How to improve performance

Why

The goal is to prevent performance issues as perceived by end users, such as slow loading fields, delayed results, or inconsistent behavior in Jira.

Check troubleshooting doc here to find out more about performance symptoms.

By applying a few best practices, you can reduce latency, avoid performance issues, and provide a consistent user experience across Jira.

Large datasets can impact performance across the entire data lifecycle: data source, Elements Connect, and Jira UI.

To minimize this impact:

Limit query results
Larger sets increase retrieval time. One thing you can do is work on your query to return only relevant data.
Avoid increasing max suggestions
Larger sets also increase rendering time. In Elements Connect there is a “Max suggestions” option in field configuration which is by default set to 1000. Overcoming this value may result in performance issues.
Check Connected field configuration doc here for more info.
Use dynamic filtering
When dealing with large sets, one recommendation is to delay the data source request until the user has typed a minimum number of characters, and to filter the results based on that input. This is achieved using the $userInput dependency.
Check the doc here for detailed guidance.

Requests sent to the data source, Elements Connect, and Jira can introduce latency, especially under high load or when rate limiting applies.

Check usage limitations doc here to find out more about Elements Connect rate limiting.

To reduce the impact, follow these recommendations:

Is your data really live?
In some cases, real-time data is not required.
Use Snapshot fields instead of live queries when data does not need to be updated dynamically. This reduces the number of requests and improves performance and stability.
Refer to the doc page here to understand the differences between Snapshot and Object fields.
Optimize cache
Caching helps reduce the number of requests sent to the data source.
- Increase cache duration for stable datasets
- Avoid repeated identical requests
- Improve response time and reduce the risk of rate limiting

Check the doc here for more info.

Plan for peak of usage
Request load can significantly increase during peak periods.
- Be aware of high traffic situations (e.g. incidents, large campaigns)
- Avoid running bulk operations that trigger many updates at once
- Spread requests over time to reduce load on the system