Disclaimer: BiWhy is available again for FREE to everyone on biwhy.net
User load usually builds gradually. If something goes wrong, users report it quickly – either as errors or slow refreshes.
Background job load is different. It may stay unnoticed for a long time while quietly consuming CPU, memory, and database resources.
Typical job problems:
too many jobs hitting the system at once
long-running data dumps with little real value – the output is too large to open or too slow to use
scheduled reports that fail after wasting resources
At the same time, jobs are extremely valuable when managed well. Some companies run reporting workloads 24/7 and save end users tons of time.
BiWhy now provides analytics for Future, Running, and History jobs.
Future
Analytics show when load will hit you – by day, day of week, day of month, or hour – and who is generating it, by user, report, or project.
Running
Analytics provide the usual BiWhy view of currently running jobs.
History
Analytics show failure patterns over time – including jobs that never finish successfully, or jobs above a failure rate you define.
In one recent case, I found 611 reports that had never finished successfully in the first four months of 2026.
Together, they accounted for about 7,130 failed instances and 172 days of processing time. The real number is 2–3 times higher, since the system keeps only the last 100 instances.
About 50 of those reports ran daily or even more often. Around 100 had an average runtime above 30 minutes – most in hours, some in days.
And that is only elapsed processing time. In real compute terms, the cost is much higher: those jobs were burning many CPUs in parallel, wasting years of compute and producing exactly ZERO useful outcome. Some of them were also bringing down BOBJ nodes almost daily.
Practical cleanup tips:
100% of sibling instances failing – likely a report issue
Some sibling instances succeed while others fail – likely a schedule-specific issue, such as overly broad prompt values
Frequently running reports – more executions, more waste
Long-running reports – fewer executions, but higher cost per failure
Tidy up your jobs. Spread them properly. Remove the ones nobody needs. Fix the ones that frequently fail.
Use BiWhy to make jobs work for you, not against you.
Future jobs load by time
Failing jobs table
Disclaimer: BiWhy is available again for FREE to everyone on biwhy.netUser load usually builds gradually. If something goes wrong, users report it quickly – either as errors or slow refreshes.Background job load is different. It may stay unnoticed for a long time while quietly consuming CPU, memory, and database resources.Typical job problems:too many jobs hitting the system at oncelong-running data dumps with little real value – the output is too large to open or too slow to usescheduled reports that fail after wasting resourcesAt the same time, jobs are extremely valuable when managed well. Some companies run reporting workloads 24/7 and save end users tons of time.BiWhy now provides analytics for Future, Running, and History jobs.FutureAnalytics show when load will hit you – by day, day of week, day of month, or hour – and who is generating it, by user, report, or project.RunningAnalytics provide the usual BiWhy view of currently running jobs.HistoryAnalytics show failure patterns over time – including jobs that never finish successfully, or jobs above a failure rate you define. In one recent case, I found 611 reports that had never finished successfully in the first four months of 2026.Together, they accounted for about 7,130 failed instances and 172 days of processing time. The real number is 2–3 times higher, since the system keeps only the last 100 instances.About 50 of those reports ran daily or even more often. Around 100 had an average runtime above 30 minutes – most in hours, some in days.And that is only elapsed processing time. In real compute terms, the cost is much higher: those jobs were burning many CPUs in parallel, wasting years of compute and producing exactly ZERO useful outcome.Some of them were also bringing down BOBJ nodes almost daily.Practical cleanup tips:100% of sibling instances failing – likely a report issueSome sibling instances succeed while others fail – likely a schedule-specific issue, such as overly broad prompt valuesFrequently running reports – more executions, more wasteLong-running reports – fewer executions, but higher cost per failureTidy up your jobs. Spread them properly. Remove the ones nobody needs. Fix the ones that frequently fail.Use BiWhy to make jobs work for you, not against you.Future jobs load by timeFailing jobs table Read More Technology Blog Posts by SAP articles
