Jonathan George's Blog

Optimising an ASP.NET MVC web site, part 5: Putting your money where your mouth is

Posted in Technical Stuff by Jonathan on November 3, 2009

Note: This was originally posted on my old blog at the EMC Consulting Blogs site.

This is the final part of a series of posts on optimisation work we carried out on my last project, – an ASP.NET MVC web site built using S#arp Architecture, NHibernate, the Spark view engine and Solr. There’s not much point starting here – please have a look at parts 1, 2, 3 and 4, as well as my post on improving YSlow scores for IIS7 sites, for the full picture.

In the posts on this series, I’ve reflected the separation of concerns inherent in ASP.NET MVC applications by talking about how we optimised each layer of the application independently. Good separation of concerns is by no means unique to applications built using the MVC pattern, but what stood out for me as I became familiar with the project was that for the first time it seemed like I hardly had to think to achieve it, because it’s so baked into the framework. I know I share this feeling with Howard and James (respectively architect and developer on the project), who’ve both talked about it in their own blogs.

The MVC pattern also makes it much easier to apply optimisations in the code. For example, it’s much easier to identify the points where caching will be effective, as the Model-View-ViewModel pattern makes it straightforward to apply a simple and highly effective caching pattern within the controllers. I know that this kind of thing isn’t limited to performance work – for example, our team security guru certainly felt that it was easier to carry out his threat modelling for this site than it would have been in a WebForms equivalent.

On the flip side, this process also brought home to me some of the dangers of using NHibernate. It’s an absolutely awesome product, and has totally converted me to the use of an ORM (be it NHib or Entity Framework). However, the relatively high learning curve and the fact that most of the setup was done before I joined the project made it easy for me to ignore what it was doing under the covers and code away against my domain objects in a state of blissful ignorance. Obviously this is not a brilliant idea, and properly getting to grips with NH it now jostling for first place on my to-do list (up against PostSharp 2 and ASP.NET MVC 2.0, amongst other things.)

My big challenge for future projects is ensuring that the optimisations I’ve talked about are baked in from the start instead of being bolted on at the end. The problem with this is that it’s not always clear where to stop. The goal of optimising the site is to get it to the point where it performs as we need it to, not to get it to the point where we can’t optimise any more. The process of optimisation is one of diminishing returns, so it’s essential to cover issues you know need to be covered and to then use testing tools to uncover any further areas to work on.

That said, in an ideal world I’d like to be able to build performance tests early and use them to benchmark pages on a regular basis. Assuming you work in short iterations, this can be done on an iteration by iteration basis, with results feeding into the plan for the next iteration. My next series of posts will be on performance and load testing, and as well as covering what we did for this project I will be looking at ways of building these processes into the core engineering practices of a project.

Was it all worth it?

I’ll be talking separately about the performance and load testing we carried out on the site prior to go live, but in order to put these posts into some context I thought it might be interesting to include some final numbers. For our soak testing, we built a load profile based on 6 user journeys through the site:

  • Homepage: 20% of total concurrent user load
  • Browse (Home -> Category Landing -> Category Listing -> Product): 30%
  • Search (Home -> Search Results): 30%
  • News (Home -> News list -> News story): 10%
  • Static Pages (Home -> Static page): 5%
  • Checkout (As for browse journey, then -> Add to basket -> View Basket -> Checkout): 5%

With a random think time of 8 – 12 seconds between each step of each journey, we demonstrated that each of the web servers in the farm could sustainably support 1000 concurrent users and generate 90 pages per second. Given the hardware in question, this far exceeds any project I’ve worked on recently.

In the end, we put live in the run up to Halloween, the busiest time of the year for the fancy dress industry. We did this with no late nights and enough confidence to go to the pub for a celebratory pint within the hour. It was also interesting that the majority of technical colleagues who responded to our go-live announcement commented on how fast it runs (which given the machinations of our corporate network’s internet routing is even more remarkable.) And best of all, we’ve had no major shocks since the site went live.

A final note

If you’ve read this series of posts, I hope you’ve got something out of it. I’d certainly be interested in any feedback that you might have – as always, please feel free to leave a comment or contact me on Twitter.  In addition, the EMC Consulting blog site has been nominated in the Computer Weekly IT Blog Awards 2009, under the “Corporate/Large Enterprise” category – please consider voting for us.

I’d also like to extend a final thanks to Howard for proof reading the first draft of these posts and giving me valuable feedback, as well as for actually doing a lot of the work I’ve talked about here.


Optimising an ASP.NET MVC web site, part 4: Output caching in the brave new world of MVC

Posted in Technical Stuff by Jonathan on November 3, 2009

Note: This was originally posted on my old blog at the EMC Consulting Blogs site.

This is the penultimate part in a series of posts on optimisation work we carried out on my last project, – an ASP.NET MVC web site built using S#arp Architecture, NHibernate, the Spark view engine and Solr. Please also take the time to read parts 1, 2 and 3 for the full picture.

Output caching is something I’ve used extensively on previous ASP.NET WebForm projects, and I was surprised when I found out that it’s relatively immature in ASP.NET MVC. The core library contains the OutputCache filter, which does the same under the covers as the @OutputCache declaration in ASP.NET Web Forms. However, caching a full page is something that rarely suits in the current climate of dynamic and personalised websites – for example (and in common with most ecommerce websites) our site includes a summary of the user’s basket on every page. In the world of web forms, I’d use the asp:substitution control to implement donut caching, or implement fragment caching. Unfortunately neither of those options exist in the brave new world of ASP.NET MVC (if you’re a Java/Ruby/etc developer… stop sniggering, we’re catching you up 🙂 )

So there are a few ways round this that I’m aware of. The first involves using the partial views to do something similar to the old style ASP.NET Fragment Caching approach. In the WebForms world, this is implemented by splitting your page up into multiple user controls, and applying output caching as appropriate to the individual controls. Here’s a pretty basic example of how a product page on an ecommerce website might be laid out.

Page layout

The boxes coloured blue, green and purple can all be cached – albeit with different cache lifetimes and keys. The red box, which displays my user  basket total is unlikely to be cachable – at least not at the rendering level. Using the classic ASP.NET fragment caching approach, each of my coloured boxes would be implemented as a separate user control with appropriate OutputCache directives set. The ASPX page itself would contain very little other than the instances of these controls.

So how does this translate into MVC? It turns out that it’s pretty much the same. You create a number of partial views and add an OutputCache directive to their markup. Phil Haack covers the approach in this blog post. There’s just one downside: that approach only works with the standard WebForms view engine, and we’re using Spark, so we can’t go down this route.

I did hear a suggestion that making use of the RenderAction method from ASP.NET MVC 1.0 Futures could be the way forward. Essentially, each of my coloured boxes from the diagram would end up corresponding to a separate controller action, each of which would have an OutputCache action filter applied. These would then be pulled together by a “dumb” controller action whose corresponding view had multiple calls to Html.RenderAction to compose the chunks of the view together.

On the face of it – and assuming you were willing to accept the overhead involved in repeatedly invoking the full MVC request pipeline – it would work. However, there has been mention of a bug with MVC 1.0 which causes the OutputCache filter on an action method called with RenderAction to be applied to the overall request, not just the chunk being dealt with. This kind of thing is probably why the RenderAction method was bumped into MVC Futures instead of being shipped as part of the core MVC 1.0 release.

Phil Haack blogged something else that didn’t quite make it to MVC 1.0 which looked good on the surface. Essentially, it’s an HtmlHelper extension that hooks into the API used by the asp:substitution control. However, I had a bit of a headache in trying to work out how to use it. The problem is that within the callback you supply to the Substitute method, you don’t have access to your ViewData – not a massive surprised once you understand how the post-cache substitution mechanism in ASP.NET works. This means that you need to code some other method – which is going to be a static method you’ve stashed alongside your controllers – that will do all the necessary work, pulling bits of data out of the supplied HttpContext and returning a string to be dumped directly into the view.

There’s no doubt that this would work, and with some thought could be done without losing the testability and separation of concerns that makes the MVC pattern great. However, it’s not an ideal approach for me because it does mean that the pattern and conventions are broken to support what’s essentially an optimisation step. Because of this it will make it harder for people to see what’s going on in the code. I’ve already covered the caching we applied in the service layer; to me, output caching should be contained within the View Engine layer and should not leak beyond that. After all, there’s nothing else in my controller layer or below that couples my code to Spark, so I have no desire to introduce something that does.

Fortunately it turns out that the 1.1 version of the Spark view engine will contain pretty comprehensive output caching. This isn’t part of the v1.0 release, but has been in the development builds for several months now and is stable. It’s absolutely perfect for what I wanted as it allowed me to implement output caching with very few changes outside the views.

Unlike the ASP.NET WebForms fragment caching approach, you aren’t required to split your view into partials – you simply add <cache> elements around the sections of the page you want to cache. These sections can be dependent on different keys and cached for different durations, and there’s also a signalling mechanism that allows cached content to be expired on demand. In our case, we had already tackled the issue of cache keys for a ViewModels when we looked at caching in the controller layer, so it was a simple matter to use these same cache keys to control our output caching.

Spark also contains something called the ValueHolder which effectively allows you to defer the gathering of model data until it’s needed. This means that rather than build up the model for every request, only to pass it to a view which doesn’t need it because it’s entirely output cached, you can build your model using ValueHolder objects containing lambda functions that will only be executed if the data is needed. This seems like an interesting approach, but it’s not one I explored in detail because the caching we’d already implemented on the controllers made it less relevant.

One of my major hopes, which was unfortunately not realised, was that we’d be able to connect Spark’s output caching service to our distributed cache, Velocity. This would further reduce the workload across the web farm because it would mean that once a page was served from one webhead, it would be available pre-built to all of the others. However the output caching mechanism in Spark places unserializable objects into the cache, making it difficult to use with an out-of-process caching mechanism. This is a shame but by no means a deal breaker.

I’ve seen a few discussions around the value of output caching in the MVC world, with some saying that because the views are essentially just writing text to a stream, there is little gain to be had from caching. On a purely subjective level, the output caching did seem to make the site faster. It’s difficult to be sure because there is no way of enabling/disabling output caching via config in Spark, so it’s not easy to do comparative tests in a realistic environment. I can see the argument, and I’d certainly agree that out of the different focus areas output caching made the least difference to overall performance, but I believe it did make a difference and for the minimal effort involved in implementing it, was worth it.

In the final section, I’ll talk about my views on optimising this MVC based site compared to my experiences in the WebForms world, and share some hard numbers gained from our performance testing.