Cluster aware coding in AEM

With AEM as a Cloud Service quite a number of small things have changed; and next to others you also get real clustering support in the authoring environment. Which is nice, because it gives you downtime-less authoring during deployments.

But this cluster also comes with a few gotchas, and one of them is that your application code needs to be cluster-aware. But what does that mean? What consequences does it have and what code do you have to change if you have never paid attention to this aspect?

The most important aspect is to do “every change only once“. It doesn’t make sense that 2 cluster nodes are importing the same set of data. A special version of this aspect is “avoid concurrent writes to the same node“, which can happen when a scheduled job is kicked off at the same time on all nodes, and this job is trying to change something in the repository. In this case you don’t only have overhead, but very likely a lot of exceptions.

And there is a similar aspect, which you should pay attention to: connections to external systems. If you have a cluster, running the same code and configs, it’s not always wanted that each cluster node reaches out to that external system. Maybe you need to the update it with the latest content only once, because it triggers some expensive processing on their side, and you don’t want to have that triggered two or three times, probably pretty much at the same time.

I have mentioned you 2 cases where a clustered application can be behave differently than a single-node environment, now let me show you how you can make your application cluster-aware.

Scheduled jobs

Scheduled jobs are a classic tool to execute certain jobs at a certain time. Of course we could use the Sling Scheduler directly, but to make the execution more robust, you should wrap it into a Scheduled Sling Job.

See the Sling Jobs website for the documentation and some example (although the Javadocs are missing the ScheduleBuilder class, but here’s the code). And of course you should check out Kaushal Mall’s post with even more examples.

Jobs give you the guarantee, that this job is going to be executed only at least once.

Use the Sling Scheduler only for very frequent jobs (e.g. once every 5 minutes), where it doesn’t matter if one execution is skipped, e.g. because the instance was just restarting. To limit the execution of such a job to a single node, you can annotate the job runner with this annotation:

@Property (name="scheduler.runOn", value="SINGLE")

(see the docs)

What about caches?

In-memory caches are often used to speed up operations. Most often they contain the results of previous operations which are then reused; cache elements are either actively purged or expire using a time-to-live.

Normally such caches are not affected by clustering. They might contain different items with potentially different values in the cluster nodes, but that must never be a problem. If that is a problem, you have to look for a different approach, e.g. persisting the data to the repository (if they are not already coming from there) or externalizing the cache (e.g to a redis or memcached instance).

Also, having a simpler application instead of the highest-cache-hit ration possible is often a good trade-off.

Ok, these were the topics I wanted to discuss here. But expect a blog post about one of my favorite topics: “Long running sessions and clustering”.

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