Book Image

Optimizing Databricks Workloads

By : Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi
Book Image

Optimizing Databricks Workloads

By: Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi

Overview of this book

Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud. In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains. By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently.
Table of Contents (13 chapters)
1
Section 1: Introduction to Azure Databricks
5
Section 2: Optimization Techniques
10
Section 3: Real-World Scenarios

Understanding the use of inferSchema

The inferSchema option is very often used to make Spark infer the data types automatically. While this approach works well for smaller datasets, performance bottlenecks can develop as the size of the data being scanned increases. In order to better understand the challenges that come with using this option for big data, we will perform a couple of experiments.

Experiment 1

In this experiment, we will re-run the code block that we ran in the previous section:

airlines_1987_to_2008 = (
  spark
  .read
  .option("header",True)
  .option("delimiter",",")
  .option("inferSchema",True)
  .csv("dbfs:/databricks-datasets/asa/airlines/*")
)
display(airlines_1987_to_2008)

The code block simply reads CSV files and creates a Spark DataFrame by automatically inferring the schema. Note the time it takes for the job to run. For us, it took...