Reschedule Assets imports running during heavy usage
This recommendation is shown when we’ve detected that Assets imports are running during periods of heavy usage and are likely contributing to high system load and slower page response times.
Signals used
Details of how we detect this issue (known as work item in Cloud).
Conditions
The following conditions need to occur together:
At least one key experience, such as viewing or editing issues, shows degraded page load times (RFU).
During the same time window, one or more Assets imports were running.
Resource usage on at least one node shows high CPU or memory utilization.
Assets imports are identified as CPU-bound, meaning most of their total runtime was spent doing CPU work (for example, parsing, transforming, validating, or computing) rather than waiting on disk, network, or database.
When these conditions are met together, we surface this recommendation and highlight the affected imports.
How we detect it
We use a combination of performance metrics, system events, and Assets import data:
Signals | Details |
|---|---|
RFU degradation | We look for Ready for User (RFU) degradation - cases where key Jira experiences take significantly longer than their historical baseline. This helps us confirm that users are actually experiencing slower page loads, not just one-off spike. |
Assets import activity during degradation | We analyze Assets import events to see whether imports were running during the same time window when RFU was degraded. For each import, we look at its run time, how many objects it processed, and whether it was CPU‑bound. |
Node resource saturation | We look at node‑level CPU and memory metrics to determine whether one or more nodes were saturated. We consider both the intensity of usage and how long it stayed high, to distinguish brief spikes from sustained load. |
Impact of imports vs. no imports | We compare RFU performance in windows with imports to similar windows where fewer or no imports were running. If RFU is noticeably better during windows with fewer imports, this increases our confidence that imports are contributing to the slowdown. |
Filters
When the issue occurs multiple times, we group occurrences under different time filters, for example a 24‑hour or 7‑day period. In this case:
For degraded experiences, we show the highest degradation from all occurrences in the selected time filter.
For identified imports, we show the sum of imports from all occurrences in the selected time filter. Additional data is provided in the CSV file for download.
This helps you see which imports are most likely contributing to high system load.
Frequency chart
Similar to filters, the frequency chart shows how often this issue has occurred over time. This can help you spot patterns such as:
specific days or times when imports regularly run during peak usage
periods where changes to import schedules or configuration align with improved or degraded performance
Recommended actions
How you address this issue will depend on how your imports are set up and when your users are most active. Here are some actions you can take:
Review identified imports
Review the identified imports to see which ones may be contributing to this issue. Pay attention to imports that are CPU‑bound and ran at similar times as the degradation. The CSV file includes additional data, such as import ID, node it ran on, and start and end times.
Reschedule imports to off-peak hours
Reschedule large or frequent imports so they run when fewer users are active. Avoid running multiple heavy imports in parallel during times when your users need normal performance. You can also break very large imports into smaller, more targeted ones. For more best practices, see Installing and configuring Assets.
Monitor for improvements
After you adjust import schedules or configuration, monitor RFU during the next few import runs and check whether page load times improved in the windows where imports previously ran. If the recommendation stops appearing, it’s a strong signal that your changes reduced the impact of imports on performance.
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