On Monday we shipped distinct_id aliasing, a service that makes it possible for our customers to link multiple unique identifiers to the same person. It’s running smoothly now, but we ran into some interesting performance problems during development. I’ve been fairly liberal with my keywords; hopefully this will show up in Google if you encounter the same problem.
The operation we’re doing is conceptually simple: for each event we receive, we make a single MySQL SELECT query to see if the distinct_id is an alias for another ID. If it is, we replace it. This means we get the benefits of multiple IDs without having to change our sharding scheme or moving data between machines.
A single SELECT would not normally be a big deal – but we’re doing a lot more of them than most people. Combined, our customers have many millions of end users, and they send Mixpanel events whenever those users do stuff. We did a little back-of-the-envelope math and determined that we would have to handle at least 50,000 queries per second right out of the gate.
At Mixpanel, we believe giving our customers a smooth, seamless experience when they are analyzing data is critically important. When something happens on the backend, we want the user experience to be disrupted as little as possible. We’ve gone to great lengths to learn new ways for maintaining this level of quality, and today I want to share some of the techniques were employing.
Mixpanel.com runs Django behind nginx using FastCGI. Some time ago, our deploys consisted of updating the code on our application servers, then simply restarting the Django process. This would result in a few of our rubber chicken error pages when nginx failed to connect to the upstream Django app servers during the restart. I did some Googling and was unable to find any content solving this problem conclusively for us, so here’s what we ended up doing.
Memcache is great. Here at Mixpanel, we use it in a lot of places, mostly to cache MySQL queries but also for other data stores. We also use kestrel, a queue server that speaks the memcache protocol.
Because we use eventlet, we need a pure python memcache client so that eventlet can patch the socket operations to be non-blocking. The de-facto standard for this is python-memcached, which we used until recently.
This post is a follow up to Why we moved off the cloud.
As a company, we want to do reliable backups on the cheap. By “cheap” I mean in terms of cost and, more importantly, in terms of developer’s time and attention. In this article, I’ll discuss how we’ve been able to accomplish this and the factors that we consider important.
Backups are an insurance policy. Like conventional insurance policies (e.g. renter’s), you want piece of mind that your stuff is covered if disaster strikes, while paying the best price you can from the available options.
Backups are similar. Both your team and your customers can rest a bit more easily knowing that you have your data elsewhere in case of unforeseen events. But on the flip side, backups cost money and time that could be better applied to improving your product — delivering more features, making it faster, etc. This is good motivation for keeping the cost low while still being reliable.
Last year, I wrote about my internship story because I felt it was such an impactful experience for me. It was simply a story of how working hard and being out in Silicon Valley can lead to very serendipitous occurrences. I don’t think I could have built Mixpanel without the knowledge and connections I gained at Slide. I learned so much about product, how to “get things done” at a real company, and met really close friends that I will take with me forever in life. I was also fortunate enough to work closely with Max, who has been an invaluable mentor and investor for our business.
The point of that post, of course, was to find ourselves interns. We wanted to get a lot of work done, but we also genuinely wanted to give them an extremely meaningful experience like my own. We’d publicly promised them one, so we set out to make good on it. At the end of the summer I asked them to write about what it was like to intern at Mixpanel. I hope those of you that are considering interning at a startup vs. a big company will benefit.
This post is a follow up to We’re moving. Goodbye Rackspace.
Cloud computing is often positioned as a solution to scalability problems. In fact, it seems like almost every day I read a blog post about a company moving infrastructure to the cloud. At Mixpanel, we did the opposite. I’m writing this post to explain why and maybe even encourage some other startups to consider the alternative.
A core component of Mixpanel is the server that sits at http://api.mixpanel.com. This server is the entry point for all data that comes into the system – it’s hit every time an event is sent from a browser, phone, or backend server. Since it handles traffic from all of our customers’ customers, it must manage thousands of requests per second, reliably. It implements an interface we’ve spec’d out here, and essentially decodes the requests, cleans them up, and then puts them on a queue for further processing.
Because of these performance requirements, we originally wrote the server in Erlang (with MochiWeb) two years ago. After two years of iteration, the code has become difficult to maintain. No one on our team is an Erlang expert, and we have had trouble debugging downtime and performance problems. So, we decided to rewrite it in Python, the de-facto language at Mixpanel.
Given how crucial this service is to our product, you can imagine my surprise when I found out that this would be my first project as an intern on the backend team. I really enjoy working on scaling problems, and the cool thing about a startup like Mixpanel is that I got to dive into one immediately. Our backend architecture is modular, so as long my service implemented the specification, I didn’t have to worry about ramping up on other Mixpanel infrastructure.
At Mixpanel, we process billions of API transactions each month and that number can sometimes increase rapidly just in the course of a day. It’s not uncommon for us to see 100 req/s spikes when new customers decide to integrate. Thinking of ways to distribute data intelligently is pivotal in our ability to remain real-time.
I am going to discuss several techniques that allow people to horizontally distribute data. We have conducted interviews (by the way, we’re hiring engineers) with people in the past that make poor decisions in partitioning (e.g. partitioning by the first letter in a user’s name) and I think we can spread some knowledge around. Hopefully, you’ll learn something new.
At Mixpanel, where our hardware is and the platform we use to help us scale has become increasingly important. Unfortunately (or fortunately) our data processing doesn’t always scale linearly. When we get a brand new customer sometimes we have to scale by a step function; this has been a problem in the past but we’ve gotten better at this.
So what’s the short of it? We’re unhappy with the Rackspace Cloud and love what we’re seeing at Amazon.
Over the history we’ve used quite a few “cloud” offerings. First was Slicehost back when everything was on a single 256MB instance (yeah, that didn’t scale). Second was Linode because it was cheaper (money mattered to me at that point). Lastly, we moved over to the Rackspace Cloud because they cut a deal with YCombinator (one of the many benefits of being part of YC). Even with all the lock in we have with Rackspace (we have 50+ boxes and hiring if you want to help us move them!), it’s really not about the money but about the features and the product offering, here’s why we’re moving: