Overview In Progress

The purpose of the document is getting results of testing Data Import Create MARC holdings records and to detect performance trends in Quesnelia in scope of ticket  PERF-833 - Getting issue details... STATUS

Compared with results in previous test report: Data Import Create MARC holdings records [ECS] [Quesnelia]

Summary

• Data import create holdings jobs perform faster in Quesnelia release about 40% in Average comparing with Poppy.

Recommendations & Jiras

Errors

Test Runs 

Set 1 - Files used to test DI create Holdings had 1 instance HRID for all created Holdings

Set 2 - Files used to test DI create Holdings had 1 unique instance HRID for every 1000 created Holdings (new approach)

Comparison

Compared with results in previous test report: Data Import Create MARC holdings records [non-ECS] [Poppy]

Service CPU Utilization

Memory Utilization

MSK tenant cluster

Disk usage by broker

CPU (User) usage by broker

RDS CPU Utilization

For all tests - 95% except of DI Holdings with 1k file - 25%.

DB Connections

DB connections - 860.

DB Load

Set #1

Set #2

SQL queries

INSERT INTO fs09000000_mod_source_record_manager.events_processed (handler_id, event_id) VALUES ($1, $2)
INSERT INTO fs09000000_mod_source_record_manager.journal_records (id, job_execution_id, source_id, source_record_order, entity_type, entity_id, entity_hrid, action_type, action_status, error, action_date, title, instance_id, holdings_id, order_id, permanent_location_id, tenant_id) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, $15, $16, $17)
UPDATE fs09000000_mod_source_record_manager.job_execution_progress SET succeeded_records_count = succeeded_records_count + $2, error_records_count = error_records_count + $3 WHERE job_execution_id = $1 Returning *
INSERT INTO fs09000000_mod_inventory_storage.holdings_record (id, jsonb) VALUES ($1, $2) RETURNING jsonb
insert into "marc_records_lb" ("id", "content") values (cast($1 as uuid), cast($2 as jsonb)) on conflict ("id") do update set "content" = cast($3 as jsonb)

WITH input_rows(record_id, holdings_id) AS (
   VALUES ($1::uuid,$2::uuid)
)
, ins AS (
   INSERT INTO fs09000000_mod_inventory.records_holdings(record_id, holdings_id)
   SELECT * FROM input_rows
   ON CONFLICT (record_id) DO UPDATE SET record_id=EXCLUDED.record_id
   RETURNING record_id::uuid, holdings_id::uuid
   )
SELECT record_id, holdings_id
FROM   ins
UNION  ALL
SELECT c.record_id, c.holdings_id 
FROM   input_rows
JOIN   fs09000000_mod_inventory.records_holdings c USING (record_id);

INSERT INTO fs09000000_mod_source_record_manager.journal_records (id, job_execution_id, source_id, source_record_order, entity_type, entity_id, entity_hrid, action_type, action_status, error, action_date, title, instance_id, holdings_id, order_id, permanent_location_id, tenant_id) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, $15, $16, $17)

INSERT INTO fs09000000_mod_source_record_manager.events_processed (handler_id, event_id) VALUES ($1, $2)

UPDATE fs09000000_mod_source_record_manager.job_execution_progress SET succeeded_records_count = succeeded_records_count + $2, error_records_count = error_records_count + $3 WHERE job_execution_id = $1 Returning *

INSERT INTO fs09000000_mod_inventory_storage.holdings_record (id, jsonb) VALUES ($1, $2) RETURNING jsonb

WITH input_rows(record_id, holdings_id) AS (
   VALUES ($1::uuid,$2::uuid)
)
, ins AS (
   INSERT INTO fs09000000_mod_inventory.records_holdings(record_id, holdings_id)
   SELECT * FROM input_rows
   ON CONFLICT (record_id) DO UPDATE SET record_id=EXCLUDED.record_id
   RETURNING record_id::uuid, holdings_id::uuid
   )
SELECT record_id, holdings_id
FROM   ins
UNION  ALL
SELECT c.record_id, c.holdings_id 
FROM   input_rows
JOIN   fs09000000_mod_inventory.records_holdings c USING (record_id);

Infrastructure

PTF - environment qcp1

• 10 m6g.2xlarge EC2 instances located in US East (N. Virginia)us-east-1
• 1 database  instances, writer

• MSK tenant
  • 2 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=2

Methodology/Approach

1. Prepare Data Import Files 1k, 5k, 10k, 80k with defined number of holding records associated with instance HRID (1 instance HRID for all records or 1 per 1000 records)
   
   1. replace instance HRID field with active one from the environment (example: =004 colin00001144043)
   2. replace location field (example =852 01$bme3CC$hKFN5860.A6$iC732) where me3CC - the code of tenant location. Go to /settings/tenant-settings/location-locations and take the code of the location with active status
   3. to replace the field 004 - extract instance HRIDs of active instances for this tenant. Use sql query below

      1. Get total jobs durations

         SQL to get job durations

         select file_name,total_records_in_file,started_date,completed_date, completed_date - started_date as duration ,status,error_status
         from [tenant]_mod_source_record_manager.job_execution
         where subordination_type = 'COMPOSITE_PARENT'
         -- where started_date > '2024-06-13 14:47:54' and completed_date < '2024-06-13 19:01:50.832' 
         order by started_date desc 
         limit 10
         

      2. Get instance HRID ids

         SQL to get instance HRIDs

         select jsonb->>'hrid' as instanceHRID
         from [tenant]_mod_inventory_storage.instance
         where jsonb->>'discoverySuppress' = 'false' and jsonb->>'source' = 'MARC'
         limit 80

      3. Put instance HRID ids into stringsHRID.txt file without double quotes and headers. Every row should contain only HRID id
      4. Use PY script to replace HRID ids in mrc file if needed. Script is located in Git repository perf-testing\workflows-scripts\data-import\Holdings\Data_preparation_steps 
2. Run Data Import sequentially one by one from the UI with 5 min delay (delay time can vary - this time defined as comfortable to get results).