Pepperdata Blog Myth #5 of Apache Spark Optimization: Spark Dynamic Allocation

In this blog series we’re examining the Five Myths of Apache Spark Optimization. The fifth and final myth in this series relates to another common assumption of many Spark users: Spark Dynamic Allocation automatically prevents Spark from wasting resources. The Value of Spark Dynamic Allocation Spark Dynamic Allocation is a useful feature that was developed through the Spark community’s focus on continuous innovation and improvement. This feature optimizes the resource utilization of Spark applications by dynamically adding and removing executors based on workload requirements. It attempts to fully utilize the available task slots per executor, eliminating the need for developers to rightsize the number of executors before applications start running. Because of these benefits, Spark Dynamic Allocation is considered a no brainer. If the application architecture can handle it, then most developers will enable Spark Dynamic Allocation. But an important question to ask is: What can Spark Dynamic Allocation not do?   What Spark Dynamic Allocation Cannot Do Tasks Cannot Use Their Full Allocation at All Times If a certain number of tasks is capable of running inside an executor, then ideally that number of tasks should be running. But for most applications, this number is not constant, because most […] The post Myth #5 of Apache Spark Optimization: Spark Dynamic Allocation appeared first on Pepperdata.