Overview

Bulk Edit - Establish a performance baseline for user status bulk updates. 

• How long does it take to export 100, 1000, 5000, 10k, and 100K records?
• Use it for up to 5 concurrent users.  
• Look for a memory trend and CPU usage
• Pay attention to any RTR token expiration messages and observe how/if BE is affected by expiring tokens. If needed set the Access token's expiration time to 300s or less to trigger the Access token's quick expiration. 

Summary 

• All tests were successful, and 100K records files after bulk-edit were downloaded(test completed successfully). System has not reached maximum capacity therefore, the number of VU can be increased to 7.
• No errors with messages like "Invalid token" or messages like "Access token has expired" during 2 tests.
• With an increase in the number of virtual users, the operating time of bulk-edit increases(All results are in the first table).
• Comparing the test results on Poppy and Orchid releases, we can conclude that the processing time increased by up to 10%(However, the previous report lacked data on the duration of Bulk-edit ).
• The system was stable during the test. Maximal resource utilization was during 100K records bulk-edit and 5 VU concurrently.
  
  • Max CPU utilization was on nginx-okapi(60%) and okapi(50%) services. 
  • Service memory usage was without memory leaks. Except the mod-search service during Test 2.
  • Average DB CPU usage was about 63%
  • Number of DB connections ~ 200
• Comparing the resource utilization graphs, we can say that the system behavior on the Poppy release is the same as on Orchid.
• From Orchid JIRAS "The high CPU usage of mod-users (up to 125% ) needs to be investigated."  During 2 tests max CPU consumption was about 40% for mod-users service.
  
  

  Recommendations & Jiras
  
  

• The high memory usage of mod-search service during test 2, needs to be investigated
  
  

Results

Total processing time of upload and edit - commit changes. Units =hours:minutes: seconds

Comparison table of bulk-edit process duration on Poppy and Orchid releases

Resource utilization

Instance CPU Utilization

Test 1. Bulk-edit 5 consecutive runs of 100K records, starting from 1VU up to 5VU. From the Instance CPU utilization graph, you can see that the bulk-edit process consists of 2 parts: file uploading(90-120 minutes) and records processing.
*During the Bulk-edit for 4 VU, one process was uploading 100K records file for more than 4 hours( jobs finished processing at about 21:30, and the grey graph was running up to 2 a.m.), but on all other bulk-edits jobs, there were no problems with this file.

Test 2.   Bulk-edit 100-1000-5000-10k records successively from 1VU up to 5VU. Blurred areas contain errors from the load generator.

Memory usage

Test 1. Bulk-edit 5 consecutive runs of 100K records, starting from 1VU up to 5VU. 

Test 2.   Bulk-edit 100-1000-5000-10k records successively from 1VU up to 5VU

Service CPU usage

Test 1. Bulk-edit 5 consecutive runs of 100K records, starting from 1VU up to 5VU. 

Test 2.   Bulk-edit 100-1000-5000-10k records successively from 1VU up to 5VU

RDS CPU utilization

Test 1. Bulk-edit 5 consecutive runs of 100K records, starting from 1VU up to 5VU.  Average CPU for 1VU ~ 32%; 2VU ~ 43%; 3VU ~ 50%; 4VU ~ 52%; 5VU ~ 63%; 

Test 2.   Bulk-edit 100-1000-5000-10k records successively from 1VU up to 5VU. Average CPU for 1VU ~ 40%; 2VU ~ 51%; 3VU ~ 53%; 4VU ~ 62%; 5VU ~ 80%; 

Database connections

Test 1. Bulk-edit 5 consecutive runs of 100K records, starting from 1VU up to 5VU. The number of connections to the database did not exceed 200

Test 2.   Bulk-edit 100-1000-5000-10k records successively from 1VU up to 5VU. The number of connections to the database did not exceed 200

Database load 

Part of test 1. Bulk-edit 100K records 5VU. 

TOP SQL Queries

Appendix

Infrastructure

PTF -environment pcp1

• 11 m6g.2xlarge EC2 instances located in US East (N. Virginia)us-east-1
• 2 instances of db.r6.xlarge database instances, one reader, and one writer
• MSK tenant
  
  • 4 m5.2xlarge brokers in 2 zones

  • Apache Kafka version 2.8.0

  • EBS storage volume per broker 300 GiB

  • auto.create.topics.enable=true
  • log.retention.minutes=480
  • default.replication.factor=3

Modules memory and CPU parameters

Methodology/Approach

1. Item records update:
   1. Upload file with item barcodes 
   2. Click the Start bulk edit option in the Action menu and make the following changes:
      1. Set Temporary location to Clear field
      2. Set Permanent location to < to the value available on test environment>
      3. Set Status to Unknown
      4. Set Temporary loan type to Clear field
      5. Set Permanent loan type to < to the value available on test environment>
      6. Add Administrative note by adding text: "This is a new administrative note"
      7. Add Action note by adding text: "This is a new action note"
      8. Suppress from discovery (set the value to true)
   3. Confirm the changes
   4. Commit the changes
   5. Verify the changes are correct
   6. Download the file with updated records
   7. Download the file with errors (if applicable)

Test 1: Was run manually from UI run Bulk-edit job with the configuration above. Run up to 5 concurrent processes in new browser tabs. Check if after the update files are downloaded successfully. 

Test 2. Was run from the Jmeter script. The configuration above was added to the POST method to run bulk-edit with proper configuration. Tests were run for 100-1000-5000-10k records successively from 1VU up to 5VU