| Jira Legacy | ||||||
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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.
Test Runs
30-min Runs for export instance UUIDs workflow:
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Test
...
Virtual Users
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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
...
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 significant memory on a machine with 8GB RAM to return 50K to 100K records.
- mod-inventory-storage was crashing a few times due to OutOfMemory exception during the test runs
The workflow that retrieves more than 100K records became unresponsive even with 1 user
- The workflow with more than 5 users became unresponsive
- 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 | 105 | 50K~100K instances10K~50K instances |
72. FameFlower | 51 | 30 min | INFO | YesNo | 501 | 10K~50K instances50K~100K instances |
83. FameFlower | 51 | 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.
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Fig 1.3 A side-by-side comparison for the 1 and 5 users tests runs, 10K~50K instances
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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, 50K~100K 10K~50K instances
Fig 1.4 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
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.
The diagram below shows mod-inventory-storage crashed a few times due to OOM. There 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
Fig 3.2: In a 30 minutes test run mod-inventory-storage is shown
4. Disk IO
5. Database CPU 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:
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Percent of total time
...
Average Time,ms
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Calls
...
Query
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32%
...
10,796
...
15
...
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.
The diagram below shows mod-inventory-storage crashed a few times due to OOM. There 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
Fig 3.2: In a 30 minutes test run mod-inventory-storage is shown
4. Disk IO
5. Database CPU Utilization
For 1 user - 30 min run
For 5 users - 30 min run
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
Summary
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The workflow that retrieves more than 100K records became unresponsive even with 1 user
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. 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
Appendix
See Attached FameFlower Performance Test Runs.xlsx for details