Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

The best place to diagnose a performance problem related to not leveraging predicate push-down is in the Query Detail screen by interpreting the Physical Plan. Predicate push-down is an optimization technique where filters are applied as early as possible in the data processing pipeline, reducing the amount of data read from disk and transmitted over the network.

When you look at the Physical Plan in the Query Detail screen, you can observe how Spark handles the filtering of data at different stages of the query execution. If predicate push-down is not being utilized, you will likely see that filtering happens after data has been read from the underlying storage, suggesting that more data is being scanned than necessary. This insight allows for the identification of specific areas where performance could be improved, such as restructuring the query to ensure that filters are applied correctly.

Other options, while they offer insights into different aspects of Spark's operation, do not directly address the issue of predicate push-down. For example, logs can provide some information but do not explicitly indicate whether predicate push-down is being applied. Analyzing data sizes from the Stage's Detail screen might suggest inefficiencies but lacks the explicit details of how filtering is managed. Observing which RDDs are stored on disk does not correlate with the mechanism of predicate push