aem asset optimizations & best practices

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AEM Asset Optimizations & Best PracticesKanika Gera| Support Engineer


Agenda

Asset Capabilities Asset Performance

Optimizations Common Asset

Architectures Assets Sizing Q&A

2


AEM Assets CapabilitiesPower of Assets


Reasons for Performance Bottlenecks

5


Assets Performance Optimizations Tuning your assets for maximizing performance


Configurations for Maximizing Asset Performance Enable HTTPS to get around any corporate HTTP traffic sniffers Deploy on Java 8. Set optimal JVM parameters Customize DAM update Asset workflow:

Transient WorkflowSelective Rendition Generation

Disabling XMP Write back (if not required ) Set the DAM Update workflows to transient

Configure a File system DataStore or an S3 DataStore

-XX:+UseConcMarkSweepGC-Doak.queryLimitInMemory=500000-Doak.queryLimitReads=100000-Dupdate.limit=250000-Doak.fastQuerySize=true


Configurations for Maximizing Asset Performance Set the Maximum Parallel Jobs to half of the available processors, that is tune Granite workflow queue to limit the concurrent jobs

Offloading Dam Update Asset Workflows to a second Author Instance with Binary less replication with a FP in 6.1 and on vanilla 6.2

Create Custom Indexes and tune them for queries you run often

Configure Workflow & Version purging


Common Asset ArchitecturesCommon Asset use cases & their Solution


Asset Ingestion, High ProcessingUse Case: When authors perform bulk imports, such as 1,000 images at a time. CPU and Memory are critical.

Solution: Offload jobs to a farm of Experience Manager worker instances.Dedicate processing instances for asset ingestion


Asset Ingestion, High VolumeUse Case: Database of one million products that has 10,000 modifications per day. The repository becomes the bottleneck. While writes are happening, reads are blocked for consistency purposes.

Solution: Segregate the import process on a dedicated author instance with its own repository. At completion, replicate a full diff/delta to the author environment, with chained replication to the publish environment, if necessary.


Large DAM repositoriesUse Case: Huge repositories, such as over 5 million assets, 10 million nodes, and 10TB disk size

Solution: Split the persistent store and the data store (optimized for handling large binaries) onto different media. The persistent store requires very low latency I/O, hence local storage works best. For the data store, a higher latency is acceptable.


Many Concurrent AuthorsUse Case: Concurrent authors are users who are actively working on the system.

Solution: Spin off each project into a separate author instance or environment in which the work in progress takes place. This technique is named content partitioning or sharding


Asset SizingSize your Assets


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15

Asset Sizing

• Sizing Repository

• Shared Data stores

• Maximum Number of Assets

• Size of Assets


Questions & Answers


THANK YOU !!

aem asset optimizations & best practices

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