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FameFlower Test Results

Overview

PERF-28 - Getting issue details... STATUS

Using the Carrier-io framework for capturing and analyzing performance test results, the following tests for the export instance UUIDs workflows were executed.  The slowness depends on the number of records retrieved from the database, so we modeled the JMeter test scripts to make queries for a batch of keywords that return anywhere from 10K to 50K instances, and another batch of keywords that results in between 50K and 100K hits. Since these are resource-intensive operations, we did not test these scenarios with more than 5 concurrent users. 

The tests were ran with 1 and 5 user with queries which return 10K~50K instances and 50K~100K instances

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

Frontend:
- folio_inventory-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.

High Level Summary

  • Most of failed requests were related to GET_/inventory/instances and GET_/instance-bulk/ids that use mod-inventory-storage service, even in a 1-user 30-min test run.
  • mod-inventory-storage used 99% of container memory on a machine with 8GB RAM to return 50K to 100K records.

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

  • The workflow with more than 5 users became non-responsive
  • fasterxml.jackson.databind.ObjectMapper.readValue method of mod-inventory-storage service consumed high CPU resources as there were a lot of JSON decoding, this implementation could be reviewed for optimization possibilities.
  • FOLIO performs better without being profiled when the tests are running 

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

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. 


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 became non-responsive even with 1 user

  • The workflow with more than 5 users 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, 10K~50K instances

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, 50K~100K instances

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

2.  CPU Utilization 

Fig 2.1 CPU utilization percentage chart for 1 and 5 users 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

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.


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



  Fig 3.1: mod-inventory-storage service consumed 99% of allocated RAM memory in the 5 users and 50K-100K instances retrieved test run.

Note that when turning on the profiler on mod-inventory-storage, the performance took a nose dive and mod-inventory-storage crashed a few times in a 30 minutes test run. Obviously this put more stress on the module (and no one would run the profiler in production) but it's consistent with observations in bug fest environment that when returning more records, mod-inventory-storage would experience OOM exception and crashed.  What is seen here is mod-inventory-storage being at the edge of getting an OOM.

4. Disk IO

5. Database CPU Utilization

For 1 user - 30 min run

For 5 users - 30 min run


Database CPU usage was not high, but the queries took a long time to return, see below.

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

7.  Database Missing indexes

8. JVM Profiling result

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.



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


Recommended improvement

MODINVSTOR-500 - Getting issue details... STATUS

MODINVSTOR-501 - Getting issue details... STATUS

MODINVSTOR-502 - Getting issue details... STATUS

Appendix

See Attached FameFlower Performance Test Runs.xlsx for details 


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