Performance Guidelines

Last update: August 15, 2024

Applies to:
Experience Manager 6.5

Topics:
Configuring

CREATED FOR:

Admin

This page provides general guidelines on how to optimize the performance of your AEM deployment. If you are new to AEM, review the following pages before you start reading the performance guidelines:

Illustrated below are the deployment options available for AEM (scroll to view all the options):

AEM

Product

Topology

Operating System

Application Server

JRE

Security

Micro Kernel

Datastore

Indexing

Web Server

Browser

Experience Cloud

Sites

Non-HA

Windows

CQSE

Oracle

LDAP

Tar

Segment

Property

Apache

Edge

Target

Assets

Publish-HA

Solaris™

WebLogic

IBM®

SAML

MongoDB

File

Lucene

IIS

IE

Analytics

Communities

Author-CS

Red Hat®

WebSphere®

HP

Oauth

RDB/Oracle

S3/Azure

Solr

iPlanet

FireFox

Campaign

Forms

Author-Offload

HP-UX

Tomcat

RDB/DB2

MongoDB

Chrome

Social

Mobile

Author-Cluster

IBM® AIX®

JBoss®

RDB/MySQL

RDBMS

Safari

Audience

Multi-site

ASRP

SUSE®

RDB/SQLServer

Assets

Commerce

MSRP

Apple OS

Activation

Dynamic Media

JSRP

Mobile

Brand Portal

J2E

AoD

LiveFyre

Screens

Doc Security

Process Mgt

desktop app

NOTE

The performance guidelines apply mainly to AEM Sites.

When to Use the Performance Guidelines

Use the performance guidelines in the following situations:

First-time deployment: When planning to deploy AEM Sites or Assets for the first time, it is important to understand the options available. Especially when configuring the Micro Kernel, Node Store, and Data Store (compared to the default settings). For example, changing the default settings of the Data Store for TarMK to File Data Store.
Upgrading to a new version: When upgrading to a new version, it is important to understand the performance differences compared to the running environment. For example, upgrading from AEM 6.1 to 6.2, or from AEM 6.0 CRX2 to 6.2 OAK.
Response time is slow: When the selected Nodestore architecture is not meeting your requirements, it is important to understand the performance differences compared to other topology options. For example, deploying TarMK instead of MongoMK, or using a File Data Sore instead of an Amazon S3 or Microsoft® Azure Data Store.
Adding more authors: When the recommended TarMK topology is not meeting the performance requirements and upsizing the Author node has reached the maximum capacity available, understand the performance differences. Compare to using MongoMK with three or more Author nodes. For example, deploying MongoMK instead of TarMK.
Adding more content: When the recommended Data Store architecture is not meeting your requirements, it is important to understand the performance differences compared to other Data Store options. Example: using the Amazon S3 or Microsoft® Azure Data Store instead of a File Data Store.

Introduction

This chapter gives a general overview of the AEM architecture and its most important components. It also provides development guidelines and describes the testing scenarios used in the TarMK and MongoMK benchmark tests.

The AEM Platform

The AEM platform consists of the following components:

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For more information on the AEM platform, see What is AEM.

The AEM Architecture

There are three important building blocks to an AEM deployment. The Author Instance which is used by content authors, editors, and approvers to create and review content. When the content is approved, it is published to a second instance type named the Publish Instance from where it is accessed by the end users. The third building block is the Dispatcher which is a module that handles caching and URL filtering and is installed on the webserver. For additional information about the AEM architecture, see Typical Deployment Scenarios.

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Micro Kernels

Micro Kernels act as persistence managers in AEM. There are three types of Micro Kernels used with AEM: TarMK, MongoDB, and Relational Database (under restricted support). Choosing one to fit your need depends on the purpose of your instance and the deployment type you are considering. For additional information about Micro Kernels, see the Recommended Deployments page.

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Nodestore

In AEM, binary data can be stored independently from content nodes. The location where the binary data is stored is referred to as the Data Store, while the location of the content nodes and properties is called the Node Store.

NOTE

Adobe recommends TarMK to be the default persistence technology used by customers for both the AEM Author and the Publish instances.

CAUTION

The Relational Database Micro Kernel is under restricted support. Contact Adobe Customer Care before using this type of Micro Kernel.

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Data Store

When dealing with large number of binaries, it is recommended that you use an external data store instead of the default node stores to maximize performance. For example, if your project requires many media assets, storing them under the File or Azure/S3 Data Store makes accessing them faster than storing them directly inside a MongoDB.

For further details on the available configuration options, see Configuring Node and Data Stores.

NOTE

Adobe recommends that you choose the option of deploying AEM on Azure or Amazon Web Services (AWS) using Adobe Managed Services. Customers benefit from a team who has the experience and skills of deploying and operating AEM in these cloud computing environments. See additional documentation on Adobe Managed Services.

For recommendations on how to deploy AEM on Azure or AWS, outside of Adobe Managed Services, Adobe recommends working directly with the cloud provider. Or, work with one of Adobe’s partners that support the deployment of AEM in the cloud environment of your choice. The selected cloud provider or partner is responsible for the sizing specifications, design, and implementation of the architecture they are supporting to meet your specific performance, load, scalability, and security requirements.

See also the technical requirements page.

Search

Listed in this section are the custom index providers used with AEM. To know more about indexing, see Oak Queries and Indexing.

NOTE

For most deployments, Adobe recommends using the Lucene Index. Use Solr only for scalability in specialized and complex deployments.

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Development Guidelines

Develop for AEM aiming for performance and scalability. The following are best practices that you can follow:

DO

Apply separation of presentation, logic, and content
Use existing AEM APIs (ex: Sling) and tooling (ex: Replication)
Develop in the context of actual content
Develop for optimum cacheability
Minimize number of saves (ex: by using transient workflows)
Make sure that all HTTP end points are RESTful
Restrict the scope of JCR observation
Be mindful of asynchronous thread

DON’T

Do not use JCR APIs directly, if you can
Do not change /libs, but rather use overlays
Do not use queries wherever possible
Do not use Sling Bindings to get OSGi services in Java™ code, but rather use:
- @Reference in a DS component
- @Inject in a Sling Model
- sling.getService() in a Sightly Use Class
- sling.getService() in a JSP
- a ServiceTracker
- direct access to the OSGi service registry

For further details about developing on AEM, read Developing - The Basics. For additional best practices, see Development Best Practices.

Benchmark Scenarios

NOTE

All the benchmark tests displayed on this page have been performed in a laboratory setting.

The testing scenarios detailed below are used for the benchmark sections of the TarMK, MongoMk, and TarMK vs MongoMk chapters. To see which scenario was used for a particular benchmark test, read the Scenario field from the Technical Specifications table.

Single Product Scenario

AEM Assets:

User interactions: Browse Assets / Search Assets / Download Asset / Read Asset Metadata / Update Asset Metadata / Upload Asset / Run Upload Asset Workflow
Execution mode: concurrent users, single interaction per user

Mix Products Scenario

AEM Sites + Assets:

Sites user interactions: Read Article Page / Read Page / Create Paragraph / Edit Paragraph / Create Content Page / Activate Content Page / Author Search
Assets user interactions: Browse Assets / Search Assets / Download Asset / Read Asset Metadata / Update Asset Metadata / Upload Asset / Run Upload Asset Workflow
Execution mode: concurrent users, mixed interactions per user

Vertical Use Case Scenario

Media:

Read Article Page (27.4%), Read Page (10.9%), Create Session (2.6%), Activate Content Page (1.7%), Create Content Page (0.4%), Create Paragraph (4.3%), Edit Paragraph (0.9%), Image Component (0.9%), Browse Assets (20%), Read Asset Metadata (8.5%), Download Asset (4.2%), Search Asset (0.2%), Update Asset Metadata (2.4%), Upload Asset (1.2%), Browse Project (4.9%), Read Project (6.6%), Project Add Asset (1.2%), Project Add Site (1.2%), Create Project (0.1%), Author Search (0.4%)
Execution mode: concurrent users, mixed interactions per user

TarMK

This chapter gives general performance guidelines for TarMK specifying the minimum architecture requirements and the settings configuration. Benchmark tests are also provided for further clarification.

Adobe recommends TarMK to be the default persistence technology used by customers in all deployment scenarios, for both the AEM Author and Publish instances.

For more information about TarMK, see Deployment Scenarios and Tar Storage.

TarMK Minimum Architecture Guidelines

NOTE

The minimum architecture guidelines presented below are for production environments and high traffic sites. These guidelines are not the minimum specifications to run AEM.

To establish good performance when using TarMK, you should start from the following architecture:

One Author instance
Two Publish instances
Two Dispatchers

Illustrated below are the architecture guidelines for AEM sites and AEM Assets.

NOTE

Binary-less replication should be turned ON if the File Datastore is shared.

Tar Architecture Guidelines for AEM Sites

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Tar Architecture Guidelines for AEM Assets

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TarMK Settings Guideline

For good performance, you should follow the settings guidelines presented below. For instructions on how to change the settings, see Performance Optimization.

Setting

Parameter

Value

Description

Sling Job Queues

queue.maxparallel

Set value to half of the number of CPU cores.

By default the number of concurrent threads per job queue is equal to the number of CPU cores.

Granite Transient Workflow Queue

Max Parallel

Set value to half of the number of CPU cores

JVM parameters

Doak.queryLimitInMemory

Doak.queryLimitReads

Dupdate.limit

Doak.fastQuerySize

500000

100000

250000

True

To prevent expansive queries from overloading the systems, add these JVM parameters in the AEM start script.

Lucene index configuration

CopyOnRead

CopyOnWrite

Prefetch Index Files

Enabled

For more details on the available parameters, see this page.

Data Store = S3 Datastore

maxCachedBinarySize

cacheSizeInMB

1048576 (1 MB) or smaller

2-10% of max heap size

TarMK Performance Benchmark

Technical Specifications

The benchmark tests were performed on the following specifications:

Author Node

Server

Bare metal hardware (HP)

Operating System

Red Hat® Linux®

CPU / Cores

Intel® Xeon® CPU E5-2407 @2.40GHz, 8 cores

RAM

32 GB

Disk

Magnetic

Java™

Oracle JRE Version 8

JVM Heap

16 GB

Product

AEM 6.2

Nodestore

TarMK

Datastore

File DS

Scenario

Single Product: Assets / 30 concurrent threads

Performance Benchmark Results

NOTE

The numbers presented below have been normalized to 1 as the baseline and are not the actual throughput numbers.

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MongoMK

The primary reason for choosing the MongoMK persistence backend over TarMK is to scale the instances horizontally. This ability means having two or more active author instances always running and using MongoDB as the persistence storage system. The need to run more than one author instance results generally from the fact that the CPU and memory capacity of a single server, supporting all concurrent authoring activities, is no longer sustainable.

For more information about TarMK, see Deployment Scenarios and Mongo Storage.

MongoMK Minimum Architecture Guidelines

To establish good performance when using MongoMK, you should start from the following architecture:

Three Author instances
Two Publish instances
Three MongoDB instances
Two Dispatchers

NOTE

In production environments, MongoDB is always used as a replica set with a primary and two secondaries. Reads and writes go to the primary and reads can go to the secondaries. If storage is not available, one of the secondaries can be replaced with an arbiter, but MongoDB replica sets must always be composed of an odd number of instances.

NOTE

Binary-less replication should be turned ON if the File Datastore is shared.

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MongoMK Settings Guidelines

For good performance, you should follow the settings guidelines presented below. For instructions on how to change the settings, see Performance Optimization.

Setting

Parameter

Value (default)

Description

Sling Job Queues

queue.maxparallel

Set value to half of the number of CPU cores.

By default the number of concurrent threads per job queue is equal to the number of CPU cores.

Granite Transient Workflow Queue

Max Parallel

Set value to half of the number of CPU cores.

JVM parameters

Doak.queryLimitInMemory

Doak.queryLimitReads

Dupdate.limit

Doak.fastQuerySize

Doak.mongo.maxQueryTimeMS

500000

100000

250000

True

60000

To prevent expansive queries from overloading the systems, add these JVM parameters in the AEM start script.

Lucene index configuration

CopyOnRead

CopyOnWrite

Prefetch Index Files

Enabled

For more details on available parameters, see this page.

Data Store = S3 Datastore

maxCachedBinarySize

cacheSizeInMB

1048576 (1 MB) or smaller

2-10% of max heap size

MongoMK Performance Benchmark

Technical Specifications

The benchmark tests were performed on the following specifications:

Author node

MongoDB node

Server

Bare metal hardware (HP)

Operating System

Red Hat® Linux®

CPU / Cores

Intel® Xeon® CPU E5-2407 @2.40GHz, 8 cores

RAM

32 GB

Disk

Magnetic - >1k IOPS

Java™

Oracle JRE Version 8

N/A

JVM Heap

16 GB

N/A

Product

AEM 6.2

MongoDB 3.2 WiredTiger

Nodestore

MongoMK

N/A

Datastore

File DS

N/A

Scenario

Single Product: Assets / 30 concurrent threads

Performance Benchmark Results

NOTE

The numbers presented below have been normalized to 1 as the baseline and are not the actual throughput numbers.

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TarMK vs MongoMK

The basic rule to account for when choosing between the two is that TarMK is designed for performance, while MongoMK is used for scalability. Adobe recommends TarMK to be the default persistence technology used by customers in all deployment scenarios, for both the AEM Author and Publish instances.

The primary reason for choosing the MongoMK persistence backend over TarMK is to scale the instances horizontally. This functionality means having two or more active author instances always running and using MongoDB as the persistence storage system. The need to run more than one author instance generally results from the fact that the CPU and memory capacity of a single server, supporting all concurrent authoring activities, is no longer sustainable.

For further details on TarMK vs MongoMK, see Recommended Deployments.

TarMK vs MongoMk Guidelines

Benefits of TarMK

Purpose-built for content management applications
Files are always consistent and can be backed up using any file-based backup tool
Provides a failover mechanism - see Cold Standby for more details
Provides high performance and reliable data storage with minimal operational overhead
Lower TCO (total cost of ownership)

Criteria for choosing MongoMK

Number of named users connected in a day: in the thousands or more
Number of concurrent users: in the hundreds or more
Volume of asset ingestions per day: in hundreds of thousands or more
Volume of page edits per day: in hundreds of thousands or more
Volume of searches per day: in tens of thousands or more

TarMK vs MongoMK Benchmarks

NOTE

The numbers presented below have been normalized to 1 as the baseline and are not actual throughput numbers.

Scenario 1 Technical Specifications

Author OAK Node

MongoDB Node

Server

Bare metal hardware (HP)

Operating System

Red Hat® Linux®

CPU / Cores

Intel(R) Xeon(R) CPU E5-2407 @2.40GHz, 8 cores

RAM

32 GB

Disk

Magnetic - >1k IOPS

Java™

Oracle JRE Version 8

N/A

JVM Heap16GB

16 GB

N/A

Product

AEM 6.2

MongoDB 3.2 WiredTiger

Nodestore

TarMK or MongoMK

N/A

Datastore

File DS

N/A

Scenario

Single Product: Assets / 30 concurrent threads per run

Scenario 1 Performance Benchmark Results

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Scenario 2 Technical Specifications

NOTE

To enable the same number of Authors with MongoDB as with one TarMK system, you need a cluster with two AEM nodes. A four node MongoDB cluster can handle 1.8 times the number of Authors than one TarMK instance. An eight node MongoDB cluster can handle 2.3 times the number of Authors than one TarMK instance.

Author TarMK Node

Author MongoMK Node

MongoDB Node

Server

AWS c3.8xlarge

Operating System

Red Hat® Linux®

CPU / Cores

32

RAM

60 GB

Disk

SSD - 10k IOPS

Java™

Oracle JRE Version 8

N/A

JVM Heap16GB

30 GB

N/A

Product

AEM 6.2

MongoDB 3.2 WiredTiger

Nodestore

TarMK

MongoMK

N/A

Datastore

File DS

N/A

Scenario

Vertical use case: Media / 2000 concurrent threads

Scenario 2 Performance Benchmark Results

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Architecture Scalability Guidelines For AEM Sites and Assets

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Summary of Performance Guidelines

The guidelines presented on this page can be summarized as follows:

TarMK with File Datastore - The recommended architecture for most customers:
- Minimum topology: one Author instance, two Publish instances, two Dispatchers
- Binary-less replication turned on if the File Datastore is shared
MongoMK with File Datastore - The recommended architecture for horizontal scalability of the Author tier:
- Minimum topology: three Author instances, three MongoDB instances, two Publish instances, two Dispatchers
- Binary-less replication turned on if the File Datastore is shared
Nodestore - Stored on the local disk, not a network attached storage (NAS)
When using Amazon S3:
- The Amazon S3 datastore is shared between the Author and Publish tier
- Binary-less replication must be turned on
- Datastore Garbage Collection requires a first run on all Author and Publish nodes, then a second run on Author
Custom index should be created in addition to the out of the box index - Based on most common searches
- Lucene indexes should be used for the custom indexes
Customizing workflow can substantially improve the performance - Remove the video step in the “Update Asset” workflow, disabling listeners which are not used, and so on.

For more details, also read the Recommended Deployments page.

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