Batch Processing and Conditional Judgment Example Workflow
This workflow automatically generates 10 data entries after being manually triggered and processes them one by one. During the processing, flexible flow control is achieved through conditional judgments. When processing the 6th data entry, a specific operation is triggered, and the loop ends. This design effectively addresses the need for executing tasks on batch data one by one while allowing for immediate interruption of subsequent operations when specific conditions are met, thereby improving processing efficiency and intelligence. It is suitable for scenarios such as data cleaning and approval processes.
Workflow Name
Batch Processing and Conditional Judgment Example Workflow
Key Features and Highlights
This workflow is triggered manually by the user to automatically generate a dataset of 10 items, which are then split into individual batches for sequential processing. Utilizing a conditional judgment node, the workflow precisely controls the processing flow: when the 6th item (index 5) is reached, a specific action is triggered and the loop ends. This demonstrates the flexible application of batch-wise data processing combined with conditional branching.
Core Problem Addressed
It solves the challenge of executing tasks on batch data item-by-item while allowing immediate interruption of subsequent operations once a specific condition is met. This prevents unnecessary or redundant processing, thereby enhancing the efficiency and intelligence of automated workflows.
Use Cases
- Scenarios requiring item-by-item processing of large datasets with conditional stopping, such as data cleansing, approval workflows, and stepwise task execution;
- Testing and demonstration of complex workflow control by combining batch splitting with conditional logic;
- A reference example for automation script developers to learn batch data processing and branching logic.
Main Workflow Steps
- Manual Trigger: The user initiates the workflow by clicking the execute button.
- Data Generation: A function node generates 10 simulated data entries.
- Batch Splitting: The generated data is split into single-item batches for sequential processing.
- Conditional Judgment: The current batch index is checked to determine if it equals 5.
- Flow Branching:
- If processing the 6th item, set the message to “Loop Ended” to indicate the loop termination.
- Otherwise, continue processing the next item by looping back to the batch splitting node.
Involved Systems or Services
Only native n8n nodes are used, including Manual Trigger, Function, SplitInBatches, IF (conditional), and Set nodes. No external systems are involved.
Target Audience and Value
This workflow is ideal for beginners and developers in automation workflow design to grasp the fundamental concepts of batch processing and conditional control. It provides an intuitive example that helps users understand how to implement dynamic data generation, batch splitting, and condition-based branching using n8n, making it highly valuable for educational and demonstration purposes.