YoVDO

Optimizing Spark SQL Jobs with Parallel and Asynchronous IO

Offered By: Databricks via YouTube

Tags

Apache Spark Courses Big Data Courses Data Processing Courses Performance Tuning Courses Cluster Computing Courses Parquet Courses

Course Description

Overview

Discover optimization techniques for Spark SQL jobs in this 21-minute Databricks conference talk. Learn how to improve performance in large-scale big data clusters using parallel and asynchronous I/O operations. Explore file-level and row group-level parallel read implementations, asynchronous spill optimization, and the innovative parquet column family design. Gain insights into how these techniques can accelerate Apache Spark jobs, potentially improving end-to-end performance by 5% to 30%. Delve into the implementation details of these features and understand their impact on job acceleration in EB-level data platforms.

Syllabus

Introduction
Why Does IO Matter
Parquet
Spiral Circles
Sequential vs Parallel IO
Group Level Parallel IO
Column Family Parallel IO
Asynchronous Sphere


Taught by

Databricks

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