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.
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.