Apache Spark Memory Management

Welcome back to our comprehensive series on Apache Spark Performance Tuning/Optimisation! In this video, we dive deep into the intricacies of Spark's internal memory allocation and how it divides memory resources for optimal performance.

🔹 What you'll learn:
1. On-Heap Memory: Learn about the parts of memory where Spark stores data for computation (shuffling, joins, sorting, aggregation) and caching directly within the Java heap.
2. Off-Heap Memory: Discover how Spark utilises memory outside the Java heap to manage data storage in crucial situations.
3. Overhead: Understand the additional memory overhead required for managing Spark internals and how it impacts your applications.
4. Unified Memory: Explore the concept of unified memory management in Spark, the movable slider between execution and storage memory and the rules that define this movement.
5. Memory Calculation: How spark calculates and allocates memory to different storages.

📘 Resources:
📄 Complete Code on GitHub: https://github.com/afaqueahmad7117/sp...
🎥 Full Spark Performance Tuning Playlist:    • Apache Spark Performance Tuning  
🔗 LinkedIn:   / afaque-ahmad-5a5847129  

📘 Chapters:
0:00 Intro
0:45 Roadmap
1:12 Executor Memory Layout
4:33 Executor Memory Calculations
11:52 Unified Memory
19:04 Off Heap Memory
22:30 Summary

#ApacheSparkTutorial #SparkPerformanceTuning #ApacheSparkPython #LearnApacheSpark #SparkInterviewQuestions #ApacheSparkCourse #PerformanceTuningInPySpark #ApacheSparkPerformanceOptimization #pyspark #databricks