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Jira LegacyserverSystem JiraserverId01505d01-b853-3c2e-90f1-

Jira Legacy
serverSystem Jira
serverId01505d01-b853-3c2e-90f1-ee9b165564fc
keyPERF-28

FameFlower Test Results

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Using the Carrier-io framework for capturing and analyzing performance test results, the following tests for the export instance UUIDs workflows were executed.

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for the export instance UUIDs workflows were executed.

Backend:
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mod-inventory-storage-19.1.2
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mod-inventory-14.1.3
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mod-authtoken-2.4.0
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mod-permissions-5.9.0
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okapi-2.38.0

Frontend:
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folio_inventory-

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2.0.2

Folio build was deployed with 50+ ECS services distributed randomly across four m5.large  EC2 instances and the database was created on the db.r5.xlarge AWS RDS instance. Logging level was set to default INFO.

Test Runs

30-min Runs for export instance UUIDs workflow:

Test

Virtual Users

Duration

OKAPI log level

Profiled

Ramp up (total time

in seconds)

Size of response (how many instances

were returned)

1. FameFlower

1

30 min

INFO

No

5

10K~50K instances

2. FameFlower

1

30 min

INFO

No

1

50K~100K instances

3. FameFlower

1

30 min

INFO

Yes

10

10K~50K instances

4. FameFlower

1

30 min

INFO

Yes

10

50K~100K instances

5. FameFlower

5

30 min

INFO

No

50

10K~50K instances

6. FameFlower

5

30 min

INFO

No

10

50K~100K instances

7. FameFlower

5

30 min

INFO

Yes

50

10K~50K instances

8. FameFlower

5

30 min

INFO

Yes

50

50K~100K instances

Results

*All numbers are in milliseconds except for those in the Delta % column, which indicates the difference in percentage going from 1 to 5 users, 10K~50K instances retrieved

1. High level FameFlower results data

1 and 5 users tests runs, 10K~50K instances

1 and 5 users tests runs, 50K~100K instances

Fig 1.1: The chart shows the overall high-level API stats obtained by JMeter calling various APIs in the save instance UUIDs worfklow. It breaks down average response times for 1 and 5 users tests per API call

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. 


Image Added

Fig 1.2: The charts below offer a clearer side-by-side comparison for the 1 and 5 users tests runs, 50K~100K instances

A few things to note:

  • GET_/inventory/instances and GET_/instance-bulk/ids have the slowest response time. Have failed responses even for 1 user 

  • The workflow with more than 100K records become unresponsive became non-responsive even with 1 user

  • The workflow with more than 5 users become unresponsive

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  • became unresponsive at times during the test runs, more on that below.


A side-by-side comparison for the 1 and 5 users tests runs, 50K~100K instancesImage Removed10K~50K instances

Image Added

Fig 1.3 A side-by-side comparison for the 1 and 5 users tests runs, 10K~50K instances


A side-by-side comparison for the 1 and 5 users tests runs, 10K~50K 50K~100K instances

Image Removed

A Image Added

Fig 1.4 side-by-side comparison for the 1 and 5 users tests runs, 50K~100K instances

Image Removed

2.  CPU

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Utilization 

These services for the selected modules were chosen for their activity in the workflow and prominent values compared to other modules.
Data were obtained from the 30-min test runs

Fig 2.1 CPU utilization percentage chart for 1 and 5 users

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3.  Memory trends

Folio build was deployed with 50+ ECS services installed randomly across 4 m5.large instances in the fcp1-pvt cluster and the database was created on the db.r5.xlarge AWS RDS instance. Logging level was set to default INFO.

According to the capacity performance test results, we can say that the saturation point was caused by high CPU utilization on one of four nodes in the fcp1-pvt cluster.

Image Removed

Based on the CPU usage per service we can make a conclusion that the most consuming service was mod-inventory-storage.

The test run with 5 users and 10K~50K instances

Image Removed

The service is 99% of allocated RAM memory.

Image Removed

Image Removed

Image Removed

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test runs retrieving ~10K-50K instances

These services for the selected modules were chosen for their activity in the workflow and prominent values compared to other modules. 


The test run with 5 users and 10K~50K instances

Image Added

Fig 2.2: CPU utilization of various modules running on an EC2 instance, including mod-inventory-storage

Based on this CPU usage chart (Fig 2.2) we can make see that the service consumed the most CPU resources was mod-inventory-storage.

3.  Memory Usage

The following data shows memory consumption of ECS services, most notably of mod-inventory-storage during the 5 users and 10K-50K instances retrieved test run.


Image Added

  Fig 3.1: mod-inventory-storage service consumed 99% of allocated RAM memory in the 5 users and 10K-50K instances retrieved test run.


The diagram below shows mod-inventory-storage crashed a few times due to OOM. There are were 4 instances of mod-inventory-storage active in this test run. This means that it crashed 3 times and spun up new mod-inventory-storage instances

Image Modified

Fig 3.2: In a 30 minutes test run mod-inventory-storage is shown 

4. Disk IO

Image Added

Image Added

5. Database CPU

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Utilization

For 1 user - 30 min run

For 5 users - 30 min run


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6.  Database Slow queries

Slowest queries which took the most of execution time were initiated by the mod-inventory-storage service presented in the following table:

Percent of total time

Average Time,ms

Calls

Query

32%

10,796

15

SELECT jsonb,id FROM fs09000000_mod_inventory_storage.instance WHERE to_tsvector($1, f_unaccent(concat_space_sql(instance.jsonb->>$2 , concat_array_object_values(instance.jsonb->$3,$4) , concat_array_object_values(instance.jsonb->$5,$6)))) @@ (to_tsquery($7, f_unaccent($8))) ORDER BY left(lower(f_unaccent(instance.jsonb->>$9)),$10), lower(f_unaccent(instance.jsonb->>$11))


23%

22,250

5

SELECT jsonb,id FROM fs09000000_mod_inventory_storage.instance WHERE (to_tsvector($1, f_unaccent(concat_space_sql(instance.jsonb->>$2 , concat_array_object_values(instance.jsonb->$3,$4) , concat_array_object_values(instance.jsonb->$5,$6)))) @@ (to_tsquery($7, f_unaccent($8)))) AND (to_tsvector($9, f_unaccent(instance.jsonb->>$10)) @@ replace((to_tsquery($11, f_unaccent($12)))::text, $13, $14)::tsquery) ORDER BY left(lower(f_unaccent(instance.jsonb->>$15)),$16), lower(f_unaccent(instance.jsonb->>$17))


13%

1,709

37

SELECT COUNT(*) FROM (SELECT jsonb,id FROM fs09000000_mod_inventory_storage.instance WHERE to_tsvector($1, f_unaccent(concat_space_sql(instance.jsonb->>$2 , concat_array_object_values(instance.jsonb->$3,$4) , concat_array_object_values(instance.jsonb->$5,$6)))) @@ (to_tsquery($7, f_unaccent($8))) ORDER BY left(lower(f_unaccent(instance.jsonb->>$9)),$10), lower(f_unaccent(instance.jsonb->>$11)) LIMIT $12) x

12%

1,818

34

WITH headrecords AS ( SELECT jsonb, lower(f_unaccent(jsonb->>$1)) AS title FROM fs09000000_mod_inventory_storage.instance WHERE (to_tsvector($2, f_unaccent(concat_space_sql(instance.jsonb->>$3 , concat_array_object_values(instance.jsonb->$4,$5) , concat_array_object_values(instance.jsonb->$6,$7)))) @@ (to_tsquery($8, f_unaccent($9)))) AND left(lower(f_unaccent(jsonb->>$10)),$11) < ( SELECT left(lower(f_unaccent(jsonb->>$12)),$13) FROM fs09000000_mod_inventory_storage.instance ORDER BY left(lower(f_unaccent(jsonb->>'title')),600) OFFSET $14 LIMIT $15 ) ORDER BY left(lower(f_unaccent(jsonb->>$16)),$17) LIMIT $18 OFFSET $19 ), allrecords AS ( SELECT jsonb, lower(f_unaccent(jsonb->>$20)) AS title FROM fs09000000_mod_inventory_storage.instance WHERE (to_tsvector($21, f_unaccent(concat_space_sql(instance.jsonb->>$22 , concat_array_object_values(instance.jsonb->$23,$24) , concat_array_object_values(instance.jsonb->$25,$26)))) @@ (to_tsquery($27, f_unaccent($28)))) AND (SELECT COUNT(*) FROM headrecords) < $29 ) SELECT jsonb, title, $30 AS count FROM headrecords WHERE (SELECT COUNT(*) FROM headrecords) >= $31 UNION (SELECT jsonb, title, (SELECT COUNT(*) FROM allrecords) AS count FROM allrecords ORDER BY title LIMIT $32 OFFSET $33 ) ORDER BY title

4%

2,804

7

SELECT COUNT(*) FROM (SELECT jsonb,id FROM fs09000000_mod_inventory_storage.instance WHERE (to_tsvector($1, f_unaccent(concat_space_sql(instance.jsonb->>$2 , concat_array_object_values(instance.jsonb->$3,$4) , concat_array_object_values(instance.jsonb->$5,$6)))) @@ (to_tsquery($7, f_unaccent($8)))) AND (to_tsvector($9, f_unaccent(instance.jsonb->>$10)) @@ replace((to_tsquery($11, f_unaccent($12)))::text, $13, $14)::tsquery) ORDER BY left(lower(f_unaccent(instance.jsonb->>$15)),$16), lower(f_unaccent(instance.jsonb->>$17)) LIMIT $18) x

3%

1,865

9

EXPLAIN ANALYZE WITH headrecords AS ( SELECT jsonb, lower(f_unaccent(jsonb->>'title')) AS title FROM fs09000000_mod_inventory_storage.instance WHERE (to_tsvector('simple', f_unaccent(concat_space_sql(instance.jsonb->>'title' , concat_array_object_values(instance.jsonb->'contributors','name') , concat_array_object_values(instance.jsonb->'identifiers','value')))) @@ (to_tsquery('simple', f_unaccent('''english''')))) AND left(lower(f_unaccent(jsonb->>'title')),600) < ( SELECT left(lower(f_unaccent(jsonb->>'title')),600) FROM fs09000000_mod_inventory_storage.instance ORDER BY left(lower(f_unaccent(jsonb->>'title')),600) OFFSET 10000 LIMIT 1 ) ORDER BY left(lower(f_unaccent(jsonb->>'title')),600) LIMIT 100 OFFSET 0 ), allrecords AS ( SELECT jsonb, lower(f_unaccent(jsonb->>'title')) AS title FROM fs09000000_mod_inventory_storage.instance WHERE (to_tsvector('simple', f_unaccent(concat_space_sql(instance.jsonb->>'title' , concat_array_object_values(instance.jsonb->'contributors','name') , concat_array_object_values(instance.jsonb->'identifiers','value')))) @@ (to_tsquery('simple', f_unaccent('''english''')))) AND (SELECT COUNT(*) FROM headrecords) < 100 ) SELECT jsonb, title, 0 AS count FROM headrecords WHERE (SELECT COUNT(*) FROM headrecords) >= 100 UNION (SELECT jsonb, title, (SELECT COUNT(*) FROM allrecords) AS count FROM allrecords ORDER BY title LIMIT 100 OFFSET 0 ) ORDER BY title

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7.  Database Missing indexes

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8.

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JVM Profiling result

CPU JVM profiling of the most resources consuming mod-inventory-storage service showed 6 methods which had a high CPU usage and impact on the overall service performance.

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fasterxml.jackson.databind.ObjectMapper.readValue method uses most of CPU capacity which leads to performance degradation


Summary

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Summary       

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  • FOLIO performs better without being profiled when the tests are running 

Issues

  • Most of failed requests were related to GET_/inventory/instances and GET_/instance-bulk/ids that use mod-inventory-storage serviceGET_/inventory/instances and GET_/instance-bulk/ids have failed responses even for 1 user (30 min test run), even in a 1-user 30-min test run.
  • mod-inventory-storage used significant memory on a machine with 8GB RAM to return 50K to 100K records.
  • mod-inventory-storage was crashing a few times due to OutOfMemoryError to OutOfMemory exception during the test runs

  • The workflow with that retrieves more than 100K records become became unresponsive even with 1 user

  • The workflow with more than 5 users become unresponsiveMemory Issues: mod-inventory-storage has noticeable to significant gains in memory used.  more than 5 users became unresponsive
  • fasterxml.jackson.databind.ObjectMapper.readValue method of mod-inventory-storage storage service overuses consumed high CPU resources as there are were a lot of JSON decoding, this implementation could be reviewed and improved to reduce operations with JSONfor optimization possibilities.
  • FOLIO performs better without being profiled when the tests are running 

Appendix

See Attached FameFlower Performance Test Runs.xlsx for details