Before attempting any solution please check the Insight system requirements

In some rare cases the CPU utilization can reach over 90 - 95% when Insight is enabled and might lead to Jira crashing. This can be due to different reasons we will try to list some of them here

  1. Insight does not have enough dedicated power; check the system requirement link above and note that these numbers are measured only for insight to run smoothly. More add-ons with scheduling tasks means you should go above these numbers.
  2. Insight gadgets in dashboards; many users having many insight gadgets in place will cause high CPU utilization as the gadgets send REST request (each individually) to Insight. Which causes the JVM to be fully utilized. We recommend setting up an shared filter or shared reports instead.
  3.  Too many automation rules running at the same time; Setting up a long idle interval can help in that case.
  4. The GC overhead is exceeded ; This can be fixed through setting up garbage collection settings in Jira
  5. In Data center environments the database gets filled up very quickly; Causing the DB to go out of memory. This relates to how the Jira API handles messages in the clustermessage table in order to replicate the Insight data across all nodes. Click hear to learn how to solve this issue
  6. The database connections are not enough; Increasing Insight Parallelism might require increasing the database pool
  7. Importing too many objects in one go; Try to filter the objects you are importing with IQL (Filter Data source using IQL) or only syncing one object type at a time. This depends on how many objects you have loaded in the system and how many objects you are importing from the data source.
  8.  Exporting an object schema with a huge number of objects and/or a lot of attachments can cause the server to go out of memory; We recommend using Object schema import type instead with the same recommendation from number 7.
  9. Having too many indexed object attributes in the memory, You can turn turn this option off for certain attributes which will aid in lowering the memory footprint consumption.

 If you still have questions contact our support service