Internship stories

Last year, I wrote about my internship story┬ábecause 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.

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Sharding techniques

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.

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We’re moving. Goodbye Rackspace.

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:

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Building C extensions in Python

At Mixpanel performance is particularly important to us and as we begin to scale our data volume to support billions of actions. We’ve found ourselves thinking about how to solve problems better.

We’re currently writing a feature that is going require considerable scale and performance but in order to do it we had to think about how to do it in a time for our users to be happy. Unfortunately, Python is too slow for some types of operations we wish to do where we can get an order of a magnitude of performance out of something lower level like C.

So imagine: You want to stick to Python because it’s so fast to develop in but need the performance of C/C++. Let me introduce you to C extensions in Python.

If you’ve ever used something like cJSON in the past, then you’ve already installed something like this before–it’s likely a lot modules you import in Python are built in C and not just pure-python.

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