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Earlier posts covered the basics of A/B tests ( Part 1 and Part 2 ), core statistical concepts ( Part 3 and Part 4 ), how to build confidence in a decision ( Part 5 ), and the the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix ( Part 6 ).
To test wildcard indexes, let’s create a small collection for storing our users’ details. You can test them on your own using explain(). db.user.createIndex( { "$**" : 1 } ) Again, you can test the same queries we did before. The feature looks amazing, but it comes at some cost.
That is, a team can work in such a way that it is less likely to cause problems for itself, by e.g., writing unit tests, having continuous integration, developing to finely grained statements of business functionality, embedding QA in the development team, and so forth. Doing these isn’t the same as increasing the probability of success.
The SE toolkit was fast, had no memory leaks (monitoring scripts could run for years) and let me implement lots of cool performance monitoring ideas. Allan also introduced me to Richard McDougall, who had worked with Allan for Sun Australia, and Richard visited for a rotation to build some monitoring tools.
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