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The problem is that we don’t know exactly what our system is going to have to deal with. Our hub could be featured in a Reddit post, shared on Facebook, mentioned in a YouTube video, and before you know it, our 200k an hour call rate has ballooned to several millions of calls.

What does this mean for scalability? You need to address the foundation of your API as if you are always on the verge launching . Ensuring that you are using a proper load balancer is hugely important, and in many cases, can be the difference between success and failure. Ensuring that your API has failover paths and secondary functions can make a huge difference not only to your API’s usability, but towards general user experience.

address the foundation of your API as if you are always on the verge launching load balancer failover paths

In fact, in some cases the server issues can be skipped entirely. If we’re truly concerned with scalability and we anticipate that our service is going to have extreme fluctuations in traffic, we can decide to simply go for a serverless solution like Amazon Web Services . By tying into an extensible system and enabling our traffic to be off-loaded via hardcoded routes in the API architecture, we can dynamically scale to meet almost any demand — in essence, we can expect the unexpected.

Finally, one of the best ways you can plan for launch is to integrate analytics into your system. Launch day is a mystery, and there will never be a perfect prediction as to what it will mean for a product — that being said, you’re essentially playing an information game. And as such, any edge you can give yourself is valuable. Being able to see trends developing in real time can help you develop in an agile way and address deficiencies as they arise organically, while predicting further failures down the road.

Speaking of “expecting the unexpected,” you never truly know how successful you’re actually going to be. This isn’t a simple consideration of traffic, either — traffic might be high even if you’re the second or third most popular choice.

The fact is that a service might easily be the number one service in a matter of days, regardless of what the expectation is. Instances of the “Reddit Hug of Death”, which was previously called the “Slashdot Effect”, can instantly inundate sites with dramatic amounts of traffic, causing them to crash and burn. It is this “crashing and burning” that can prematurely kill the cost benefit argument for a service, and can directly cause loss of retention.

Simply put, a provider cannot know how much traffic they can expect, and thus when planning in scalability terms, providers should plan for the most extreme case possible within their means in order to enable the handling of these extremes. Another way to frame this would be to anticipate success .

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March 15, 2018 at 6:01 pm

Would this be bad news for difference-in-difference type estimator that’s popular in applied econometrics? There the goal is to estimate an interaction and find statistical significance…

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March 15, 2018 at 6:50 pm

I think the blog post is misleading in a couple of different ways.

(1) Contrary to the suggestion of the title, an interaction and a main effect will have the same power given the same N and the same standardized effect size (partial eta^2). Of course, the body of the blog post doesn’t actually dispute this–it’s based on the explicit assumption that the interaction effect size is half that of the main effects–so I’d just say the title is misleading, given that most people who see the title’s conclusion will likely interpret it as applying to situations where the standardized effect size is held constant.

(2) As noted in the addendum, there’s some ambiguity about what it means for the interaction effect size to be half that of the main effect on an unstandardized scale. I’d argue that we can only sensibly compare the sizes of unstandardized effects in this way after equating them on var(X), otherwise it’s sort of apples-to-oranges. The 16X conclusion violates this principle because it compares main effects on a [-1/2, +1/2] scale to an interaction that’s on a [-1/4, +1/4] scale. So when you cut β_interaction in half in addition to that change of scaling, I’d argue you’re really putting the interaction at a quarter the size of the main effects, not half the size. If you equate the scales first and then set β_interaction to half on that scale, the sample size multiplier is 4X, not 16X (as hinted in the addendum). This also agrees with the fact that the sample size multiplier is 4X, not 16X, when the standardized effect size (partial eta^2) for the interaction is half that for the main effects.

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I don’t really understand things like standardized effect size and partial eta—actually, I’ve never heard of partial eta. As discussed in the addendum, the power for estimating main effect and interaction will only be equal in a setting where, for example, the main effect is 0.6, with an effect of 0 for one group and 1.2 for the other. I agree that this can occur but I don’t think it’s typical. This is related to the piranha principle that we discussed not long ago on the blog.

In any case, I hope the R code and then the addendum helped.

To elaborate on one of the points implicitly arising in our discussion: There are cases where main effects are small and interactions are large. Indeed, in general, these labels have some arbitrariness to them, as my colleagues and I realized many years ago when studying congressional elections: recode the outcome from Democratic or Republican vote share to incumbent party vote share, and interactions with incumbent party become main effects, and main effects become interactions. So, yes, the above post is in the context of main effects which are modified by interactions; there’s the implicit assumption that if the main effect is positive, then it will be positive in the subgroups we look at, just maybe a bit larger or smaller. Again, I think this makes sense in most of the social science research I’ve seen, and I think it makes sense for most of the interactions that people look at—especially in the common setting that people look at lots of interactions, in which case I think most of them will have to be small—but it won’t apply in every case.

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