Name: Data Intelligence 20 of 21 - Testing REST API Operators
Uploaded: 2023-9-6
Duration: 297 s

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<div><div><div>In this video series, we'll introduce some of the applications when using Data Intelligence. In this video, we'll go through using the Modeler to test a pipeline which uses the OpenAPI operator to expose data as an API.<br><br>0:00 - Introduction<br>0:50 - Build a POST request using Postman adding authorization, header, and body<br>3:15 - Send message and view result in wiretap and Modeler<br><br><br>Please note:<br>                     This video was originally published on the SAP HANA Academy YouTube Channel                      and therefore may contain references to SAP HANA Academy and YouTube.                      The tech world is always evolving and the SAP HANA Academy YouTube channel                      is set to close on December 31, 2023.                      At that time, references will no longer be valid                      and will gradually be removed when the videos are updated.<br>Also, this video includes automatically generated captions                      and therefore might misrepresent the spoken content or display incorrect product names.</div></div></div>

Data Intelligence 20 of 21 - Testing REST API Operators

Welcome to the SAP HANA Academy. My name is Bob, and in this series of videos by Digital Partner Engineering and the SAP HANA Academy, we'll be looking at Data Intelligence 3.1. This video goes through testing operators that use REST APIs when using the modeler. In the previous video we looked at creating a pipeline with a web service that can be called externally so that when that message is sent it can be written to a file within our semantic data lake. Now that graph's already been executed and it's running at the moment. And here we can see the status that it's running. And at the moment we've got no information because of course what we need to do is send a message and that's what we're going to cover in this video. So there's different ways you can do this. I'm going to use a tool called Mailman. And in the next video I'll show you how to do this with some Python. So the assumption is you've used Mailman before. I'm going to generate a post request. And then we need to enter in our request URL. So how do we get that request URL? Well, if I go back to my Data Intelligence, we've got a tab open, which is the Wiretaps tab. But you can actually use any of the tabs where you're using Data Intelligence. So what we need to do is get this URL, which is the first part of that URL all the way up to PipelineModularService. So I'll copy this part of the URL including the HTTPS and back in Mailman we'll paste that as the request URL. Next we need to get the base path and we get that from the OpenAPI operator. So if I copy the base path we'll go back to our mailman and we'll paste this at the end of that request URL. For my tests I'll also add a forward slash test at the end. Now even though this process tag isn't used, it's important to add this at the end of your request. Otherwise we'll receive an error. Now in terms of the authorization we need to set that up. So below I'll go to my authorization tab and we're going to use basic authentication. And you'll need the name of your tenant with a backslash and the name of your user and then of course your password. For me this is the same user that I've logged into Data Intelligence with. Now in the headers tab we just need to add a parameter which is x-requested-with with the value xmlhttp-request. If you don't have this parameter, we'll get the error forbidden cross site request. For the body, this is where we're going to add our message. And there are many different formats you can send that message as. I'm going to use simple text. So I'm going to show you what that message looks like. I'll just paste that in. And quite simply, we're sending the four column headers and a single row of data. Again, if we go back to the wiretap, we can see that it's empty. But we can also see that the graph is currently being executed and it's still running. Of course, again, it's going to wait for a message and then terminate. So, what I'll do is I'll send that message. We don't get any response back, but we know it works because our status is 202 accepted. But if I go back to the wiretap, we can see that we've got some form of a response. And what you'll want to do is have a quick scan of that log file just to make sure that you've received no form of errors. Now if we go back to the modeler you'll see that the actual graph has completed executing. However again in the real world if you're sending multiple messages you would want this to be continually running so you wouldn't have that workflow terminator in the graph. And of course the ultimate proof is if I refresh my folder within the Metadata Explorer within that semantic data lake we can see that we've got a new file that's appeared. This was the original, this is the new file, and if we do a data preview, we can see we've got that one row of data. So, to summarize, that was a short couple of videos which showed how to use the OpenAPIServeFlow operator in order to send data to our semantic data lake using a REST API. Now we can also do this with code, so in the next video I'm going to show you how we can do this with some Python as well as demonstrate an app sending code to our files in our semantic data lake. You can find more tutorials on our YouTube channel, and if you'd like to be informed as soon as new video tutorials are published, then please subscribe. Thanks for watching.