Right-sizing compute resources
One of the critical factors that affects the performance and cost-effectiveness of Apache Spark applications is the size and type of compute resources used. Right-sizing your Spark cluster can result in significant improvements in processing speed and cost efficiency.
This section dives deep into the concept of right-sizing compute resources for Apache Spark and provides guidelines to achieve the best balance between performance and cost.
Understanding the basics
Before diving into right-sizing, it’s essential to understand the fundamental components that are part of a Spark cluster:
- Executor: The JVM process is initiated on a worker node and is responsible for executing tasks and storing data in memory or disk storage. Each task runs on a single executor.
- Memory: This shows how much RAM is available on each node.
- Core: This is a computational unit available to the executor. Memory on each node is generally split between...
