Many of us in the big data world are already familiar with Spark. But newcomers may be wondering: What is Spark? Even if you’re a user, there are a lot of Spark performance tuning tips around the internet. How do you sort the wheat from the chaff?
Spark is an open-source, distributed processing framework designed to run big data workloads at a much faster rate than Hadoop and with minimal resources. Spark leverages in-memory caching and optimized query execution to perform fast queries against data of any size.
In today’s big data world, Spark technology is a core tool. However, it is very complex, and it can present a range of problems if not properly optimized. Without the right approach to Spark performance tuning, you put yourself at risk of many Spark performance issues, including overspending and suboptimal performance.
What is Spark Performance Tuning?
Spark performance tuning is the process of making rapid and timely changes to Spark configurations to ensure all processes and resources are optimized and function smoothly. This Spark optimization process enables users to achieve SLA-level Spark performance while mitigating resource bottlenecks and preventing performance issues.