What is the best practice when experiencing delays during peak hours in streaming jobs?

Optimizing the job for smaller batch sizes is a critical best practice when dealing with delays during peak hours in streaming jobs. By reducing the size of the batches being processed in each trigger, the system can handle data more efficiently, allowing for lower latency and quicker insights. Smaller batches tend to complete faster, which may help alleviate congestion that can occur during peak loads since it allows for a more frequent and continuous flow of data processing.

When processing larger batches, the system might take longer to execute, leading to backlogs and increased delays, especially when the incoming rate of data is high. By keeping batches smaller, the streaming job can react more swiftly to incoming data while maintaining system responsiveness.

In situations where delays occur, choosing to optimize batch sizes is a strategic way to ensure that the streaming application can remain agile and maintain a level of performance that meets business needs, especially during high-traffic times.