Adobe Social Collaboration:

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1 Adobe Social Collaboration: A Deep Dive Into Performance and Scalability Sruthisagar Kasturirangan, Infrastructure Architect, Infrastructure Practice, SapientNitro, Bangalore 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, 213

2 AEM Version AEM 5.6. System Configuration Author & Publish Environments: 8 CPUs Currently (Logical CPUs) 8 CPUs Configured Number of Processors: 2 (Allocated) PowerPC_POWER7 Processor 64 bit Hardware TL2 AIX Kernel Version Memory Size: 8192MB Total Paging Space: 248MB JVM Settings: Maximum Heap Size: 4GB; PermGen: 512MB; IBM J9VM 1.6, GENCON Algorithm Benchmark Architecture SINGLE PUBLISH CONFIGURATION REVERSE REPLICATION AUTHOR NODE USER REQUESTS PUBLISH NODE Test Scenario The 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 15.) #startupdelay is the ramp-up time for starting threads. (For 15 threads, set it to 6 seconds.) #holdloadfor is the time the test is run. (For 1 minutes, set it to 6.) #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. Sapient Corporation, 213

3 Iteration 1 startthreadcount (the total number of concurrent users/threads)=15 startupdelay=6 holdloadfor=12 shutdowntime= requestspersec=2 RPSduration=3 Load Ramp Up Model Expected parallel users count 2 Number of active threads :: :2:6 :4:12 :6:18 :8:24 :1:3 :12:36 :14:42 :16:48 :18:54 :21: Elapsed Time Throughput Throttling Expected RPS 1 Number of requests/sec :: ::3 ::6 ::9 ::12 ::15 ::18 ::21 ::24 ::27 ::3 Elapsed Time Note: This test was run with Ultimate Thread Group by throttling requests per second to 2. Results Sapient Corporation, 213

3 25 2 15 1 5 755 47 144 1341 1644 194 2234 Response Times vs.

4 Response Times vs. Elapsed Time 3 27 Response times in ms add Topic to Publish Node 15 get Topic Page 12 settotaltime :: :4:5 :8:11 :12:17 :16:23 :2:28 :24:34 :28:4 :32:46 :36:51 :4:57 Elapsed Time (granularity: 1 ms) From the graphs above, it is clear that only when the load is throttled in such a way as to limit the (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 2 startthreadcount (the total number of concurrent users/threads)=15 startupdelay=12 holdloadfor=12 shutdowntime= Load Ramp Up Model Expected parallel users count 2 Number of active threads :: :4: :8: :12: :16: :2: Elapsed Time :24: :28: :32: :36: :4: Note: This test was run without Ultimate Thread Group and no throttling was applied Sapient Corporation, 213

Elapsed Time 2 18 16 Response times in ms 14 12 add Topic to Publish Node 1 get Topic Page 8 settotaltime 6 4 2 :: :4:3 :8:6 :12:9 :16:12 :2:15 :24:18 :28:21 :32:24 :36:27 :4:3

5 Results Response Times vs. Elapsed Time Response times in ms add Topic to Publish Node 1 get Topic Page 8 settotaltime :: :4:3 :8:6 :12:9 :16:12 :2:15 :24:18 :28:21 :32:24 :36:27 :4:3 Elapsed Time (granularity: 5 ms) 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 15 users were ramped up, the response times grew to a level that were not within acceptable limits for the page performance. Sapient Corporation, 213

6 Iteration 3 startthreadcount (the total number of concurrent users/threads)=1 startupdelay=1 holdloadfor=6 shutdowntime= Load Ramp Up Model Expected parallel users count 1 Number of active threads :: :1:1 :2:2 :3:3 :4:4 :5:5 :7: :8:1 :9:2 :1:3 :11:4 Elapsed Time Note: This test was run without Ultimate Thread Group and no throttling was applied. Results Sapient Corporation, 213

ms) 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 1 seconds, the moment all 1 users were ramped up, the response times

7 Response Times vs. Elapsed Time Response times in ms 7 6 add Topic to Publish Node 5 get Topic Page 4 settotaltime :: :1:1 :2:21 :3:31 :4:42 :5:53 :7:3 :8:14 :9:25 :1:35 :11:46 Elapsed Time (granularity: 5 ms) 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 1 seconds, the moment all 1 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 1 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 1 concurrent users. Overall System Utilization Publish CPU Total hdadhdcom User% Sys% Wait% 2 1 5:3 5:1 5:2 5: 4:4 4:5 4:3 4:1 4:2 4: 3:4 3:5 3:3 3:1 3:2 3: 2:4 2:5 2:3 2:1 2:2 2: 1:4 1:5 1:3 1:1 1:2 1: :4 :5 :2 :3 :1 : Author CPU Total hdadhdcom User% Sys% Wait% 2 1 5:4 5:3 5:1 5:2 5: 4:4 4:5 4:2 4:3 4:1 4: 3:4 3:5 3:3 3:1 3:2 3: 2:5 2:4 2:3 2:2 2:1 2: 1:4 1:5 1:3 1:2 1:1 1: :4 :5 :2 :3 :1 : Sapient Corporation, 213

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. 2. 3.

8 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: For a total achievable throughput, a single publish and a single author are able to achieve 1.6 within an acceptable response time (those response times below 2 seconds). For a total achievable concurrent user/thread count, a single publish instance is able to handle less than 1 concurrent threads/users performing continuous read operations and updates to maintain response times within SLAs (service-level agreements). 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 CQ Planning and Capacity Guide CQ Hardware Sizing Guidelines guidelines.html Introduction to Adobe s Social Communities ABOUT THE AUTHOR Sruthisagar 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. Sapient Corporation, 213

SOFTWARE PERFORMANCE TESTING TIPS WITH JMETER

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