Apache Spark Executor Tuning | Executor Cores & Memory

Welcome back to our comprehensive series on Apache Spark Performance Tuning & Optimisation! In this guide, we dive deep into the art of executor tuning in Apache Spark to ensure your data engineering tasks run efficiently. 🔹 What is inside: Learn how to properly allocate CPU and memory resources to your Spark executors and the number of executors to create to achieve optimal performance. Whether you're new to Apache Spark or an experienced data engineer looking to refine your Spark jobs, this video provides valuable insights into configuring the number of executors, memory, and cores for peak performance. I’ve covered everything from understanding the basic structure of Spark executors within a cluster, to advanced strategies for sizing executors optimally, including detailed examples and calculations. 📘 Resources: 📄 Complete Code on GitHub: https://github.com/afaqueahmad7117/spark-experiments 🎥 Full Spark Performance Tuning Playlist: https://www.youtube.com/playlist?list=PLWAuYt0wgRcLCtWzUxNg4BjnYlCZNEVth 🔗 LinkedIn: https://www.linkedin.com/in/afaque-ahmad-5a5847129/ Chapters: 0:00 - Introduction to Executor Tuning in Apache Spark 0:37 - Understanding Executors in a Spark Cluster 3:30 - Example: Sizing Executors in a Cluster 4:58 - Example: Sizing a Fat Executor 9:34 - Example: Sizing a Thin Executor 12:50 - Advantages and Disadvantages of Fat Executor 18:25 - Advantages and Disadvantages of Thin Executor 22:12 - Rules for sizing an Optimal Executor 26:30 - Example 1: Sizing an Optimal Executor 38:15 - Example 2: Sizing an Optimal Executor 43:50 - Key Takeaways #ApacheSparkTutorial #SparkPerformanceTuning #ApacheSparkPython #LearnApacheSpark #SparkInterviewQuestions #ApacheSparkCourse #PerformanceTuningInPySpark #ApacheSparkPerformanceOptimization #ApacheSpark #DataEngineering #SparkTuning #PythonSpark #ExecutorTuning #SparkOptimization #DataProcessing #pyspark #databricks #dataengineering #interviewquestions #azuredataengineer