Understanding the System

A typical DAM setup consists of end users accessing DAM via a load balancer. The DAM instance might be part of a clustered setup, where each DAM instance runs in a Java™ Virtual machine process on either a physical machine or a virtual machine. DAM storage is either provided by a RAID disk if there are single-machine setups, or a managed network-attached storage if there are clustered setups.

The following legend describes the possible performance pitfall areas with some solutions, as appropriate.

Network connection to end user A slow network connection can cause throughput issues, and in some rare cases also latency issues. Sometimes the user has a slow connection from the ISP, especially in intranets. This is a sign of incorrect network topology.

Temporary File System A slow local file system can cause interactive performance problems, especially when searching, because the search indexes are stored on the local disk. It can also cause asset processing problems if the command-line process is being used.

AEM DAM Finder Interactive performance problems, often experienced in searches are caused by high CPU utilization due to many concurrent users or other CPU-consuming processes on the same instance. Moving from virtual machines to dedicated machines and making sure no other services run on the machine can help improving performance. If high CPU load is caused due to asset processing and many concurrent users, Day recommends adding additional cluster nodes.

AEM DAM Workflow Long-running workflow processes during asset ingestion cause asset processing performance problems. Depending on the type of assets being processed, this can indicate CPU over-utilization. Day recommends that you reduce the number of other processes running on the system and to increase the number of available CPUs by adding cluster nodes.

NAS Connectivity Poor network connectivity to the NAS causes interactive performance problems, because accessing new nodes during asset processing is slowed down due to network latency. Also, slow network throughput adversely affects throughput, but also asset processing performance, because loading and saving renditions is slowed down.

Reasons for bad latency and throughput in a NAS are network topology or NAS over-utilization by other services.

Network Attached Storage Over-used network-attached storage systems can cause an array of problems:

  • Low disk space is a frequently encountered trouble that can be prevented through proper sizing of a DAM project.
  • High disk latency propagates into slow access times for CRX and may result in interactive performance problems.
  • Low disk throughput may result in low performance for CQ5 DAM.

Testing for Performance

For every DAM project, be sure to establish a performance testing regime that can identify and resolve bottlenecks quickly. To do so, consider the following checkpoints:

  1. End-to-end performance tests using JMeter - Simulate an example search-and-browse session to detect interactive performance problems.
  2. Throughput and latency tests using JMeter - Running on a client computer ensures that there are no topology-related issues.
  3. Standardized asset processing tests - Ingest a few example assets and measure the time. This should include external workflow integration.
  4. Monitor CPU, Disk, and memory utilization of each cluster node.
  5. CRX read/write performance diagnostics to identify non-processing related issues.
  6. Monitor network latency and throughput from DAM cluster to your NAS.
  7. Test, read, and write performance and disk latency directly on the NAS, if possible.

Tweaking Bottlenecks

The following performance tweaks have been used in projects so far:

  • Selective rendition generation: only generate the renditions that you need by adding conditions to the asset processing workflow, so that more costly renditions are only generated for select assets.
  • Shared data store among instances: when running low on disk space this can considerably reduce the amount of disk space needed at the cost of higher configuration efforts and losing the auto-cleanup of the datastore.

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