Data-based optimizations in Apache Spark
In addition to Spark’s inner optimizations, there are certain things we can take care of in terms of implementation to make Spark more efficient. These are user-controlled optimizations. If we are aware of these challenges and how to handle them in real-world data applications, we can utilize Spark’s distributed architecture to its fullest.
We’ll start by looking at a very common occurrence in distributed frameworks called the small file problem.
Addressing the small file problem in Apache Spark
The small file problem poses a significant challenge in distributed computing frameworks such as Apache Spark as it impacts performance and efficiency. It arises when data is stored in numerous small files rather than consolidated in larger files, leading to increased overhead and suboptimal resource utilization. In this section, we’ll delve into the implications of the small file problem in Spark and explore effective...
