Best way to bulk update large volume of tickets

[yaron]

Hello,

We plan to run data backfill: update new custom field for old issues.

The trigger in update in the Epic level and then to push the field value down to the related child issue and in some cases even to the sub tasks of some of the child issues.

In total, the update should impact over 24K tickets.

What is your recommendation (better based on past experience) - 

• Based on automation rules with chunks of tickets.
• Use Bulk update per selected filters, in chunks as well.

Any tips and guidelines will be appreciated.

Thanks,

Yaron

[Tudor Tofan]

Hi @yaron !

Let's take them one by one:

* Automation - the main disadvantage is that it will take quite some time and for 24k tickets you will most likely exceed the service limit https://support.atlassian.com/cloud-automation/docs/automation-service-limits/

* Bulk Update - you need to update them in chunks of 1000 tickets

* CSV Import - Take great care of the csv and the mapping. You should create a sandbox (if you have premium) and try first on the sandbox.

Another way is via REST API. You should be able to do this in Postman with minimal technical skills. Here is the link to the Jira Cloud REST API: https://developer.atlassian.com/cloud/jira/platform/rest/v3/intro/#about

[Sreenivasaraju P]

Hi @yaron ,

We have used CSV File import

[Anthony Morais]

Hi @yaron 

For a dataset that large, I’d recommend processing it in smaller JQL-based batches to avoid automation and API limits.

Use a top-down approach (Epics → Stories → Sub-tasks) and split the updates by logical filters — for example, statusCategory != Done, creation date ranges, or project components.

This helps maintain consistency between parent and child issues and prevents timeouts or throttling during bulk edits.

Hope this helps!

[Gor Greyan]

Hi @yaron

I think automation is best for this.

You can split the 24K tickets into small parts, for example, via the created date, and run automation on that.