Clusters will not be fully utilized unless you set the level of parallelism for each operation high enough. In this blog post, the author provides tips and optimization methods that help achieve high efficiency using Apache Spark. The blog post covers the factors affecting partitioning, such as business logic, data, and environment. The author recommends reducing the working dataset size to speed up the Spark application, which can be achieved by filtering the source data or using built-in data formatting mechanisms. The author also recommends repartitioning before multiple joins to improve performance. Additionally, the author provides useful recommendations for setting the number of partitions to achieve optimal performance. Overall, the blog post is a comprehensive guide to maximize the efficiency of Apache Spark for Python applications.
