Using Google Cloud Functions to get around Google Ads Scripts limitations

Cloudy lake

My first real venture into the more technical side of online marketing was when I learned some JavaScript to write my own Google Ads (Adwords) Scripts. With Google Ads Scripts you can automate stuff within your Google Ads account. You can automate to the extent that it exceeds the standard functionality of the regular interface. So if you want to go beyond the regular options and buttons that any SEA specialist can use, Google Ads Scripts is the way to go.

But you probably already know this if you’re reading this article. You’re probably here because you found out that Ads Scripts also have limitations. This article explores the possibility of using Google Cloud Functions together with Google Ads Scripts as a workaround for these limitations. (Spoiler: it’s possible!)

I’ll show you a real-life example of something quite hard to do directly within Ads Scripts: fuzzy string matching on search terms. This is useful for classifying search terms that have spelling errors for example. We’ll get to that further down this article.

The limitations of Google Ads Scripts

Like I said, Google Ads Scripts are powerful, but they also have limitations. These limitations include:

  • Maximum execution time of 30 minutes. This sounds like a lot, but it’s a real limitation. Mainly because:
    • You can only use synchronous JavaScript from the older JavaScript 1.6 standard, plus a few features from 1.7 and 1.8.
    • Your script runs on a server somewhere that is probably not solely dedicated to the fastest possible execution of your script. Execution is quite slow.
    • No external libraries like lodash or async. You can only use the methods that Google provided, so if you need something else you’re out of luck.

All in all, it’s easy to find yourself stuck in a situation where you’re looping over a loop inside another loop with runtimes that easily exceed 30 minutes. What if we’d move some of the heavy work outside Ads Scripts to another tool that does not have these limitations? That’s where Google Cloud functions come in!

Google Cloud Functions to the rescue

Cloud Functions is Google Cloud’s event-driven serverless compute platform. This is a complicated way of saying that you can execute functions written in Node.js, Python or Go in the cloud. It’s not a free tool, but there is a free tear that lets you make up to two million invocations per month without charge. This plenty for our use case. I won’t go into detail here about how to set up a new Cloud Function. Check out one of these blogs to learn about setting up Cloud Functions: herehere or here.

With Cloud Functions, we can run our code on faster machines. We can also import external libraries from NPM for Node.js or pip for Python. And last but not least, we can run asynchronous code. The only limitation here is a maximum execution time of 9 minutes. But if this is a problem you could divide the workload over multiple Cloud Functions that run in parallel.

Google provides several ways to trigger a Cloud Function. For our use case, we need to create a Cloud Function that uses an HTTP trigger. This means that it can be triggered by either a GET request or a POST request. We can make these requests from our Google Ads Script as our gateway to pass information between Google Ads and Google Cloud.

How to make a POST request from Ads Scripts to a Cloud Function

Making a POST request from a Google Ads Script is fairly straightforward. We can use UrlFetchApp.fetch() to make a request. We can add an options object that contains JSON payload. Use this to send data from our Google Ads account to the Cloud Function.

This specific example populates a JSON payload with two key-value pairs: ‘example_content‘ and ‘another_example_content‘. The next section shows how to access this data in the Cloud Function.

The Cloud Function code

When you create a new Cloud Function, you can choose between a Node.js, Python or Go runtime. Google provides you with a code example for the runtime of your choice. I’ll give you an example that works with the Google Ads Script code example above for both Node.js and Python. I’m not familiar with Go, so I’ll skip that programming language if you don’t mind. 🙂

One thing worth mentioning is that the Cloud Function should always return a string. So keep this in mind when you’re writing your code.


With Python, you can get to the ‘example_content’ from Google Ads through request.get_json().


With Node.js, you can access the ‘example_content’ from Google Ads through the express.js request with req.body.

Example use case: fuzzy string matching

Let’s see if we can use the ideas above in practice with an example. Let’s say you want to classify incoming search terms based on whether they contain a word from a list of words. This list of words could, for example, be a list of brand names. In JavaScript this could be accomplished like this:

However, this only works for exact matches. If the search term contains a spelling error it doesn’t work. We need fuzzy string matching for this. 

We could do this with JavaScript, as explained in this article, but there is an extra problem. We would use this code to compare two strings, such as “facebook” and “facebok”. But we want to find a string that might be part of a longer search query, such as “sign up for facebok online”. This requires us to compare the word we’re looking for to every part of this search query. These parts are called N-grams

A search query with 5 words consists of 5+4+3+2+1 = 15 N-grams. Our example search query consists of these N-grams: “sign”, “up”, “for”, “facebok”, “online”, “sign up”, “up for”, “for facebok”, “facebok online”, “sign up for”, “up for facebok”, “for facebok online”, “sign up for facebok”, “up for facebok online”, and finally “sign up for facebok online”.

Now you probably see why this could lead to problems with the maximum execution time. 😉 We would end up looping over every N-gram inside a loop over every search term. And if we’d be looking for multiple words we’d get a nested loop that is three layers deep. That’s not efficient at all! Can we find a more efficient way?

Requesting a Cloud Function from Google Ads

The code snippet below looks a lot like one of the examples above. Instead of example content, we filled the JSON object with some search terms and a list of brands. In the real world, these arrays could contain hundreds or even thousands of items.

A Cloud Function with FuzzyWuzzy

The Python package FuzzyWuzzy uses Levenshtein Distance to calculate the differences between strings. It also has a method that accepts an entire list (array) of items at once and that returns the item with a word that is most alike.

Importing FuzzyWuzzy

Before we can use FuzzyWuzzy in our Cloud Function, we must include it in our package.txt. To do this, add these two lines to package.txt:


The Cloud Function code

Just as with the examples above, this code snippet accepts a request with a JSON object. This JSON object contains the two lists or arrays with search terms and brands. For each search term, it finds the brand that is most alike and returns that brand together with a score.

The result

Our example returns this output:

{'buy nike shoes': ('nike', 100), 'adidas online': ('adidas', 100), 'rebok cloths': ('reebok', 56)}

The search term ‘buy nike shoes’ matches with the brand Nike with a 100% score. The search term ‘rebok cloths’ on the other hand matches the brand Reebok, but with a 56% score. A higher score means a closer match.

Now, all that’s left is to process these results. I’ll leave it up to you to come up with ideas on how to use Cloud Functions together with Google Ads Scripts! 🙂