machine code (assembler) instruction - then first the L1-3 cache hierarchy is checked to see if it contains the requested memory address.

We call data...

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