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Data Store Garbage Collection data-store-garbage-collection

When a conventional WCM asset is removed, the reference to the underlying data store record may be removed from the node hierarchy, but the data store record itself remains. This unreferenced data store record then becomes 鈥済arbage鈥� that does not need to be retained. In instances where several garbage assets exist, it is beneficial to get rid of them to preserve space and to optimize backup and filesystem maintenance performance.

For the most part, a WCM application tends to collect information but not delete information nearly as often. Although new images are added, even superseding old versions, the version control system still retains the old one and supports reverting to it if needed. Thus the majority of the content we think of as adding to the system is effectively permanently stored. So what is the typical source of 鈥済arbage鈥� in the repository that we might want to clean up?

AEM uses the repository as the storage for several internal and housekeeping activities:

When any of these temporary objects is large enough to require storage in the data store, and when the object eventually passes out of use, the data store record itself remains as 鈥済arbage鈥�. In a typical WCM author/publish application, the largest source of garbage of this type is commonly the process of publish activation. When data is being replicated to Publish, it if first gathered in collections in an efficient data format called 鈥淒urbo鈥� and stored in the repository under /var/replication/data. The data bundles are often larger than the critical size threshold for the data store and therefore wind up stored as data store records. When the replication is complete, the node in /var/replication/data is deleted, but the data store record remains as 鈥済arbage鈥�.

Another source of recoverable garbage is packages. Package data, like everything else, is stored in the repository and thus for packages which are larger than 4KB, in the data store. In the course of a development project or over time while maintaining a system, packages may be built and rebuilt many times, each build resulting in a new data store record, orphaning the previous build鈥檚 record.

How does data store garbage collection work? how-does-data-store-garbage-collection-work

If the repository has been configured with an external data store, data store garbage collection will run automatically as part of the Weekly Maintenance Window. The system administrator can also run data store garbage collection manually on as as-needed basis. In general, it is recommended that data store garbage collection be performed periodically, but that the following factors be taken into account in planning data store garbage collections:

The data store garbage collector first makes a note of the current timestamp when the process begins. The collection is then carried out using a multi-pass mark/sweep pattern algorithm.

In the first phase, the data store garbage collector performs a comprehensive traversal of all the repository content. For each content object that has a reference to a data store record, it located the file in the filesystem, performing a metadata update 鈥� modifying the 鈥渓ast modified鈥� or MTIME attribute. At this point files that are accessed by this phase become newer than the initial baseline timestamp.

In the second phase, the data store garbage collector traverses the physical directory structure of the data store in much the same way as a 鈥渇ind鈥�. It examined the 鈥渓ast modified鈥� or MTIME attribute of the file and makes the following determination:

This approach works well for a single node with a private data store. However the data store may be shared, and if it is this means that potentially active live references to data store records from other repositories are not checked, and active referenced files may be mistakenly removed. It is imperative that the system admin understand the shared nature of the data store before planning any garbage collections, and only use the simple built-in data store garbage collection process when it is known that the data store is not shared.

Running Data Store Garbage Collection running-data-store-garbage-collection

There are three ways of running data store garbage collection, depending on the data store setup on which AEM is running:

If TarMK is being used as both the node store and data store, then Revision Cleanup can be used for garbage collection of both node store and data store. However if an external data store is configured such as File System Data Store, then data store garbage collection must be explicitly triggered separate from Revision Cleanup. Data store garbage collection can be triggered either via the Operations Dashboard or the JMX Console.

The below table shows the data store garbage collection type that needs to be used for all the supported data store deployments in AEM 6:

Running Data Store Garbage Collection via the Operations Dashboard running-data-store-garbage-collection-via-the-operations-dashboard

The built-in Weekly Maintenance Window, available via the Operations Dashboard, contains a built-in task to trigger the Data Store Garbage Collection at 1 am on Sundays.

If you need to run data store garbage collection outside of this time, it can be triggered manually via the Operations Dashboard.

Before running data store garbage collection you should check that no backups are running at the time.

Running Data Store Garbage Collection via the JMX Console running-data-store-garbage-collection-via-the-jmx-console

This section is about manually running data store garbage collection via the JMX Console. If your installation is set up without an external data store, then this does not apply to your installation. Instead see the instructions on how to run Revision cleanup under Maintaining the Repository.

To run garbage collection:

Automating Data Store Garbage Collection automating-data-store-garbage-collection

If possible, data store garbage collection should be run when there is little load on the system, for example, in the morning.

The built-in Weekly Maintenance Window, available via the Operations Dashboard, contains a built-in task to trigger the Data Store Garbage Collection at 1 am on Sundays. You should also check that no backups are running at this time. The start of the maintenance window can be customized via the dashboard as necessary.

If you do not want to run data store garbage collection with the Weekly Maintenance Window in the Operations Dashboard, it can also be automated using the wget or curl HTTP clients. The following is an example of how to automate garbage collection by using curl:

Here is an example curl command to invoke data store garbage collection via the command line:

curl -u admin:admin -X POST --data markOnly=true  https://localhost:4503/system/console/jmx/org.apache.jackrabbit.oak"%"3Aname"%"3Drepository+manager"%"2Ctype"%"3DRepositoryManagement/op/startDataStoreGC/boolean

The curl command returns immediately.

Checking Data Store Consistency checking-data-store-consistency

The data store consistency check will report any data store binaries that are missing but are still referenced. To start a consistency check, follow these steps:

After the consistency check is complete, a message will show the number of binaries reported as missing. If the number is greater than 0, check the error.log for more details on the missing binaries.

Below you will find an example of how the missing binaries are reported in the logs:

11:32:39.673 INFO [main] MarkSweepGarbageCollector.java:600 Consistency check found [1] missing blobs

11:32:39.673 WARN [main] MarkSweepGarbageCollector.java:602 Consistency check failure intheblob store : DataStore backed BlobStore [org.apache.jackrabbit.oak.plugins.blob.datastore.OakFileDataStore], check missing candidates in file /tmp/gcworkdir-1467352959243/gccand-1467352959243