Page Comparison

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• Kiwi performs much better than Juniper GA, response times for check-ins are <900ms, checkout <1500ms, with not much variation between 1 and 20 users
• Database performance is better and uses much less CPU compared to Juniper GA
• Worst-performing APIs are still POST /checkin-by-barcode and POST /checkout-by-barcode.  The response time are still about 500ms. GET /circulation/loans also takes more than 200ms. GET /inventory/item (by barcode) takes less than 100ms now.
• Longevity test shows response times worsen over time, probably due to the growing DB CPU utilization. 

  Jira Legacy
  server System JiraFOLIO Issue Tracker serverId 01505d016ccf3fe4-b8533301-3c2e368a-90f1983e-ee9b165564fc20c466b11a49 key CIRCSTORE-304

  Potentially could address this situation

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In general there is no regression in performance.  The response times between Kiwi and Juniper are very close to each other for 1-8 users load unless they were in the 95th percentile group or the 20 users load where Kiwi clearly out-perform Juniper.  In the tables below, the Delta columns express the differences between Juniper and Kiwi releases in percentage. Any percentage +/-5% is not statistically is within the margin of error.  It is also noteworthy that Kiwi seems to invoke the GET /automated-patron-blocks 3 times instead of once

Jira Legacy
server System JiraFOLIO Issue Tracker serverId 01505d016ccf3fe4-b8533301-3c2e368a-90f1983e-ee9b165564fc20c466b11a49 key UICHKOUT-755

.  This call averages 25ms under all loads, so if 2 of these 3 calls were not needed (why would the UI calls it thrice?) then the Kiwi's Checkout average response times could improve by another 50ms. 

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Due to the left() function (more details in 

Jira Legacy
server System JiraFOLIO Issue Tracker serverId 01505d016ccf3fe4-b8533301-3c2e368a-90f1983e-ee9b165564fc20c466b11a49 key CIRCSTORE-304

), the indexes were not applied. After removing the left() function, the indexes were applied and when running another longevity test, this time the DB CPU utilization was constant and under 20%, as seen in the diagram below. 

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In the 8-users tests, mod-inventory spiked to almost 400% while averaged around 175%. This is an anomaly that can be disregarded. We have seen mod-inventory spiked while being idle or during a test run of any workflow before, mostly due to DI events_cache topic processing. In other tests this is not a problem, so it does not seem to be a bad trend here. 

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Miscellaneous

• Raw test data: 

  View file
  name CICO-kiwi.xlsx height 250