Look at Your Data

John Rauser (Snapchat)
Mission City Ballroom
Please note: This and all other keynotes will be live streamed and recorded.
Average rating: ****.
(4.63, 35 ratings)

Modern monitoring software makes it easy to plot a statistic like
average latency every minute — too easy. Fancy dashboards of time
series plots often lull us into a false sense of security. Underneath
every point on those plots is a distribution, and underneath that
distribution is a series of individuals: your customers. If you don’t
take the time to look deeply at your data, you don’t truly understand
your business.

Photo of John Rauser

John Rauser


John has been extracting value from large datasets for over 20 years at hedge funds, small data-driven startups, Amazon, Pinterest, and now Snapchat. He has deep experience in machine learning, data visualization, on-line experimentation, website performance and real-time fault analysis. An empiricist at heart, “Just do the experiment!” is his favorite call to arms.

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Picture of Aaron Peters
Aaron Peters
06/20/2011 2:26pm PDT

Excellent talk. Simple, relevant to everybody in the room and very well delivered. A+

Aveek Misra
06/16/2011 6:01pm PDT

Nice session. However there are some things that John said that I would like to add with respect to the particular problem that he illustrated for looking at the raw data. While I don’t debate the fact that sometimes logs need to pored over to arrive at the exact issue, I think that should happen at the very last stage where you have at least a log or better a part of the log that you want to look into. In John’s case, I would have liked to have gotten to the point where I know which log I am looking for. Andthe way to do it is to gather more graphical data sliced and diced by different contexts such as the host (in this particular example). If you had a graph where you could track latencies per host, you could have easily seen the particular host or a bunch of hosts as the outlier. And this is precisely what we do in my company. There is a downside though – how many contexts can you allow. You have to track latencies per host, per web service call, per partner, per customer etc. and the list could get very big. Then there is the issue of how many values a particular context can have. If you wanted to track latencies per host and there are a million of them, there are possibly a million lines in your graph (assuming each host has a line). That is a challenge when you have to render the graph itself for example. But overall slicing and dicing the data on different contexts does help a lot. So a single graph can almost always never give you the overall picture, it has to be taken in context with other graphs that show the data sliced and diced on different contexts.

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Ernest Mueller
06/16/2011 2:37am PDT

Excellent session. I am always amazed by the things I find when I spend time delving more deeply into our own metrics or logs.

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Steve Souders
06/01/2011 4:52am PDT

John delivered two AMAZING talks last year. I’m super excited to have him back again. I love the theme of this talk. I’ve always encouraged performance engineers to take a few hours digging into an anomaly to figure out “why?”. Even if it’s not what you expected, it’s usually illuminating – as is John.

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