Batch Processing

Batch processing executes many similar tasks together to reduce overhead, increase throughput, and lower resource usage. Use it for bulk validation, mass transformation, and synchronized evaluation across large sets of homogeneous, independent items.

Parallel processing runs independent tasks concurrently to cut total runtime. You can batch-process a list of items to validate and transform them in a single run, amortizing setup costs and saving execution time.

Batch processing supports flexible execution patterns including fail_fast, best_effort, and transactional modes. These patterns provide robust error handling and reliability for bulk validation and synchronized evaluation across multiple items.

Yes, batch concept execution processes several concepts in a single pass to amortize setup costs. Loading shared context once enables efficient mass transformation and synchronized evaluation across large sets of homogeneous input tasks.

Robust error handling during synchronized batch evaluation is managed through flexible execution patterns: fail_fast to stop on errors, best_effort to continue processing, or transactional modes to ensure all-or-nothing reliability across the batch.

Sequential execution causes high latency because it processes tasks one by one, repeatedly loading shared context. Batch processing reduces this overhead by grouping homogeneous tasks together, enabling parallel execution and higher throughput.