Your n8n workflows have moved beyond simple trigger-action sequences. Now, it's time to master the complex routes data can take. The key is understanding n8n's execution logic. It's deceptively simple: nodes execute based on their connections, not their position on the canvas. When a node finishes, it passes its output data to the next node it’s wired to.

But what happens when a node connects to multiple downstream nodes? n8n follows a top-to-bottom, branch-by-branch rule. It will execute the entire branch connected to the topmost output of a node first. Once that branch is complete, it will return and execute the next branch down. This predictable order is crucial for managing workflows where timing and data dependencies matter.

As your logic grows, relying on nested If nodes creates a tangled mess that's hard to debug. For workflows that need to route data down one of many possible paths, the Switch node is a far cleaner solution. Instead of a binary true/false, the Switch node evaluates incoming data against a set of rules and directs it to the corresponding output.

Imagine you're processing customer support tickets. A Switch node can route a ticket based on its category: 'Billing' goes to one branch that alerts the finance team, 'Technical' goes to another that creates a developer task, and 'General Inquiry' goes to a third that sends a standard reply. This keeps your main workflow tidy and logic easy to follow.

Branching is only half the story. Once your data has traveled down separate paths, you often need to bring it back together. This is the job of the Merge node, a powerful tool for synchronizing and combining data from parallel branches. How it works depends entirely on the mode you select.

The Combine mode is perfect for data enrichment. One branch might fetch a customer's basic info, while another retrieves their recent orders. The Merge node can combine these into one complete object. The Wait mode is your go-to for synchronization. It acts as a checkpoint, ensuring that all parallel processes—like writing files or calling multiple APIs—are finished before the workflow proceeds. This prevents race conditions, where a later step accidentally runs before its required data is ready.

Processing lists of items is a common task. The standard approach in n8n is to use the built-in Loop Over Items setting on a node. When enabled, the node will execute once for every single item it receives as input. For a list of 100 users, a node with looping enabled will run 100 times. This is simple and effective for small-to-medium datasets.

But what about a list of 100,000 users? Running a node that many times can overwhelm your n8n instance or hit API rate limits on an external service. For large-scale operations, you need a different strategy: batching.

The [{] node is designed for this. It takes a large list and breaks it into smaller, manageable chunks. You can set the batch size to 100, for example, and it will turn your list of 100,000 items into 1,000 separate runs, each with a batch of 100 items. This dramatically improves performance and reliability when dealing with big data.

Mastering these logic patterns—routing with Switch, synchronizing with Merge, and processing at scale with batching—transforms your workflows from simple automations into robust, efficient systems.