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Ticket: 

Jira Legacy
serverSystem Jira
serverId01505d01-b853-3c2e-90f1-ee9b165564fc
keyPERF-756


Summary

  • There is significant performance improvement for in data import performance in Poppy with Poppy using the file splitting feature compared with to Orchid (40% for DI Create, 25% for DI Update) but . However, there is a small degradation (up to 5%) compared to Poppy without the file splitting feature when running with CICO. CO response times are almost the same as identical to Poppy without the file splitting feature. The CI response time is 20% slower with and without Data Import.
  • Average CPU utilization did not exceed 150% for all the modules. The highest consumption was observed from mod-inventory. It was growing from 110% up to 250% at the end of the test (So as memory grows too, we can suspect the issue
    Jira Legacy
    serverSystem Jira
    serverId01505d01-b853-3c2e-90f1-ee9b165564fc
    keyMODINV-944
    . It is fixed in version 20.1.9 but this test was run on version 20.1.7 of mod-inventory). 
    Spikes of mod-data-import were observed in Data Import jobs with 50k files up to 130%. for jobs and a 250% spike for 100k. For Data Import jobs CPU utilization didn't exceed 110% for all other modules
  • Memory utilization increase is a result of previous modules restarting (everyday cluster shutdown process). Memory consumption before tests for mod-search was 45% and for mod-inventory - 55%. During the test with 100k files mod-search memory utilization increases to 90% and mod-inventory up to 100%.
  • Average DB CPU usage during data import is about 95% The same results compared to 95% which is consistent with the performance observed during the same tests in Orchid.
  • Average The average connection count during data import is about approximately 600 connections for Create jobs, which is two times higher than without twice as high as when the file splitting feature is disabled. For Update jobs - 560 connections, the connection count is 560.

Test Runs 

Test #

Scenario

Load level
1DI MARC Bib Create5K, 10K, 25K, 50K, 100K consequentially (with 5 min pause)
CICO 8 users
2DI MARC Bib Update5K, 10K, 25K, 50K, 100K consequentially (with 5 min pause)
CICO 8 users

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Infrastructure

PTF -environment pcp1

  • 10 m6i.2xlarge EC2 instances located in US East (N. Virginia)us-east-1
  • 1 database  instance, writer


    NameMemory GIBvCPUsmax_connections

    db.r6g.xlarge

    32 GiB4 vCPUs2731


  • MSK cluster - 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

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Grafana dashboard:

MARC Bib Create + CICO

MARC Bib Update + CICOhttp://carrier-io.int.folio.ebsco.com/grafana/d/elIt9zCnz/jmeter-performance-with-average-latency?orgId=1&from=1710766047451&to=1710775415290&var-percentile=95&var-test_type=fix_load&var-test=circulation_checkInCheckOut_orchid&var-env=int&var-grouping=1s&var-low_limit=250&var-high_limit=750&var-db_name=jmeter&var-sampler_type=All&var-Request=All