© Sapient Corporation, 2013
Adobe Social Collaboration: A Deep Dive Into Performance and Scalability
Sruthisagar Kasturirangan, Infrastructure Architect, Infrastructure Practice, SapientNitro, Bangalore
POINT OF view
INTRODUCTION
Adobe’s Social Collaboration unifies all social networking and collaboration applications within AEM (Adobe Experience Manager) and has gained a lot of attention—in part because today’s consumers are increasingly active on various mobile devices and placing a lot of value on feedback from fellow buyers. And smart content and commerce platforms are capitalizing on Social Collaboration to boost sales and give the end user the best experience possible.
In order to understand Adobe’s Social Collaboration better, we dove into a complete analysis of its performance and scalability aspects. We accomplished this by performing tests with Adobe’s provided JMeter scripting framework for running the benchmark tests you’ll see below. The tests include scripts that perform pure write operations so that it’s possible to measure the overall throughput that can be supported in order to eventually arrive at a physical architecture sizing and capacity plan.
Through these tests, we are now able to provide a general guidance on the methodology needed in order to size the infrastructure and identify key bottlenecks when integrating Social Collaboration as part of the overall design of a content and collaboration platform.
This paper has been written not to contend the results provided by Adobe Systems Incorporated in their documentation but to extend the results for virtualized environments due to the influx in development in the arena of cloud hosting. The following results have been elaborately analyzed and discussed before arriving at the conclusions you’re about to read.
ExpERImENTal SETUp
First, let’s briefly go through the experimental setup we used to conduct those benchmark tests, including the AEM version used, the system configuration, the benchmark architecture, and the test scenario.
© Sapient Corporation, 2013
POINT OF view
AEM VersionAEM 5.6.0
System ConfigurationAuthor & Publish Environments:8 – CPUs Currently (Logical CPUs)8 – CPUs ConfiguredNumber of Processors: 2 (Allocated)PowerPC_POWER7 – Processor64 bit – Hardware7.1.2.1 TL02 – AIX Kernel VersionMemory Size: 8192MBTotal Paging Space: 2048MBJVM Settings: Maximum Heap Size: 4GB; PermGen: 512MB; IBM J9VM 1.6, GENCON Algorithm
Benchmark Architecture
Test ScenarioThe tests below were all performed using Adobe’s out-of-the-box application Geometrixx. Adobe’s benchmark scripts have procedures to create multiple users in the author and publish environments so that a realistic test scenario can be created. In this case, a test forum topic was created with a small description. The user was then pre-authenticated during the warm up and, once authenticated, held the session and performed continuous write operations.
ITERaTIONS
The various iterations of testing are tabulated and the details of the load model and results are described in the following sections. In particular, the result sections are focused on analyzing the transactions per second as a function of the total number of transactions and average response times (i.e., time taken for last byte).
Load Model #Generic properties: threads/users.#All timings are in seconds.#startThreadCount is the total number of concurrent threads/users. (For 5 requests per second, set it to 150.)#startupDelay is the ramp-up time for starting threads. (For 150 threads, set it to 60 seconds.)#holdLoadFor is the time the test is run. (For 10 minutes, set it to 600.)#shutdownTime is the time it takes the threads to shut down. (Set it to the same value as startupDelay.)#requestsPerSec is the number of requests per number of seconds.
SINGLE PUBLISH CONFIGURATION
PUBLISH NODEAUTHOR NODE
REVERSE REPLICATION
USER REQUESTS
© Sapient Corporation, 2013
Iteration 1startThreadCount (the total number of concurrent users/threads)=150 startupDelay=60holdLoadFor=1200shutdownTime=0requestsPerSec=2RPSduration=30
Load Ramp Up Model
Throughput Throttling
Note: This test was run with Ultimate Thread Group by throttling requests per second to 2.
Results
POINT OF view
200
180
160
140
120
100
80
60
40
20
000:00:00 00:02:06
Expected parallel users count
00:04:12 00:06:18 00:08:24
Elapsed Time
Num
ber
of a
ctiv
e th
read
s
00:10:30 00:14:42 00:16:48 00:18:54 00:21:0000:12:36
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1.54
1.56
1.58
1.6
1.62
1.64
1.66
1.68
1.7
1.72
TPS
TPS
Transactions
755 1044 1341 1644 1940 2234 470
10
9
8
7
6
5
4
3
2
1
000:00:00 00:00:03
Expected RPS
00:00:06 00:00:09 00:00:12
Elapsed Time
Num
ber
of r
eque
sts/
sec
00:00:15 00:00:21 00:00:24 00:00:27 00:00:3000:00:18
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© Sapient Corporation, 2013
Response Times vs. Elapsed Time
From the graphs above, it is clear that only when the load is throttled in such a way as to limit the TPS (transactions per second) to be around 2 are we able to achieve response times within an acceptable range. Throttling is performed using a JMeter Plugin (Ultimate Thread Group) but this does not indicate the concurrent user sessions.
Therefore, additional testing is required to understand the behaviors associated with these changing user patterns.
Iteration 2startThreadCount (the total number of concurrent users/threads)=150 startupDelay=1200holdLoadFor=1200shutdownTime=0
Load Ramp Up Model
Note: This test was run without Ultimate Thread Group and no throttling was applied
POINT OF view
0
500
1000
1500
2000
2500
3000
755 1044 1341 1644 1940
AVG_RESPONSE_TIME
AVG_RESPONSE_TIME
Transactions
2234 470
30 000
27 000
24 000
21 000
18 000
15 000
12 000
9 000
6 000
3 000
000:00:00 00:04:05 00:08:11 00:12:17 00:16:23
Elapsed Time (granularity: 100 ms)
Res
pons
e tim
es in
ms
00:20:28 00:28:40 00:32:46 00:36:51 00:40:57
add Topic to Publish Node
get Topic Page
setTotalTime
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200
180
160
140
120
100
80
60
40
20
000:00:00 00:04:00
Expected parallel users count
00:08:00 00:12:00 00:16:00Elapsed Time
Num
ber
of a
ctiv
e th
read
s
00:20:00 00:28:00 00:32:00 00:36:0000:24:00
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00:40:00
© Sapient Corporation, 2013
Results
Response Times vs. Elapsed Time
From the graphs above, we can see that the load was not throttled and users were ramped up at the rate of 1 user every 8 seconds. The moment all 150 users were ramped up, the response times grew to a level that were not within acceptable limits for the page performance.
POINT OF view
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
TPS
TPS
Transactions
1223 1971 2748 3454 4192 4953 5736 6432 7166 7935 8734 9500 9882482
0
5000
10000
15000
20000
25000
30000
1223 1971 2748 3454 4192
AVG_RESPONSE_TIME
4953 5736 6432 7166 7935 8734 9500 9882482
AVG_RESPONSE_TIME
Transactions
add Topic to Publish Node
get Topic Page
setTotalTime
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200 000
180 000
160 000
140 000
120 000
100 000
80 000
60 000
40 000
20 000
000:00:00 00:04:03 00:08:06 00:12:09 00:16:12
Elapsed Time (granularity: 500 ms)
Res
pons
e tim
es in
ms
00:20:15 00:28:21 00:32:24 00:36:27 00:40:3000:24:18
© Sapient Corporation, 2013
POINT OF view
Iteration 3startThreadCount (the total number of concurrent users/threads)=10startupDelay=100holdLoadFor=600shutdownTime=0
Load Ramp Up Model
Note: This test was run without Ultimate Thread Group and no throttling was applied.
Results
10
9
8
7
6
5
4
3
2
1
000:00:00 00:01:10
Expected parallel users count
00:02:20 00:03:30 00:04:40
Elapsed Time
Num
ber
of a
ctiv
e th
read
s
00:05:50 00:08:10 00:09:20 00:10:30 00:11:4000:07:00
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2.05
2.1
2.15
2.2
2.25
2.3
2.35
2.4
2.45
2.5
2.55
TPS
TPS
Transactions
946 1429 1774459
3000
3200
3100
3300
3400
3500
3600
3700
946 1429 1774
AVG_RESPONSE_TIME
459
AVG_RESPONSE_TIME
Transactions
© Sapient Corporation, 2013
POINT OF view
Response Times vs. Elapsed Time
From the graphs above, we can see that, since the load was not throttled and users were ramped up at the rate of 1 user every 10 seconds, the moment all 10 users were ramped up, the response times grew to a level that were not within acceptable limits for the page performance.
In this scenario, it did not make any sense to go below 10 concurrent users. And since the average response times were in the order of 3.5 seconds, it was concluded that a single publish server would be able to support less than 10 concurrent users.
OvERall SySTEm UTIlIzaTION
Publish
Author
add Topic to Publish Node
get Topic Page
setTotalTime
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10 000
9 000
8 000
7 000
6 000
5 000
4 000
3 000
2 000
1000
000:00:00 00:01:10 00:02:21 00:03:31 00:04:42
Elapsed Time (granularity: 500 ms)
Res
pons
e tim
es in
ms
00:05:53 00:08:14 00:09:25 00:10:35 00:11:4600:07:03
CPU Total hdadhdcom03 19-7-2013
User% Sys%
0
10
20
30
40
50
60
70
80
90
100
00:0
0
00:1
0
00:2
0
00:3
0
00:4
0
00:5
0
01:0
0
01:1
0
01:2
0
01:3
0
01:4
0
01:5
0
02:0
0
02:1
0
02:2
0
02:3
0
02:4
0
02:5
0
03:0
0
03:1
0
03:2
0
03:3
0
03:4
0
03:5
0
04:0
0
04:1
0
04:2
0
04:3
0
04:4
0
04:5
0
05:0
0
05:1
0
05:2
0
05:3
0
Wait%
0
10
20
30
40
50
60
70
80
90
100
00:0
0
00:1
0
00:2
0
00:3
0
00:4
0
00:5
0
01:0
0
01:1
0
01:2
0
01:3
0
01:4
0
01:5
0
02:0
0
02:1
0
02:2
0
02:3
0
02:4
0
02:5
0
03:0
0
03:1
0
03:2
0
03:3
0
03:4
0
03:5
0
04:0
0
04:1
0
04:2
0
04:3
0
04:4
0
04:5
0
05:0
0
05:1
0
05:2
0
05:3
0
05:4
0
CPU Total hdadhdcom01 19-7-2013
User% Sys% Wait%
© Sapient Corporation, 2013
aBOUT THE aUTHORSruthisagar Kasturirangan is an Infrastructure architect, Infrastructure practice, at SapientNitro Bangalore. a graduate from Iowa State University, he moved on to gain extensive experience within leading IT organizations and eventually moved back to his home country to join Sapient Corporation. He has over 11 years of experience in systems administration of Unix platforms and application Servers such as WebSphere and Weblogic, and intense exposure on capacity planning and performance tuning of Java applications.
POINT OF view
CONClUSION
After conducting this series of tests, and then discussing and analyzing them, we’ve arrived at a few key takeaways that we think are worthwhile to consider:
1. For a total achievable throughput, a single publish and a single author are able to achieve 1.6 TPS within an acceptable response time (those response times below 2 seconds).
2. For a total achievable concurrent user/thread count, a single publish instance is able to handle less than 10 concurrent threads/users performing continuous read operations and updates to maintain response times within SLAs (service-level agreements).
3. Scaling publish servers horizontally, in order to handle higher volumes of updates, is of no value since the bottleneck would lead to reverse replication to the author instance. (Throughput indicated above is for the entire publish layer and not for a single publish layer.)
Adobe’s Social Collaboration can help to achieve social media goals and improve strategy, performance, and scalability. It is our hope that this paper has answered some of your questions and helped you better understand this particular social solution.
References
1. CQ Planning and Capacity Guide http://dev.day.com/docs/en/cq/current/managing/capacity-guide.html
2. CQ Hardware Sizing Guidelines http://wem.help.adobe.com/enterprise/en_US/10-0/wem/managing/hardware_sizing_guidelines.html
3. Introduction to Adobe’s Social Communities http://dev.day.com/docs/en/cq/current/administering/social_communities.html