Pepperdata Blog Myth #3 of Apache Spark Optimization: Instance Rightsizing

In this blog series we are examining the Five Myths of Apache Spark Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Spark users: Choosing the right instances will eliminate waste in a cluster. The Value of Instance Rightsizing Selecting appropriate cloud instance types for workload demands is an essential step in any cloud computing effort. Some applications are more CPU intensive, while others might be more memory intensive. In either case, Instance Rightsizing—aligning the CPU and memory requirements of an application with an appropriate instance type—ensures that an application can run more efficiently, which can lead to significant cost savings and performance improvements. Choosing optimal instance types from among the 600+ offered on AWS can be a daunting challenge, especially as workloads change. Many cloud practitioners rely on the assistance of cloud-providers or third-party instance recommendation tools to help with this effort. Karpenter in particular addresses many challenges around Instance Rightsizing in Kubernetes environments. It is worth noting that an overall Instance Rightsizing effort might include additional financial optimizations such as: Reserved Instances and Savings Plans are […] The post Myth #3 of Apache Spark Optimization: Instance Rightsizing appeared first on Pepperdata.