manually, so this is going to save you a ton of time in either building or testing workflows. Thanks for listening to the third video of the NN Advanced course, where we covered pinning data and editing outputs. In the next video, we'll cover subw workflows and when to use them.

hey and welcome to video number three of the advanced course for NN. In this video, we'll be covering pinning data, what it is, why it's useful, as well as the edit output data feature. Um, both of these features will come in very handy when building your workflows, so let's talk a little bit about pinning data. Um, to speed up workflow building, you want to rely as little as possible on external services to reduce execution time as much as possible. Every time you're re-executing something, you're wasting time. In comes in the pinning data feature. You can pin the output of a node to make it readily accessible. You can use this to avoid triggering external systems again, like making API calls, or to speed up execution while building. You can pin data by clicking on the little pin icon that you can see to the top right of the output. For example, if a part of a workflow takes a few minutes to execute, you can just pin the final node and continue building your workflow from there. Pinned data is only used for text.

edge cases, like making new API calls or using a code or um edit Fields node using the same example. I can edit the output web hook uh node to test different sets of workflow trigger items again without having to re-trigger the web hook every single time. You can also paste in uh data from a previous execution with the uh edit output feature. You can do this by going into a previous execution, navigating to the output pane of the node in question, switching to Json View, and then clicking the copy button um. From here, you can go back into your workflow that you're working on, edit the output, paste in the values, and you will have um that data ready to work with in the workflow that you're building um. This is very useful when debugging a workflow that might have failed and now you need to go in and well make a fix. You can save a lot of time testing workflows by using the edit output node paired with a mock data tool. A mock data tool that I like to use is Mockaroo. This will help you generate

hook node copy the output from the JSON view, return back to the editor, unpin the data, paste in the data from the execution that had an error, and save it. And now I can keep building the workflow from that state, figure out why the error was generated, and implement a fix for next time. Last part of editing and pinning data is to show how we can use, for example, mock data. So here I'm going to start building a workflow, and I need some different information, you know, 10 examples of contacts. So I'm going to use a tool called Mockaroo and ask it to generate an ID, first name, email, gender, IP address. Let's say that 10% of the time the email is empty. And here I make sure to include the JSON format. So when I click generate data, sorry, when I click preview, I have some data that I can then copy-paste back into Anyen. And now I have this pinned data that I can start working with without having to ask ChatGPT or generate all of this.

test executions, not production ones, where the node is always executed. You can see if a node has pinned data by the blue icon on the canvas. You can keep one single set of pin data, and when the workflow is activated, it will just execute as normal. For example, here, the pin data from the web hook node, so we don't have to trigger it again when building the workflow, which would require going into a tool, testing the workflow, sending the web hook, and then building. Here, we can just start building straight away. Another trick that we can use to facilitate workflow building is to use the edit output feature. As its name suggests, this feature lets you manually edit the output of a given node. You can see this feature. You can use this feature along with the pinned data feature to tweak execution data to see how it performs under specific edge cases, for example, by setting a field to null or passing a string instead of a number. This can save you a ton of time when working with external systems, avoiding you having to recreate these.

sets of randomized information, and you can even, uh, set options for blank data, allowing you to generate a realistic set of mock data items. Just be sure to select the JSON format so you can copy-paste it directly into n. Here, I have a relatively simple workflow that listens to a web hook, edits some fields, sends a message on Slack, and then, if the ID is known, updates it, uh, in a Google Sheets. A pener updates. So here, when I'm testing the workflow, I can send myself a test event, and to avoid myself having to send myself this test event multiple times, the first thing I can do is simply pin the data. So here, we can see we have the blue check mark, which means the data is pinned, and if I refresh the page, I can see that the information is still available here, and I can still, uh, run the workflow as if I had the output item here. Now, if I go into my past executions, I have one here that returned an error. So what I can do is come into to the web

n8n Advanced Course (3/8) - Pinning and editing output data

n8n Advanced Course (3/8) - Pinning and editing output data