Evolution of SoundCloud’s Architecture

This is a story of how we adapted our architecture over time to accomodate growth. Scaling is a luxury problem and surprisingly has more to…

Evolution of SoundCloud’s Architecture

August 30th, 2012 by Sean Treadway

This is a story of how we adapted our architecture over time to accomodate growth.

Scaling is a luxury problem and surprisingly has more to do with organization than implementation. For each change we addressed the next order of magnitude of users we needed to support, starting in the thousands and now we’re designing for the hundreds of millions.  We identify our bottlenecks and addressed them as simply as possible by introducing clear integration points in our infrastructure to divide and conquer each problem individually.

By identifying and extracting points of scale into smaller problems and having well defined integration points when the time arrived, we are able to grow organically.

Product conception

From day one, we had the simple need of getting each idea out of our heads and in front of eyeballs as quickly as possible. During this phase, we used a very simple setup:

Apache was serving our image/style/behavior resources, and Rails backed by MySQL provided an environment where almost all of our product could be modeled, routed and rendered quickly. Most of our team understood this model and could work well together, delivering a product that is very similar to what we have today.

We consciously chose not to implement high availability at this point, knowing what it would take when that time hopefully arrived. At this point we left our private beta, revealing SoundCloud to the public.

Our primary cost optimization was for opportunity, and anything that got in the way of us developing the concepts behind SoundCloud were avoided. For example, when a new comment was posted, we blocked until all followers were notified knowing that we could make that asynchronous later.

In the early stages we were conscious to ensure we were not only building a product, but also a platform. Our Public API was developed alongside our website from the very beginning. We’re now driving the website with the same API we were offering to 3rd party integrations.

Growing out of Apache

Apache served us well, but we were running Rails app servers on multiple hosts, and the routing and virtual host configuration in Apache was cumbersome to keep in sync between development and production.

The Web tier’s primary responsibility is to manage and dispatch incoming web requests, as well as buffering outbound responses so to free up an application server for the next request as quickly as possible. This meant the better connection pooling and content based routing configuration we had, the stronger this tier would be.

At this point we replaced Apache with Nginx and reduced our web tier’s configuration complexity, but our architecture didn’t change.

Load distribution and a little queue theory

Nginx worked great, but as we were growing, we found that some workloads took significantly more time compared to others (in the order of hundreds of milliseconds).

When you’re working on a slow request when a fast request arrives, the fast request will have to wait until the slow request finishes, called “head of the line blocking problem”. When we had multiple applications servers each with its own listen socket backlog, analogous to a grocery store, where you inevitably stand at one register and watch all the other registers move faster than your own.

Around 2008 when we first developed the architecture, concurrent request processing in Rails and ActiveRecord was fairly immature. Even though we felt confident that we could audit and prepare our code for concurrent request processing, we did not want to invest the time to audit our dependencies. So we stuck with the model of a single concurrency per application server process and ran multiple processes per host.

In Kendall’s notation once we’ve sent a request from the web server to the application server, the request processing can be modeled by a M/M/1 queue. The response time of such a queue depends on all prior requests, so if we drastically increase the average work time of one request the average response time also drastically increases.

Of course, the right thing to do is to make sure our work times are consistently low for any web request, but we were still in the period of optimizing for opportunity, so we decided to continue with product development and solve this problem with better request dispatching.

We looked at the Phusion passenger approach of using multiple child processes per host but felt that we could easily fill each child with long-running requests. This is like having many queues with a few workers on each queue, simulating concurrent request processing on a single listen socket.

This changed the queue model from M/M/1 to M/M/c where c is the number of child processes for every dispatched request. This is like the queue system found in a post office, or a “take a number, the next available worker will help you” kind of queue. This model reduces the response time by a factor of c for any job that was waiting in the queue which is better, but assuming we had 5 children, we would just be able to accept an average of 5 times as many slow requests. We were already seeing a factor of 10 growth in the upcoming months, and had limited capacity per host, so adding only 5 to 10 workers was not enough address the head of the line blocking problem.

We wanted a system that never queued, but if it did queue, the wait time in the queue was minimal. Taking the M/M/c model to the extreme, we asked ourselves “how can we make c as large as possible?”

To do this, we needed to make sure that a single Rails application server never received more than one request at a time. This ruled out TCP load balancing because TCP has no notion of an HTTP request/response. We also needed to make sure that if all application servers were busy, the request would be queued for the next available application server. This meant we must maintain complete statelessness between our servers. We had the latter, but didn’t have former.

We added HAProxy into our infrastructure, configuring each backend with a maximum connection count of 1 and added our backend processes across all hosts, to get that wonderful M/M/c reduction in resident wait time by queuing the HTTP request until any backend process on any host becomes available. HAProxy entered as our queuing load balancer that would buffer any temporary back-pressure by queuing requests from the application or dependent backend services so we could defer designing sophisticated queuing in other components in our request pipeline.

I heartily recommend Neil J. Gunther’s work Analyzing Computer System Performance with Perl::PDQ to brush up on queue theory and strengthen your intuition on how to model and measure queuing systems from HTTP requests all the way down to your disk controllers.

Going asynchronous

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