Jira Service Management: Asset Import Mapping Fails Inconsistently

Other Questions Jira
1

I’m hitting a snag with the Jira Mapping Endpoint for asset imports. It’s super frustrating because sometimes it works fine, but other times I get a 500 error saying ‘Something went wrong. Contact administrator.’ I can’t figure out if I’m messing up or if it’s an Atlassian issue.

Here’s a snippet of what I’m trying:

const response = await jiraAPI.sendRequest({
  endpoint: `/assets/workspace/${wsID}/v1/importsource/${impID}/mapping`,
  method: 'PUT',
  payload: JSON.stringify(schemaMap),
  headers: {
    'Accept': 'application/json',
    'Content-Type': 'application/json'
  }
});

I’m following the docs and checking for existing mappings before using PUT or PATCH. I’ve tweaked the code and mapping, but it’s still hit or miss. Any ideas on how to make this more reliable? It’s driving me nuts!

2

hey emma, i feel ya. those 500 errors are the worst! have u tried breaking up ur imports into smaller chunks? that helped me when i was dealing with similar headaches. also, maybe add some logging to track when it fails vs succeeds. could give u clues bout whats goin wrong. gl!

3

Working with the Jira asset import API can be challenging. In my experience, the intermittent 500 errors are often caused by issues on the server side rather than mistakes in the code. I once faced a similar issue and found that implementing an exponential backoff strategy helped, as waiting for a short period before retrying often resolved the error. I also made sure to double-check my payload to ensure every required field was correctly formatted. Reducing the size of each import and keeping an eye on API rate limits helped me manage the load effectively. If the error continues, contacting Atlassian support might provide additional insights.

4

I’ve encountered similar issues with Jira’s asset import API. One approach that worked for me was implementing robust error handling and logging. By capturing detailed error responses and request payloads, I was able to identify patterns in the failures. This helped pinpoint whether the issue was consistently related to specific asset types or data structures.

Additionally, I found that breaking down larger imports into smaller batches and introducing slight delays between requests improved reliability. It’s also worth checking your network stability and considering using a dedicated, stable connection for these operations.

If the problem persists, I’d recommend opening a support ticket with Atlassian. They might have insights into any known issues or potential workarounds specific to your Jira instance or dataset.