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Mastering Apache Spark 2.x - Second Edition

You're reading from  Mastering Apache Spark 2.x - Second Edition

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Pages 354 pages
Edition 2nd Edition
Languages

Table of Contents (21) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. A First Taste and What’s New in Apache Spark V2 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 associated with such an address a memory page. If this is the case, then main memory access is omitted and the page is directly...

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