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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Understanding the Parquet format


the Google Dremel paper. In Parquet, data in a single column is stored contiguously.

Apache Parquet is a columnar data storage format, specifically designed for big data storage and processing. It is based on record shredding and the assembly algorithm from the Google Dremel paper. In Parquet, data in a single column is stored contiguously. The columnar format gives Parquet some unique benefits. For example, if you have a table with 100 columns and you mostly access 10 columns in a row-based format, you will have to load all the 100 columns, as the granularity level is at the row level. But, in Parquet, you will only need to load 10 columns. Another benefit is that since all of the data in a given column is of the same datatype (by definition), compression is much more efficient.

While we are discussing Parquet and its merits, it's a good idea to discuss the economic reason behind Parquet's success. There are two factors that every practitioner would like to...

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