Merge Iterative Processing Results
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Efficiently process extensive lists of data in manageable batches and then seamlessly merge all the individual results into a single, comprehensive output. This workflow is ideal for handling large datasets without overwhelming system resources, ensuring reliable data consolidation.
About This Workflow
When dealing with large volumes of data, direct processing can lead to performance bottlenecks or memory issues. This powerful n8n workflow offers a robust solution by intelligently splitting your data into configurable batches. It iterates through these batches, allowing for controlled processing and optional delays, ensuring stability and resource management. Crucially, once all batches are processed, the workflow automatically aggregates and merges all individual batch results into a single, unified dataset. This provides a complete overview, making it perfect for complex reporting, data migrations, or any scenario requiring comprehensive data consolidation after iterative operations.
Key Features
- Batch Processing for Large Datasets: Efficiently handle extensive lists of items by breaking them into smaller, manageable batches, preventing system overload.
- Iterative Execution Control: Automatically loop through batches with smart conditional checks to manage the entire processing cycle from start to finish.
- Seamless Data Consolidation: Collect and merge results from every single batch iteration into one complete output, providing a unified view of your processed data.
- Configurable Delays: Integrate optional wait times between batch processes, perfect for respecting API rate limits or synchronizing with external systems.
- Flexible Data Source Integration: Easily adapt to pull data from any CRM, database, or API by replacing the example 'Customer Datastore' node.
How To Use
- Define Your Data Source: Replace the "Customer Datastore" node with your specific data source (e.g., a "Read from Database" node, a "CRM" node set to
getAll, or an "HTTP Request" node for an API). - Configure Batch Size: Adjust the "Loop Over Items" node to define how many items are processed in each batch, balancing performance and resource usage.
- Implement Your Processing Logic: Insert the nodes for the actual operations you want to perform on each batch between "Loop Over Items" and "Wait". Ensure the final data you wish to merge passes through the "NoOp" node.
- Set Delays (Optional): Modify the "Wait" node to introduce pauses between batches, useful for rate limiting or allowing external systems to catch up.
- Review Merged Output: The "Merge loop items" node automatically collects and concatenates all data that flows through the "NoOp" node during each loop iteration, providing a comprehensive final result.
Apps Used
Workflow JSON
{
"id": "7f9e11d7-9148-452c-8a85-c87178ec0c2b",
"name": "Merge Iterative Processing Results",
"nodes": 23,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 7f9e11d7-9148...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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