Long running sessions and clustering

In the last blog post I briefly talked about the basics what to consider when you are writing cluster-aware code. The essence is to be aware of your write activities, and make sure that the scheduled activities are running only on a single cluster node and not on many or all of them.

Today’s focus is on the behavior of JCR sessions with respect to clustering. From a conceptual point of view there is hardly a difference to a single-node cluster (or standalone instance), but the presence of more cluster nodes add a new angle of potential problems to it.

When I talk about JCR, I am thinking of the Apache Oak implementation, which is implemented on top of the MVCC pattern. (The previous Jackrabbit implementation is using a different approach, so this whole blog post does not apply there.) The basic principle of MVCC is that each session is clearly separated from any other session which is open in parallel. Also any changes performed on a session is not visible to other sessions unless

  • the other session is invoking session.refresh() or
  • the other session is opened after the mentioned session is closed.

This behavior applies to all sessions of a JCR repository, no matter if the are opened on the same cluster node or not. The following diagram visualizes this

Diagram showing how 2 sessions are performing changes to the repository whithout seeing the changes of the other as long as they don’t use session.refresh()

We have 2 sessions A1 and B1 which are initiated at the same time t0, and which perform changes independently of each other on the repository, so session B1 cannot see the changes performed with A1_1 (and vice versa). At time t1 session A1 is refreshed, and now it can see the changes B1_1 and B1_2. And afterwards B1 is refreshed as well, and can now see the changes A1_1 and A1_2 as well.

But if a session is not refreshed (or closed and a new session is used), it will never see the changes which happened on the repository after the session has been opened.

As said before, these sessions do not need to run on 2 separate cluster nodes, you get the same behavior on a single cluster node as well. But I mentioned, that multiple cluster nodes are a special problem here. Why is that case?

That problem are OSGI services in the background, which perform a certain job and write data to the JCR repository. In a single-node cluster this not a problem, because all of these activities go through that single service; and if that service uses a long-running JCR session for it, that will never be a problem. Because this service is responsible for all changes, and the service can read and write all the relevant data. In a cluster with more than 2 nodes, each cluster node might have that service running, and the invocations of the services might be random. And as in the diagram above, on cluster node A the data A1_1 is written. And on cluster node 2 the data point B1_1 is written. But they don’t see each other’s changes if they don’t refresh the session! And in most applications, which are written for single-node AEM instances, session.refresh() is barely used, because in such situations there’s simply no need for it, as this problem never occurred.

So when you are migrating your application to AEM as a Cloud Service, review your applications and make sure that you find all long-running ResourceResolvers and JCR sessions. The best option is then to remove these long-running sessions and replace them with short-living ones, which are closed if the job is done. The second-best option is to introduce a session.refresh(), so the session sees any updates which happend to the repository in the meanwhile. (And btw: if you registering an ObservationListener in that session, you don’t need a manual refresh, as this refresh is done by the ObservationListener method anyway; what would it be for if not for reporting changes to the repository, which happen after opening the session?)

That’s all right now regarding cluster-aware coding. But I am sure that there is more to come 🙂

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