In the world of big data, Apache Spark is loved for its ability to process massive volumes of data extremely quickly. Being the number one big data processing engine in the world, learning to use this tool is a cornerstone in the skillset of any big data professional. And an important step in that path is understanding Spark’s memory management system and the challenges of “disk spill”.
Disk spill is what happens when Spark can no longer fit its data in memory, and needs to store it on disk. One of Spark’s major advantages is its in-memory processing capabilities, which is much faster than using disk drives. So, build applications that spill to disk somewhat defeats the purpose of Spark.
Disk spill has a number of undesirable consequences, so learning how to deal with it is an important skill for a Spark developer. And that’s what this article aims to help with. We’ll delve into what disk spill is, why it happens, what its consequences are, and how to fix it. Using Spark’s built-in UI, we’ll learn how to identify signs of disk spill and understand its metrics. Finally, we’ll explore some actionable strategies for mitigating disk spill, such as effective data partitioning, appropriate caching, and dynamic cluster resizing.
Before diving into disk spill, it’s useful to understand how memory management works in Spark, as this plays a crucial role in how disk spill occurs and how it is managed.
Spark is designed as an in-memory data processing engine, which means it primarily uses RAM to store and manipulate data rather than relying on disk storage. This in-memory computing capability is one of the key features that makes Spark fast and efficient.
Spark has a limited amount of memory allocated for its operations, and this memory is divided into different sections, which make up what is known as Unified Memory:
