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1. In this workflow, we are checking the performance of exporting MARC Bib records workflow (default profile - does not involve items and holdings) running in the Honeysuckle release -

   Jira Legacy
   server System JiraJIRA columnIds issuekey,summary,issuetype,created,updated,duedate,assignee,reporter,priority,status,resolution columns key,summary,type,created,updated,due,assignee,reporter,priority,status,resolution serverId 01505d01-b853-3c2e-90f1-ee9b165564fc key PERF-134

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      2. Check how many jobs can run in parallel -

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1. As the number of instances to export increases from 1 - 500,000 for 1, 2 users, performance improves. For all cases, mod-data-export, mod-source-record-storage is stable and performant.
2. data-export/job-executions API is polled every 3 seconds vs 5 seconds in Goldenrod. This means there are more requests overall but it also improves data-export execution. Data-export job completes faster(around 2 seconds) compared to the previous release. Other than that, data-export is stable and see performance improvements compared to the Goldenrod release.
3. A new feature added for data-export is it will kill failed job every 6 hours
4. How many data-export jobs can run in parallel? Multiple jobs can run in parallel but and 2.75 Million instance records can be exported at any given point in time. If we try to export more than that for example 3 Million instance records, mod-data-export crashes with OOM(Out Of Memory)

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Check how many jobs can run in parallel

Multiple jobs can run in parallel but data-export fails if trying to export 3 Million instance records with the below configuration.

Current memory allocation to mod-data-export service in ECS task definition container:
Soft memory limit - 360 MB
Hard memory limit - 512 MB

Memory Utilization gradually increases from 101% to 141% as we increase the number of instance records where it eventually crashes.

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