AEM micro-optimizations (part 3) | AEM Community Blog Seeding

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AEM micro-optimizations (part 3) by Jörg Hoh

Abstract

Welcome to my third post on AEM micro-optimizations. Again with some interesting ways how you can improve your AEM application performance, somethings with little improvements, but sometimes with significant ones.

During some recent performance optimization I came across code, which felt a bit odd. Technically it was quite easy:

for (Item item : manyItems) {
proprocessSingleItem (resolver, item);
}
void processSingleItem (ResourceResolver resolver, Item i} {
// do something with the resourceResolver
resolver.commit();
}
That is indeed a very common pattern, especially in software, which evolved over time: You have code, which deals with a single item. And later, if you need to do it for multiple items, you execute this code in a loop. Works perfectly, and the pattern is widely used.

And it can be problematic.

If you have an operation in that performSingleItem() method, which comes with a method creating some overhead . Maybe you are not aware of that overhead, so it goes unnoticed. Maybe you expect, that if a that performSingleItem() method takes 5 ms for an item, requiring 50 ms for 10 items is ok. Well, an O(n) algorithm isn’t too bad, is it?

But what if I tell you, that the static overhead of that method is that so large, that providing 10 items as parameters instead of just one will increase the runtime of it not by a factor of 10, but only by a factor of 1.1?

Imagine you need to go grocery shopping for your Sunday dinner. You get yourself ready, take the bike to the grocery store, get the potatoes you need. Pay, and get back home. Drop the potatoes there. Then again, taking the bike to the grocery store, getting the some meat. Back home. Again to the grocery store, this time for paprika (grilled paprika are delicious …). And so on and so on, until you have everything you need for your barbecue on Sunday. You spent now 6 hours mostly on the bike and waiting at the counter.

Are you doing that? No, of course not. You drive once to the grocery store, get all the things and pack them onto your bike, and get home. Takes maybe 90 minutes. Have the static overhead (cycling, waiting at the counter) just once saves a lot of it.

It’s the same in coding. You have static overhead (acquiring locks, getting database connections, network latency, calling through thick framework layers will just copying references to the data), which is not determined by the amount of data you process. But unlike in the example of grocery shopping it’s not directly visible at which times there is such a static overhead, and unfortunately documentation rarely point that out.

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AEM micro-optimizations (part 3)

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