Purpose: To iterate over a collection of items and execute a set of activities for each item in parallel, potentially improving performance for processing large collections.
Functionality: "Parallel For Each" is similar to "For Each" but designed for parallel execution. It:
Iterates over a Collection: Processes a data collection, just like "For Each".
Executes in Parallel: Attempts to execute the loop body activities for multiple items in the collection concurrently or in parallel, leveraging multi-threading or parallel processing capabilities of the workflow engine.
Potential Performance Gains: Can significantly reduce the overall processing time for large collections, especially when the loop body activities are independent and can be executed concurrently.
Considerations: Parallel processing might introduce complexities related to concurrency, shared resources, and potential race conditions. It's important to ensure that the loop body activities are thread-safe or properly handle concurrent execution if using "Parallel For Each".
Use Cases:
Performance-Critical Batch Processing: When processing large datasets and minimizing processing time is crucial (e.g., bulk data transformations, parallel API calls for multiple items, large-scale data analysis).
Independent Item Processing: Scenarios where the processing of each item in the collection is independent of others and can be safely executed in parallel.
Optimizing Workflow Throughput: Improving the overall throughput of workflows that involve processing large collections by utilizing parallel execution.
