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Why Is The Google Cloud UI So Slow?

· Updated on · 7 min read

Opening a page in the Google Cloud Console always takes a long time.

Here are some metrics I collected on a high-end 2018 MacBook Pro on a UK-based Gigabit internet connection.

PageDownloadJavaScriptCPU TimeMain ContentFully Loaded
Cloud Functions4.2 MB15.7 MB5.3s6.7s8.1s
Compute Engine4.5 MB15.1 MB6.5s6.7s8.1s
Cloud Storage4.3 MB16.2 MB6.2s6.5s8.2s
Download size is the compressed size, JavaScript size is uncompressed. Main Content is the time when e.g. the Cloud Functions become visible, Fully Loaded is when no more changes are made to the UI.

We can see that each page loads over 15 MB of JavaScript code. A look at the performance timeline in Chrome DevTools confirms that running this code is the primary cause of the poor page performance.

DevTools CPU timeline showing a large amount of JavaScript work

This article will take a closer look at the page load process of the Google Cloud Functions page, and examine how it could be sped up.

You can use these strategies to investigate and improve the performance of the apps you're working on.

Loading the HTML document

The initial HTML request is very fast and only takes about 150ms. It contains an embedded SVG spinner that shows while the first chunk of JavaScript code is loading.

Loading the initial JavaScript bundles

These are the first two JavaScript bundles the page starts loading.

  • routemap 21 KB (103 KB uncompressed)
  • core,pm_ng1_bootstrap 1.3 MB (4.8 MB uncompressed)

These files don't take too long to download, but running the code freezes the UI for a while. The spinner SVG becomes stuck at this point, until it's replaced by a skeleton UI for Google Cloud Console page.

Filmstrip showing initial rendering of the GCP page

Here's what happens when the browser wants to run some JavaScript code.

  1. Parsing (done lazily at first, and then as needed later on)
  2. Compilation (also happens lazily)
  3. Initialization – the browser runs module initialization code, i.e. code that runs when loading a module rather than when calling one of its functions
  4. Running core app code – renders the application using the initialized modules

For the whole Google Cloud page, just parsing the source code takes 250ms, and compilation takes another 750ms (not including the 113 ms spent on "Compile Script").

DevTools profile showing a breakdown of CPU activity

The initial render of the Angular app takes about 1s.

JavaScript execution flamechart

Eventually we start to see a new spinner.

Page frame and new spinner

Loading page bundles

Once the generic Google Cloud UI is rendered the page starts loading 18 additional JavaScript files with an overall size of 1.5 MB.

Making a lot of separate requests isn't actually a problem though – it can improve performance by increasing the likelinhood of cache hits, and splitting up bundles makes it easy to load only necessary code.

After loading the first set up bundles the app starts making fetch requests and loads 3 more bundles at a total size of 6 MB.

When loading the page on my normal network the requests all kind of blurred together and it was hard to see which requests were sequential. So this screenshot shows the request chart on a throttled connection.

Request waterfall showing three sets of JavaScript being loaded sequentially

Loading the list of Cloud Functions

The request loading the list of Cloud Functions takes about 700ms. But it doesn't start as soon as the bundles are loaded, in part because there's a testIamPermissions request that needs to finish first.

As a result the CPU ends up being idle for half a second – this time could be used better if the request started sooner.

Waterfall showing requests made to load the list of cloud functions

Finally the app re-renders and we get the list of Cloud Functions we wanted to see.

Page showing GCP Cloud Functions

Detecting unused code in Chrome DevTools

Chrome DevTools has a code coverage tool tracks which parts of the code actually run on the current page. This can help identify code that doesn't have to be loaded.

The Cloud Functions page runs 53% of the JavaScript code it downloads. This is actually a bit disappointing, as it means that even if only necessary code is loaded it would still only cut the total JavaScript size of the page in half.

Chrome DevTools Code Coverage tool

Moving configuration into JSON

A good amount of the code actually consists of configuration objects. For example, this 200 KB object with 4997 keys.

Configuration object in a JavaScript bundle

Loading this as a JSON string with JSON.parse could be faster, as JSON is simpler to parse than a JavaScript object. This would be easy to do, but might not result in a huge performance improvement.

Ideally the app wouldn't need to load the full list on the client, but this would be harder to implement.

Reduce code duplication

The 200KB JSON object above is actually included in two of the JavaScript bundles. Breaking it out and reusing it would save download and processing time.

The same seems to apply to a bunch of UI components, like this one.

Duplicate code in DevTools code search

Prioritize primary content

The Google Cloud page loads a large initial JavaScript bundle. The longer it takes to load and initialize this code, the longer it takes to load page-specific code and to render the list of Cloud Functions the user wants to see.

But the initial bundle also contains secondary content, like the complex navigation sidebar. This menu becomes functional before the main page content is loaded, but it should only be loaded after the primary content.

Sidebar menu is open while main content is still loading

Google Cloud already does this in some cases. For example, the page initially renders a simpler version of the header and then loads more complex features later on.

Header doesn't show project dropdown at first and then shows it later


While the performance of static pages tends to be dominated by render-blocking network requests, single-page apps are often blocked by JavaScript execution or loading account data.

Downloading large amounts of code can hurt performance on slow connections, but due to compression and caching CPU processing often has a greater impact.

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