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Table of Contents |
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This document contains the results of testing workflows Check-in/Check-out and Data Import for MARC Bibliographic records in the Quesnelia release with a new MSK instance type where zookeeper instances are not required. The main idea is to see how the kafka.m7g.2xlarge with KRaft mode affects FOLIO performance. Compared results for main workflows with different instance types: kafka.m5.2xlarge against kafka.m7g.2xlarge.
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- Comparing kafka.m5.2xlarge, zookeeper metadata mode against kafka.m7g.2xlarge, KRaft metadata mode
- Resource utilization
Test Runs
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Test #
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Scenario
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Test Results
This table shows results of Check-In/Check-out and Data Import create and update jobs.
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Check-in/Check-out without DI
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- Tests with KRaft mode enabled utilize less CPU resources by brokers ((5% at least, for some broker - 13%) during CI/CO and during DI + CI/CO and at the same time the CPU utilizations are more balanced across the brokers compared to zookeeper mode
- The previous test results * showed that the performance actually the same for m5 and m7g clusters so m7g and m7g KRaft mode should perform the same way as well. Results for comparison **
- Main workflows KPIs do not degrade in tests with Check-in/Check-out with Data import
- No memory leaks. No errors during tests.
- Resource utilization
- Service memory usage doesn't differ in tests with both MSK cluster
- Service CPU utilization in Zookeeper mode: mod-inventory-b - 147%, mod-quick-marc-b - 81%, mod-di-converter-storage-b - 116%, nginx-okapi - 79% the rest of modules utilized less than 50%
- Service CPU utilization in KRaft mode: mod-inventory-b - 133%, mod-quick-marc-b - 75%, mod-di-converter-storage-b - 103%, nginx-okapi - 75% the rest of modules utilized less than 50%
- DB CPU was on level of 90% during tests with both MSK cluster
**Kafka Zookeeper Mode vs KRaft Mode MSK - instance type comparison
Test Runs
Test # | MSK instance type | Scenario | Load level |
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1 | kafka.m5.2xlarge | CICO + DI MARC Bib Create | 8 users + 5K, 25K sequentially |
2 | DI MARC Bib Create | 5K, 25K sequentially | |
3 | CICO + DI MARC Bib Update | 8 users + 5K, 25K sequentially | |
4 | DI MARC Bib Update | 5K, 25K sequentially | |
5 | kafka.m7g.2xlarge | CICO + DI MARC Bib Create | 8 users + 5K, 25K sequentially |
6 | DI MARC Bib Create | 5K, 25K sequentially | |
7 | CICO + DI MARC Bib Update | 8 users + 5K, 25K sequentially | |
8 | DI MARC Bib Update | 5K, 25K sequentially |
Test Results
This table shows results of Check-In/Check-out and Data Import create and update jobs.
MSK instance: kafka.m5.2xlarge, metadata mode - ZooKeeper | |||||
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Job profile | File size | DI Duration without CI/CO | DI Duration with CI/CO | CI with DI Average sec | CO with DI Average sec |
PTF - Create 2 | 5k | 00:03:45 | 00:02:44 | 0.736 | 1.16 |
25k | 00:14:40 | 00:13:36 | 0.787 | 1.176 | |
PTF - Updates Success - 6 | 5k | 00:04:43 | 00:04:18 | 0.764 | 1.153 |
25k | 00:20:21 | 00:21:25 | 0.767 | 1.179 | |
MSK instance: kafka.m7g.2xlarge, metadata mode - KRaft | |||||
Job profile | File size | DI Duration without CI/CO | DI Duration with CI/CO | CI with DI Average sec | CO with DI Average sec |
PTF - Create 2 | 5k | 00:02:49 | 00:02:39 | 0.765 | 1.118 |
25k | 00:13:31 | 00:12:04 | 0.777 | 1.186 | |
PTF - Updates Success - 6 | 5k | 00:04:36 | 00:04:31 | 0.706 | 1.095 |
25k | 00:24:07 | 00:21:50 | 0.74 | 1.16 |
Check-in/Check-out without DI
Scenario | Load level | Request | Response time, sec | Response time, sec | ||
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95 perc | average | 95 perc | average | |||
Circulation Check-in/Check-out (without Data import) | 8 users | Check-in | 0.695 | 0.587 | 0.695 | 0.583 |
Check-out | 1.148 | 0.958 | 1.151 | 0.944 |
Comparison
Data Import durations and Check-In/Check-Out response time comparison
- Data import durations fluctuate within a 10% range of the baseline (tests with Zookeeper metadata mode)
- Response times of CI/CO with Data import do not differ in both MSK clusters
Job Profile | File size | DELTA, DI without CI/CO | DELTA, DI+CI/CO | DELTA, CI with DI | DELTA, CO with DI |
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PTF - Create 2 | 5k | 00:00:56 | 00:00:05 | -0.029 | 0.042 |
25k | 00:01:09 | 00:01:32 | 0.01 | -0.01 | |
PTF - Updates Success - 6 | 5k | 00:00:07 | -00:00:13 | 0.058 | 0.058 |
25k | -00:03:46 | -00:00:25 | 0.027 | 0.019 |
Check-in/Check-out without DI
- Check-in/Check-out perform the same in both MSK clusters. The difference of response times is so small that it can be neglected.
Scenario | Load level | Request | Response time, sec | Response time, sec | Delta | ||
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95 perc | average | 95 perc | average | Average | |||
Circulation Check-in/Check-out (without Data import) | 8 users | Check-in | 0.695 | 0.587 | 0.695 | 0.583 | 0.004 |
Check-out | 1.148 | 0.958 | 1.151 | 0.944 | 0.014 |
MSK resource utilization (CPU)
Load scenario | Brokers | MSK instance: kafka.m5.2xlarge | MSK instance: kafka.m7g.2xlarge | Delta, % |
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CICO | 1 | 13 | 9 | -4 |
2 | 13 | 9 | -4 | |
CICO+DI | 1 | 45 | 32 | -13 |
2 | 34 | 30 | -4 |
Response time
MSK instance: kafka.m5.2xlarge
MSK instance: kafka.m7g.2xlarge
Service CPU Utilization
CPU utilization table
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DI MARC BIB Create and Update + CICO
MSK instance: kafka.m5.2xlarge
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MSK instance:
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kafka.m7g.2xlarge
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Comparison
Data Import durations and Check-In/Check-Out response time comparison
- Data import durations fluctuate within a 10% range of the baseline (tests with Zookeeper metadata mode)
- Response times of CI/CO with Data import do not differ in both MSK clusters
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Check-in/Check-out without DI
- Check-in/Check-out perform the same in both MSK clusters. The difference of response times is so small that it can be neglected.
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Response time, sec
MSK instance: kafka.m5.2xlarge
Response time, sec
MSK instance: kafka.m7g.2xlarge
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Delta
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MSK resource utilization (CPU)
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Response time
MSK instance: kafka.m5.2xlarge
MSK instance: kafka.m7g.2xlarge
Service CPU Utilization
Delta for CPU utilization shows in mod-di-converter-storage-b 20% decrease for update job and 10% decrease for mod-feesfines-b module. The most part of modules CPU utilization deltas fluctuate under 10%.
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title | MSK instance: kafka.m5.2xlarge vs MSK instance: kafka.m7g.2xlarge |
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DI MARC BIB Create and Update + CICO
MSK instance: kafka.m5.2xlarge
MSK instance: kafka.m7g.2xlarge
Service Memory Utilization
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Service Memory Utilization
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MSK instance: kafka.m5.2xlarge
MSK instance: kafka.m7g.2xlarge
DB CPU Utilization
Average DB CPU utilization is 85% 90% during both create jobs and 87% during update jobs for tests with different MSK instance types. DB CPU utilized 15% during Check-In/Check-Out period without DI.
MSK instance: kafka.m5.2xlarge
MSK instance: kafka.m7g.2xlarge
DB Connections
Average connection count is about 850 900 connections for create and 860 connections for update jobs with CI/CO and 730 . 770 connections for CI/CO without data import for tests with different MSK instance types.
MSK instance: kafka.m5.2xlarge
DB load
MSK instance: kafka.m5.2xlarge
Top SQL-queries:
MSK instance: kafka.m7g.2xlarge
Top SQL-queries:
Appendix
Infrastructure
PTF -environment qcp1
- 10 m6i.2xlarge EC2 instances located in US East (N. Virginia)us-east-1
1 database instance, writer
Name Memory GIB vCPUs max_connections db.r6g.xlarge
32 GiB 4 vCPUs 2731 - MSK ptf-mobius-testing2
- 2 m5.2xlarge brokers in 2 zones (total 2 brokers)
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=2
- revision - 2
- metadata mode - ZooKeeper
- Total topics: 1534
- Total partitions: 12155
- MSK ptf-KRaft-mode
- 2 m7g.2xlarge brokers in 2 zones (total 2 brokers)
Apache Kafka version 3.7.x
EBS storage volume per broker 300 GiB
- auto.create.topics.enable=true
- log.retention.minutes=480
- default.replication.factor=3
- revision - 26
- metadata mode - KRaft
- Total topics: 1474
- Total partitions: 11909
Task count for module mod-graphql set to 0 before test start.
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Methodology/Approach
- Populate ptf-mobius-testing2 cluster with topics from tenant cluster
- Run CICO for 2 hours
- After 10 min delay after start of CICO Run DI Create - Export - Update for 5 and 25k
- Run alone Data Imports
- Create new kafka cluster
- Populate NEW cluster with topics from tenant cluster
- Run CICO for 2 hours
- After 10 min delay after start of CICO Run DI Create - Export - Update for 5 and 25k
- Run alone Data Imports
- Compare resource utilization of MSK and main KPI for CICO & DI
Additional/Files
Topics:
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