Based on a tutorial by n8n Official
Are you struggling to understand how data flows through multiple branches in your n8n workflows? You're not alone! Many automation builders get confused when their workflow branches don't execute in the expected order.
In this comprehensive guide, I'll break down the first video of the n8n Advanced Course, explaining exactly how data flows through workflows, the execution order of nodes, and how to effectively split and merge data between branches.
Quick Navigation
Node Execution Order in n8n
If you've created multiple branches in your workflows, understanding the execution order is crucial for predictable results. In n8n version 1.0 and above, there's a very specific pattern to how nodes execute.
Key Points:
- Branches execute in their entirety, one after another
- Branch execution order: top to bottom, then left to right
- When branches are at the same height, the leftmost branch executes first
- While execution order can be changed, it's not recommended
My Take:
This top-to-bottom, left-to-right execution pattern is similar to how we read in Western languages, making it intuitive to visualize. Always arrange your branches in the canvas with this order in mind to avoid unexpected behavior.
Splitting Data into Multiple Branches
There are several ways to split your workflow data into different paths, each with specific use cases. The tutorial covers three main methods.
Key Points:
- If Node: Splits data into two branches (true/false) based on a condition
- Switch Node: Creates multiple branches (n+1, including fallback) based on conditions
- Multiple output connections: Sends all items down each branch
My Take:
The Switch node is particularly powerful when you need to route items based on multiple conditions. It's like having multiple If nodes combined into one, making your workflow cleaner and easier to manage.
Merging Data from Different Branches
Once data is split into different branches, you'll often need to bring it back together. The Merge node is the key to combining data from separate paths.
Key Points:
- The Merge node has two inputs and waits for data from both branches
- Append: Simply combines all items from both inputs
- Choose: Selects items from either branch or neither
- Combine: Merges inputs by field position or creates all combinations
- Similar to SQL join operations (inner join, outer join, left/right join)
My Take:
Think of the Merge node as your data orchestrator. The "Combine" option is particularly useful for data enrichment workflows, where you want to add information from one dataset to another based on matching fields.
Loop Over Items Node
The Loop Over Items node (formerly Split in Batches) helps when you need to process items individually or in small groups, which is essential for certain operations.
Key Points:
- Splits input items into batches of a specified size
- Useful when nodes return multiple items from a single input
- Helps manage API rate limits by spreading out calls
- Each loop iteration is marked with a green number
- Output panel shows items for each iteration
My Take:
The Loop Over Items node is your best friend when dealing with APIs that limit the number of calls you can make. By batching your requests and using the Wait node between batches, you can prevent hitting rate limits that would cause your workflow to fail.
Practical Examples
The tutorial demonstrates two practical examples of using branches and the Merge node in real-world scenarios.
Key Points:
- Example 1: Using If node to split contacts into personal and professional emails, adding email type tags, then merging with Append
- Example 2: Enriching professional contacts with company data using the Combine merge option
- Demonstration of joining data by matching fields (domain from email with website)
- Shows how to keep all contacts while adding company information where available
// Example code shown in the tutorial for extracting domain from email
// Split the email at @ and take the second part
return email.split('@')[1];
My Take:
The data enrichment example showcases one of n8n's most powerful features: the ability to pull data from different sources and intelligently combine them. This pattern can be adapted for countless use cases from CRM enrichment to cross-referencing analytics data.
Comments
Post a Comment