What method improves processing times by allowing idle executors to start on new batches during longer jobs?

The method that improves processing times by allowing idle executors to start on new batches during longer jobs is related to trigger interval adjustment. This technique enhances the efficiency of a streaming workload by enabling the system to manage the time between successive micro-batch processing. By adjusting the trigger interval, you can control how often new data is fetched and processed, which can lead to more optimal resource utilization.

When the system is set to a shorter trigger interval, it can process smaller segments of data more frequently. This means that if an executor is free or has completed its current batch and there are new batches available to process, it can immediately start on those new batches instead of remaining idle. Therefore, trigger interval adjustment directly influences how quickly new data is processed while allowing for more agile resource allocation.

Conversely, batch interval adjustment and dynamic partition sizing address processing overhead from different angles. Batch interval affects the size and timing of the data processed in each batch, while dynamic partition sizing refers to optimizing the partitioning of data to balance loads. Data skew handling is aimed at dealing with imbalances in data distribution across partitions. While these methods can contribute to performance improvements, they do not specifically focus on allowing idle executors to pick up new batches during processing.