Using Google Lighthouse to monitor webpage performance

When it comes to analysing site performance, PageSpeed Insights remains the de facto option for many developers and marketers. Unfortunately, whilst it can provide a good top-level summary of potential issues, the tool has a number of shortcomings.

  1. Scoring system – The scores given by PageSpeed Insights are based on the individual factors it tests for, rather than actual page load times. In essence, a high score merely indicates that a URL correlates well with a performant webpage.
  2. Metrics used – By focusing solely on the individual factors that can contribute to low page load times, the tool ignores a multitude of useful performance metrics such as Page Weight, Number of Requests, Time to First Byte, First Paint and Load Time.
  3. Dated recommendations – Many of the recommendations given are invalidated by recent advances in web performance. Implementing HTTP/2, for example, makes workarounds like image spriting, domain sharding and file concatenation unnecessary.

Instead, far better tooling options include Chrome’s Developer Tools, WebPageTest and the more recently launched Google Lighthouse. Primarily designed to help support the development of Progressive Web Apps, Lighthouse’s Performance, Accessibility and Best Practices sections cover factors that are applicable to any website.

Google Lighthouse key features

The standard Lighthouse report provides information on a wide array of factors, in an easily digestible format. Some notable inclusions for website performance are:

  1. HTTP/2 – Provides a list of every internal resource that was not served over HTTP/2.
  2. WebP Images – Highlights all of the images that could be served in WebP, Google’s new, open source image format.
  3. Critical Request Chains – Lists the critical resources that had to be downloaded before the page could be rendered, and the order in which they were requested.
  4. First Meaningful Paint – The time required to begin rendering content within the browser.
  5. Time to Interactive – A measure of the point at which a page has loaded enough for a user to interact with it.
  6. Speed Index – An overall speed scoring, using Speedline, which measures how quickly a page is visibly populated.

Getting started with Google Lighthouse

Lighthouse is available for use as a Chrome extension but needs to open its own browser instance in a new tab. This can be quite annoying if you’re trying to do other things whilst the test runs. Instead, I’d recommend running it as a command line tool, using Node.js.

First off, we’ll need to install the LTS version of Node.js, which can be found on the official website. Once that’s done, load up the Node command prompt and install Lighthouse using the npm package manager with the following command:
npm install -g lighthouse
Command Line detail

Once this has finished you’re ready to go. Run an audit by inputting:
lighthouse [url]
Command Line detail

An HTML report will be saved to the directory you’re currently in. This is specified to the left of the blinking cursor within the command prompt, and can be changed by using the cd command.

For a full list of commands type:
lighthouse --help
Options include the ability to save reports in JSON, specify a different report name or save screenshots.

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Performing batch tests with Lighthouse

Standalone Lighthouse reports have utility by themselves, but for a better view of overall site performance you’ll want to audit multiple page templates. Lighthouse lacks the functionality to batch test out of the box, but the beauty of NPM is that you can usually find a user submitted package to perform a specific task.

In this instance, we can install Lighthouse Batch Reporter. Bring up the Node command prompt again and enter:
npm install lighthouse-batch -g

To run an audit enter the following command, using commas to separate the URLs:
lighthouse-batch -s ,[url1],[url2],[url3] -g
If you get a runtime error on Windows make sure you’ve added the ‘-g’. This may not be necessary on Mac or Linux, but bear in mind that the commands will be slightly different on a Unix terminal (see the options within the documentation).

Command line detail

The exports will be located at \[your file path]\report\lighthouse\. Add ‘-h’ if you want the report in HTML. It’s also worth noting that running another batch will overwrite the old ones unless you move them or specify a different output folder.

Other packages to explore

Hopefully this post has demonstrated how Lighthouse – and in fact other tools – can help you when analysing webpage performance. But if you want to take things further then there are lots more packages you can explore.

One worth looking at is Pwmetrics, which uses the Lighthouse API but only collects the key performance metrics. This can also be integrated to push the results to Google Sheets, or automatically save Chrome DevTools timeline traces to Google Drive. These can then be shared and compared.

Other interesting options include Phantomas and the more well-known, both of which collect a huge range of metrics by leveraging multiple open source tools.

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