Scaling AEM (CQ5) Gem Session

Scaling AEM
Michael Marth | Senior Engineering Manager

© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.

Scalability?
§

Performance vs Scalability
§
§

§
§
§

Performance: “it takes X secs to do Y”
Scalability: “it takes X secs to do Y simultaneously Z times”

Yet, performance can help with scalability
This talk is about horizontal scalability (vertical scaling is trivial)
The content is documented at
http://dev.day.com/docs/en/cq/current/deploying/scaling.html


2

Preliminary Notes
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This talk is about pre-Oak scalability patterns, i.e. the CQ5.x line
§

§

with AEM 6 these patterns will change

CRX2 Cluster is mostly a technology for reliability, not for scalability
§

Slight exception is read scalability


3

Scaling AEM
1. High Volume and High Performance Delivery
2. High Frequency Input Feed
3. High Processing Input Feed
4. High Volume Input Feed
5. Many Editors
6. Geo-distributed Editors
7. Many DAM assets
8. Geo-distributed disaster recovery


4

High Volume and High Performance Delivery - Description
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Use Case:
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High traﬃc site (100m impressions/d)

§

Examples: adobe.com

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Limiting factor
§

CPU on publish


5

High Volume and High Performance Delivery - Solution Pattern
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Leverage dispatcher caching as much as possible
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§

SSI and/or client-side for personalized content

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§

in latest dispatcher: single-page dispatcher flush and scripted flushing, use to cache/flush
content in dispatcher

Selectors for query caching

CDN with short TTL

P

Disp

round robin

Load
Balancer

P

Disp


6

CDN

Client

High Volume and High Performance Delivery
§

Notes
§

Related to rendering performance, see also
§

§

§

CQ performance patterns (use CQ timing component, prefer tree walking over JCR queries,
use ClientLibraryManager to concat and minify JS, etc, see [1])
Generic performance patterns (reduce requests with e.g. css sprites, gzip responses, put JS
calls at bottom of HTML, etc, see [2])

Anti-Pattern
§

Adding publishers before leveraging caching

[1] http://dev.day.com/docs/en/cq/current/deploying/performance.html
[2] http://shop.oreilly.com/product/9780596529307.do

7

High Frequency Input Feed - Description
§

Use Case: news feed import (moderate amounts, but constant updates)

§

Limiting factor
§

Dispatcher cache invalidation
§

Therefore actual limiting factor is CPU on publish


8

High Frequency Input Feed - Solution Pattern 1
§

Set up content structure so that other pages do not get invalidated on dispatcher
cache
§
§

§

if possible: highly volatile content e.g. in /etc
with latest dispatcher: single-page flush possible

Separate replication queue (so that main queue is not blocked)

Backend

import
process

editing

A


replication

P

9

flush

Disp

High Frequency Input Feed - Solution Pattern 2
§

Set up content structure so that other pages do not get invalidated on dispatcher
cache
§

§

as previous pattern

Import directly into Publish (no replication necessary)

Backend

Backend

import
process

A


import
process

P

flush

Disp

High Frequency Input Feed
§

Questions to ask
§

Human filtering/processing needed? Then imports should be on author and
replicated.
§

If no: is the use case OK with diﬀerent states on publish?
§

if yes: no replication needed, then pattern 2 is preferable


11

High Processing Input Feed - Description
§

Use Case:
§

DAM import of images
§
§

happens regularly

§

§

1000 images at once

other editors are editing content at the same time

Limiting factor
§

CPU, memory on author


12

High Processing Input Feed - Solution Pattern 1
§

Separate processing instances from human editing instances
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Offload 1 Workflow step, e.g. thumbnail generation from PSDs

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There can be more than 1 processing instance

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Replicate back and forth in packages if possible
§

CQ5.6.1: share DS between instances and replicate without binary, offloading framework

Processing
Author

Processing
Author

workflow steps
running, replicating results back

offload CPU-intensive
WF steps
by replication
editing


A

workflows
running

13

High Processing Input Feed - Solution Pattern 2
§

Separate pre-processing instances for uploading
§

There can be more than 1 pre-processing instance
§

CQ5.6.1: share DS between instances and replicate without binary

workflows
running
image
upload

Processing
Author

replication

editing


A

14

High Processing Input Feed
§

Notes
§

Author cluster can help mitigate the problem, but editors must edit content on slave

§

Throttling WFs or execution during night can help mitigate the problem

§

If the import is limited by CPU needed image conversion consider using ImageMagick
rather than Java


15

High Volume Input Feed - Description
§

Use Case:
§

Product data import
§

§

1 million products, 10000 modifications/day

Limiting factor
§

Writing to the repository
§

§

reads are also blocked

Potentially (to a lesser degree) in case repository scans are needed to create diﬀs:
§

CPU for calculating diﬀs

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Repository read caches get flushed


16

High Volume Input Feed - Solution Pattern
§

Separate import instance to process imports, partition if possible
§

§

only useful if import requires significant CPU (e.g. no diff delivered)

Replicate to author
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Replicate as package
§

§

CQ5.6.1: share DS between instances and replicate without binary

Replication to publish as package if possible
import into
repository
Backend

import
process

Import
Processing
Author

replication of diff

editing


A

17

replication
of diff

P

High Volume Input Feed
§

Questions to ask
§

§

Can the import be throttled? Most problems get much less severe.

Notes
§

Use batch saves (1000 nodes) on import (reduces overhead in indexing, etc and
speeds up the import overall)

§

Import as nt:unstructured rather cq:Page if possible
§

§

If not: switch oﬀ heavy listeners (e.g. ContentSync) or use the JcrObservationThrottle

Anti-Pattern
§

Usage of network disc (usually have high latency)

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Replicating to publish through same replication queue as editorial content
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§

Might heavily delay important editorial changes from publication

Using PageManager (cannot operate in batch mode, unless you use 5.6.1 where that
option was added)


18

Many Editors - Description
§

Use Case:
§
§

§

News or media portal
>50 editors editing content concurrently

Limiting factor
§

Depends on what do the editors actually do, eg. MSM rollouts, starting WFs, but
usually CPU-bound


19

Many Editors - Solution Pattern 1
§

Sharding: split up diﬀerent web sites / parts of web sites onto separate author
instances

§

Publish instances are shared

editing

A

replication

site 1

editing

A

P

site 2

editing

A

P

site 3


P

20

Many Editors - Solution Pattern 2
§

Sharding: split up diﬀerent web sites into separate author instances, but replicate into
one main author, e.g. for shared workflow processes
§
§

§

Practical if the shards do not need to share content.
Cross-replication can be done, but will be hard to keep consistent

Publish instances are shared
A

editing

site 1

A

editing

replication

site 2

A

editing

site 3


21

A

P

Many Editors
§

Questions to ask
§

§

What do the editors actually do?

Notes
§

Author dispatcher helps to reduce CPU load on author instances

§

Author CRX cluster instead of sharding will mitigate the problem if CPU-bound


22

Geo-distributed Editors
§

Use Case:
§

§

Editors located in diﬀerent geos (US, EMEA, APAC)

Limiting factor
§

Bandwidth between editor location and author server location


23

Geo-distributed Editors - Solution Pattern
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Use Dispatcher in front of Author

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Guiding principle: limit traffic between Dispatcher and editor location.
§

gzip traffic

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Use Client Library Manager to minimize traffic
§

§

minify, concat and gzip all client libraries

Cache all responses that are not under /content in
§

Editor’s browser cache

§

Potentially also dispatcher cache

cache

cache
minimize requests
gzip responses


Disp

24

A

Geo-distributed Editors
§

Notes
§

In extreme cases consider writing templates that treat author renditions diﬀerently from
publish renditions (especially reducing the number of necessary requests, e.g. by dropping
requests to tracking servers, external CSS, etc)

§

Or use Scaﬀolding for editing


25

Many DAM Assets
§

Use Case:
§

§

Many assets (>5Mio) in DAM

Limiting factor
§

Disc space


26

Many DAM Assets - Solution Pattern
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Split physical storage of data store and repository tar files
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§

for data store high latency is acceptable

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§

tar files need disc with very low latency

Locate data store on cheap discs remotely (NAS, S3)

Share data store between instances
§

In 5.6.1: use binary-less replication in case of shared DS to minimize network traﬃc

Repository

tar

local

DS

S3
27

Many DAM Assets
§

Notes
§

In case of shared DS: the DS garbage collection needs to be run on an instance that keeps
references to all assets in DS

§

In 5.6.1: huge performance improvements (~10x or more) for DS GC when the persistence is
tar-based


28

Geo-distributed disaster recovery
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Use Case:
§
§

§

Data centers located in diﬀerent geos
One DC shall act as failover for author

Limiting factor
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Latency between DCs (in very low latency cases CRX clustering could be used)


29

Geo-distributed disaster recovery - Solution Pattern
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Use file level tools like rysnc to create replicas in 2nd DC

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Hourly: sync data store
§
§

§

This is usually the most time consuming part
Sync can be performed anytime, due to add-only data store architecture

Nightly:
§

Create incremental backup into filesystem on 1st DC to get consistent state of files

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Rsync backup to 2nd DC. For that period CQ on 2nd DC must not be running.


30

Scaling AEM (CQ5) Gem Session

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