What can contribute to slower performance on join or shuffle jobs?

Data skew is a significant factor that can contribute to slower performance on join or shuffle jobs. When data is skewed, it means that one or more partitions of data are disproportionately larger than others. In a distributed computing environment, this imbalance can lead to one or more nodes being overloaded while others remain underutilized. As a result, the overall execution time increases because the jobs must wait for the longest-running node to complete.

In contrast, autoscaling is often designed to optimize resource allocation based on workload demands, which can help improve performance rather than hinder it. Using large partition sizes may lead to increased memory consumption, but it does not directly relate to the performance slowdown seen with data skew. Data caching typically enhances performance by storing frequently accessed data in memory, allowing faster retrieval on subsequent requests.

Therefore, data skew emerges as the primary concern in this context as it can severely disrupt the balance of workload distribution across nodes, leading to inefficiencies during join or shuffle operations.