Using PhantomJS at scale

About a year ago SmugMug had a dilemma. Our upcoming site-wide redesign and refactor  (aka the new SmugMug) moved all of our rendering code into client-side JavaScript using YUI. We had a problem; SEO is critical for our customers and search engines couldn’t index the new site.

Possible solutions were thrown around: do we duplicate our code in PHP? Use an artificial DOM? What about PhantomJS? Duplicating code would be a monumental effort and a continued burden when writing new features. Initial tests of fake/artificial DOMs proved unreliable. A small prototype Node.js web server that hooked into PhantomJS proved promising. Node.js’ async model would be perfect for handling things that wait for I/O like rendering webpages. We came up with the project name ‘The Phantom Renderer’ soon after.

The prototype

I spent a few days whipping up a prototype proxy server to test with that worked like so:

  • Node.js web server accepts a url in the querystring
  • Send that URL to a newly-spawned PhantomJS process that listens on stdin
  • PhantomJS fetches the page, we wait 500ms after the last HTTP request is sent to get the rendered content via the page.content property
  • Send content back to Node.js
  • Send content back to search bot

We thought we had a fairly simple and working solution.

The Reality

While our prototype worked (mostly), we knew we had a lot of work to do. Our pages were complex JavaScript applications with many HTTP requests and expectations that they would live in a ‘traditional’ desktop browser. GoogleBot sometimes would crawl us at over 500 reqs/s. PhantomJS can be CPU and memory intensive (and randomly crashes or freezes). We had to be absolutely sure we were sending back fully rendered pages.

Problem 1: When is a webpage ‘complete’?

In our prototype app we assumed that a webpage was ‘finished’ 500ms after the last HTTP request had begun. As you can probably already guess, this is incredibly naive. Our site loads dozens of images, scripts and stylesheets (not to mention lots of analytics code). Some load instantly, some take > 500ms to return content. What happens if a request completely fails? If the page is redirected (301, 302 or even via JS/meta tag)?  404s? We had to handle all those cases appropriately and gracefully.

At first, we had many pages that looked like this after ‘rendering’:

blank page

Obviously, this wasn’t going to work.

Through a lot of manual testing and QA we eventually came to a solution where we tracked each and every HTTP request PhantomJS makes and watch every step of the transaction (start, progress, end, failed). Only once every single request has completed (or failed, etc) we start ‘waiting’. We give the page 500ms to either start making more requests or finish adding content to the DOM. After that timeout we assume the page is done.

Once we did that, we had a 100% success rate for rendering pages and saw pages that looked like this:Screen Shot 2013-12-03 at 5.11.05 PM

Much better! But we weren’t out of the woods yet…

Problem 2: PhantomJS and Node.js Bugs


Getting PhantomJS to render pages correctly during testing was a lot of work, but dealing with PhantomJS bugs made tear our hair out on occasion. When you are dealing with > 500 requests/second you uncover sporadic, random bugs that most people don’t. Also we are using a large percentage of the PhantomJS API, which means we are more likely to hit bugs or undocumented behavior. We also were new to PhantomJS so there was lots of user error 🙂

Some of these fun bugs and problems we dealt with were:

  • If PhantomJS got in a redirect loop it would hog all CPU and rapidly fill up memory until it crashed itself or the server it was on
  • Random ECONNRESET errors from child processes upon termination
  • Small percentage of PhantomJS processes simply not returning
  • PhantomJS’ onResourceRequested and onResourceReceived returning different URLs for the same resource due to url encoding. This causes problems if you are tracking requests.
  • Expecting PhantomJS processes to terminate cleanly. Instead tell it to exit, then kill the process. Double tap!

Problem 3: Scaling PhantomJS and NodeJS


Since this was a brand new project and we knew rendering web pages was CPU intensive, we spent a lot of time running benchmarks (and learning how to benchmark).

Our testing infrastructure consisted of a test Phantom Renderer box and a separate server running http_load that was used to send varying amounts of traffic. We created a list of 600 public gallery urls from our most popular customer sites and repeatedly slammed our test server with varying load to determine the best combination of processes, CPU and RAM.

It’s important to also document raw number of requests/sec and response time. A server isn’t very useful if it can handle hundreds or thousands of requests/sec but takes far too long to complete them.

When performance testing we learned a few things:

  • Don’t test against your normal QA/test environment. This will make your QA and dev teams unhappy.
  • Do make sure the any dependent services can also hand additional load/traffic!
  • Do use as close to production workloads and data.
  • Do repeat your tests multiple times to allow for services to ‘warm up’.
  • Do test multiple configurations (number of processes, max connections, etc) on the same hardware.
  • Do write down all your results and extra data
  • Do test for long periods of time (hours at least). You’ll probably uncover issues that won’t occur during a short performance test.

We also had a few problems scaling PhantomJS once it was in production and running for long periods of time:

  • Setting PhantomJS’ cache size too big, causing all 64 PhantomJS processes to slam the disk with reads and writes when the cache filled up and needed items removed.
  • Running too many PhantomJS instances, filling up RAM over a period of a few hours and causing processes to be killed.
  • Node.js’ Cluster module on Ubuntu not load balancing equally between processes, causing server CPU to be underutilized (fix is to put HAProxy in front of Node.js)
  • Setting too high of a limit on number of connections on our HAProxy servers, overloading our servers.

We also spent some time optimizing PhantomJS to load pages quickly by turning off image loading, allowing it to use a small disk cache and keeping the PhantomJS processes alive instead of respawning them for every request. We also spawn a separate Node.js process for each processor core, allowing for massive parallelization.

The importance of logs


During testing and tuning Phantom Renderer, we developed one strong habit; log everything. When we first started the project, we had no logging whatsoever. Debugging issues was easy at first when the codebase was small, but once it grew in size and complexity debugging became much more difficult. When Phantom Renderer was being tested it was difficult to determine the cause of bugs and errors (or even what PhantomJS and Node.js was doing).

About midway through the project we started using Winston, a great logging utility for Node.js. With Winston in place we added logging to every single step of the render process in PhantomJS and HTTP process in Node.js. We also used Winston’s log levels to allow for different levels of logging for debugging and production. Combining that with Splunk gave us deep insights into how specific requests were handled and how often certain errors were occurring in production. If you’re starting a new project logging should be a required piece of it.

The future of The Phantom Renderer

We’re hoping to open source The Phantom Renderer sometime in the near future. Hopefully it will be useful for web apps that have a mix of different frontend and backend technologies. Let us know if it’s something your team or company is interested in using!

We’ll be posting more in-depth posts about our experience with PhantomJS and NodeJS. Stay tuned!

Logs photo By Aapo Haapanen from Tampere, Finland (Logs) [CC-BY-SA-2.0 (], via Wikimedia Commons