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.

Writing to the repository comes with such a static overhead; and it can be like a 20 minutes ride to the grocery store. Saving 10 times smaller batches definitely takes more time than saving once with a batch of 10-times the size.  At least if you keep the size of the changeset limited, for details here check this earlier posting of mine.

Check this great presentation of Georg Henzler at adaptTo() 2019 (starting at 17:00min ) (slides) for some benchmark data, how the size of the changeset influences the time to save (spoiler: for realistic sizes it does not really increase).

So I changed the above code to something like this:

for (Item item : manyItems) {   
  proprocessSingleItem (resolver, item);
} 
resolver.commit();

void processSingleItem (ResourceResolver resolver, Item i} { 
  // do something with the resourceResolver but no commit
}

Switching to this approach improved the performance for ~ 100 items by a factor of more than 10! And that’s an impressive number for such a minimal change.

So check your code for this specific coding pattern, find out if the parameters are good (that means small changes) and add some performance logging. And then convert to this batching mode and see what your numbers are doing.

Of course, very often this saving is operating in the context of a much larger operation, and a 10 times improvement in this area will only speed up the larger operation of 12 seconds to 11 seconds. But hey, when you get this 1 second for almost free, just do it (and we are still talking about micro-optimizations). But nothing prevents you from taking a deeper look into what the system is doing in the remaining 11 seconds.

Leave me a comment if you have some interesting story to share, where such small changes resulted in big improvements.

Micro optimizations are important, and their importance is described by a LWN posting about the linux kernel:

Most users are unlikely to notice any amazing speed improvements resulting from these changes. But they are an important part of the ongoing effort to optimize the kernel’s behavior wherever possible; a long list of changes like this is the reason why Linux performs as well as it does.

And is not specific for the Linux kernel, but you can apply the same strategy to every piece of software. AEM as a complex (and admittedly, it can sometimes be really slow) beast applies the very same.

There are a number of cases in AEM, where do you operate not only single objcets (pages, assets, resources, nodes), but apply the same operation on multiple of these objects.

The naive approach of just iterating the list and execute the operation on a single element of that list can be quite ineffective, especially if this operation comes with a static overhead.

Some examples:

• For replication there are some pre-checks, then the creation of the package, the creation of the sling jobs (or sending the package to the pipeline when running on AEM as a Cloud Service), the update of the replication status, writing the audit log entries.
• When determining the replication status of a page, the replication queues need to checked if this page is still subject to a pending replication, which can get slow when the queues are full.
• Committing changes to the JCR repository; there is a certain overhead in it (validating all changes, comitting them to permanent storage, invoking the synchronous listeners, locking etc).

And in many cases these bottlenecks are known for a while, and there is API which allows to perform this action in a batch mode for a multitude of elements:

• Replication: Batch replication (you can provide a number of path strings)
• Getting status for a large amount of resources: ReplicationStatusProvider.getBatchReplicationStatus
• The Audit Log
• and many more

(The ReplicationStatusProvider has been introduced some years back when we had to deal with large workflow packages being replicated, which resulted in a lot of traversales of the replication queue entries. Adding this optimized version improved the performance by at least a factor of 10; so even in less intense operations I expect an improvement.

So if you have a hand-crafted loop to execute a certain activity on many elements, check if a more efficient batch API is available. There’s a good chance that it is already there.

If you have more cases where batch mode should be available, you it isn’t, leave a comment here. I am happy to support to either find the right API or potentially kickstart a product improvement.