WORK IN PROGRESS
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In this workflow, we are checking the performance of the renewal of loan items for the Goldenrod release.
We tested it with 1, 2 virtual users for 30 minutes.
Backend:
- mod-users-17.1.0
- mod-users-bl-6.0.0
- mod-authtoken-2.5.1
- mod-permissions-5.11.2
- mod-circulation-19.0.9
- mod-circulation-storage-12.0.1
- okapi-3.1.1
Environment:
- 55 back-end modules deployed in 110 ECS services
- 3 okapi ECS services
- 8 m5.large EC2 instances
- 1 db.r5.xlarge AWS RDS instance
- INFO logging level
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Test | Virtual Users | Duration | OKAPI log level | Profiled | Loans per User |
1. | 1 | 30 min | WARNING | No | 10, 50, 100, 200 |
2. | 2 | 30 min | WARNING | No | 10, 50, 100, 200 |
| 3. | 1 | 30 min | INFO | No | 10, 50, 100, 200 |
Results
Renew all loans by barcode
For 30 minutes Loans Renew all loans (Average) POST_/circulation/renew-by-barcode (Average) 1 user 10 6.52 seconds
585.11 ms
50 31.81 seconds
637.48 ms
100 1.06 minutes 650.15 ms
200 2.09 minutes 673.40 ms
2 users 10 7.42 seconds
683.76 ms
50 32.61 seconds
686.65 ms
100 1.10 minutes
705.37 ms
200 2.27 minutes
747.04 ms
- Below is the dependency graph generated from Giraffe for single POST_/circulation/renew-by-barcode API. It shows individual calls made by renew-by-barcode to gather prerequisite data from different APIs such as item-storage, location-units, loan-types, request-storage, notice-policy, locations, and mod-authtoken. In all, this call took 990 ms, and 6% of that is consumed by mod-authtoken and the remaining 94% of the time is taken by other APIs to gather prerequisite data.
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The database CPU usage increases gradually for 1, 2 users run. At maximum 9% CPU usage for 1 user. For 2 users, max CPU utilization increases and then stabilizes as the number of loans increases, mostly because RDS Postgresql is caching the most frequent query results. Therefore, CPU utilization is relatively on the lower side.
| For 30 minutes | Loans | RDS CPU Utilization |
| 1 user | 10 | 7% |
| 50 | 8% | |
| 100 | 8% | |
| 200 | 9% | |
| 2 users | 10 | 14% |
| 50 | 15% | |
| 100 | 15% | |
| 200 | 15% |
Database queries
| Anchor | ||||
|---|---|---|---|---|
|
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| Query | Total time | Average Time | Total calls made |
|---|---|---|---|
| SELECT count_estimate('SELECT jsonb,id FROM fs09000000_mod_inventory_storage.item WHERE lower(f_unaccent(item.jsonb->>''barcode'')) LIKE lower(f_unaccent(''54357881''))') | 2 minutes or 71% | 14 ms | 9027 |
| SELECT count_estimate('SELECT jsonb FROM fs09000000_mod_patron_blocks.user_summary WHERE (jsonb->>''userId'') = ''4deba809-4fb4-432a-b3df-c54b6ad51d22''') | 0 minutes or 10% | 2 ms | 8457 |
Service CPU Utilization
Service CPU utilization is relatively on a lower side for most modules except for mod-inventory-storagecirculation
| For 30 minutes | Modules | Service CPU Utilization |
| 1 user |
| mod-inventory | 3% | |
| mod-inventory-storage | 10% | |
| mod-circulation |
| 80% |
| mod-circulation-storage |
| 6% | |
| 2 users | mod-inventory |
3.25% |
| mod-inventory-storage |
...
17.75% | |
| mod-circulation | 81.75% |
| mod-circulation-storage | 11.75% |
Service Memory Utilization
Memory was stable throughout the runs, only a spike here or there, but in a 30 minutes run they were consistent.
| For 30 minutes | Modules | Memory CPU Utilization |
| 1 user |
| mod-inventory | 63% | |
| mod-inventory-storage | 56% | |
| mod-circulation |
| 115% |
| mod-circulation-storage |
| 42% | |
| 2 users | mod-inventory |
65% |
| mod-inventory-storage |
56% | |
| mod-circulation | 115% |
| mod- |
| circulation-storage | 49% |
Recommended Improvements
| Anchor | ||||
|---|---|---|---|---|
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- The following JIRA has been created for mod-authtoken optimization:
Jira Legacy server System Jira columns key,summary,type,created,updated,due,assignee,reporter,priority,status,resolution serverId 01505d01-b853-3c2e-90f1-ee9b165564fc key MODAT-80
Appendix
See Attached rtac-test-report.xlsx for details