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Time to SLA makes use of every Jira API feature available to minimize load on your Jira instance. Under normal circumstances, TTS functions without a significant effect on performance. However, But in rare instances, internal effects like a high number of SLAs and external effects like an overall high load on Jira instances and complicated app integrations may result in cause relatively slower issue loading times after issue transitions.

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There may be times when Time to SLA has trouble keeping up with issue updates due to numerous automations. If Jira's race condition can cause the SLA calculation to be delayed if the JQL condition is changed or updated during workflow transitions, the SLA calculation may be delayed because of Jira’s race condition. This is due to the fact that the issue's indexing may be completed after Time to SLA begins calculating the SLA, causing JQL to fail.

This could lead to problems with calculations and the wrong information being shown on the SLA Panel. Enabling the Asynchronous Update for the affected SLA on the SLA Configurations screen will get rid of such problems.

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🤔 How does it work?

Since it is not possible to change the way Jira works, Time to SLA offers a limited but effective solution: the Asynchronous SLA calculation method. You can activate the asynchronous SLA calculation for SLAs with this problem, recalculate the existing issues that have this SLA, and investigate the behavior of the plugin. TTS evaluates those SLAs out of issue event cycles so that JQL conditions will return the correct results.

When Asynchronous Update is enabled, here’s how Time to SLA will calculate your issue:The the event will be saved in the TTS database. Depending on how often the task is updated, Time to SLA TTS will calculate the issue and the SLA with a roughly 1-minute delay.

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