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Optimizing Databricks Workloads

You're reading from   Optimizing Databricks Workloads Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801819077
Length 230 pages
Edition 1st Edition
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Authors (3):
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Anshul Bhatnagar Anshul Bhatnagar
Author Profile Icon Anshul Bhatnagar
Anshul Bhatnagar
Sarthak Sarbahi Sarthak Sarbahi
Author Profile Icon Sarthak Sarbahi
Sarthak Sarbahi
Anirudh Kala Anirudh Kala
Author Profile Icon Anirudh Kala
Anirudh Kala
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Toc

Table of Contents (13) Chapters Close

Preface 1. Section 1: Introduction to Azure Databricks
2. Chapter 1: Discovering Databricks FREE CHAPTER 3. Chapter 2: Batch and Real-Time Processing in Databricks 4. Chapter 3: Learning about Machine Learning and Graph Processing in Databricks 5. Section 2: Optimization Techniques
6. Chapter 4: Managing Spark Clusters 7. Chapter 5: Big Data Analytics 8. Chapter 6: Databricks Delta Lake 9. Chapter 7: Spark Core 10. Section 3: Real-World Scenarios
11. Chapter 8: Case Studies 12. Other Books You May Enjoy

Working with the OPTIMIZE and ZORDER commands

Delta lake on Databricks lets you speed up queries by changing the layout of the data stored in the cloud storage. The algorithms that support this functionality are as follows:

  • Bin-packing: This uses the OPTIMIZE command and helps coalesce small files into larger ones.
  • Z-Ordering: This uses the ZORDER command and helps collocate data in the same set of files. This co-locality helps reduce the amount of data that's read by Spark while processing.

Let's learn more about these two layout algorithms with a worked-out example:

  1. Run the following code block:
    from pyspark.sql.types import *
    from pyspark.sql.functions import *
    manual_schema = StructType([
      StructField('Year',IntegerType(),True),
      StructField('Month',IntegerType(),True),
      StructField('DayofMonth',IntegerType(),True),
      StructField('DayOfWeek',IntegerType(),True...
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