parallel-power

Speed up batch processing by running independent task items in parallel using multiple workers. This restructures requests so each item is handled simultaneously, significantly reducing long serial wait times for folders of files.

The best way to process independent spreadsheet rows is to assign each row to a separate worker running in parallel. This allows multiple rows to be analyzed simultaneously, dramatically cutting down the total execution time compared to sequential processing.

Yes, you can use parallel workers to research multiple companies at once. As long as the research tasks are independent, multiple workers can gather information simultaneously, complete the batch job far faster, and save separate output files for each company.

To merge separate output files after parallel batch jobs, you must include an explicit merge step once all workers complete. This consolidation step combines the individual results from each worker into a single final deliverable or overview file.

You should not use parallel execution for batch tasks if the items are dependent on each other. Parallel workers require tasks to be completely independent, meaning no item's processing should rely on the result or timing of another item.