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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
Languages
Concepts
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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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The preceding code block creates a Spark DataFrame and displays the first 1,000 records. Now, let's run some code with the collect() function."

A block of code is set as follows:

airlines_1987_to_2008.select('Year').distinct().collect()

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Click on Spark Jobs under this command and select View next to the second job. This opens up the job page in Spark UI."

Tips or Important Notes

Executor slots are also called cores or threads.

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