Fabric Trial Capacity - Too Many Requests for Capacity Error

Hi shashwotm,
Thank you for reaching out to the Microsoft Fabric Community Forum and thanks to the Tamanchu , arabalca for sharing helpful insights.

Just checking in, were you able to resolve the issue? If not please feel free to share an update and we will be happy to assist further.
Your feedback will also help others facing similar challenges.

Thank you.

Hi shashwotm,

This usually happens when the Fabric capacity has reached its Spark compute/concurrency limits, even on Trial capacities.

A few things worth checking :

• Go to Workspace Settings - Data Engineering/Science - Spark settings - Jobs to see active and queued Spark sessions.
• Sometimes orphaned Spark sessions remain active in the background and continue consuming capacity.
• The default Starter Pool can also be quite heavy for small capacities/trials.

A few practical mitigations that often help :

• Stop unused notebooks/sessions
• Reduce concurrent notebook executions
• Create a smaller custom Spark pool instead of using the default Starter Pool
• Enable High Concurrency mode when relevant
• Retry after the Spark session fully terminates

Microsoft documentation also explains this behavior here :
https://learn.microsoft.com/en-us/fabric/data-engineering/troubleshoot-permissions-capacity 

And for Spark concurrency limits :
https://learn.microsoft.com/en-us/fabric/data-engineering/spark-job-concurrency-and-queueing 

If this helped, please consider marking the response as a solution so it can help others facing the same issue.

Hi  shashwotm , 

This error means your Fabric capacity has reached the concurrent Spark jobs limit. This is a SKU/capacity-level throttling issue, not necessarily a problem with your code.

Learn more here: https://learn.microsoft.com/fabric/data-engineering/spark-job-concurrency-and-queueing

I would recommend reviewing the following:

1. Review and cancel active jobs

Go to Workspace Settings → Data Engineering/Science → Spark settings → Jobs and cancel any non-critical Spark jobs.

There are often "zombie" notebook sessions consuming Spark slots without doing anything useful.

https://learn.microsoft.com/fabric/data-engineering/job-concurrency-queue-monitoring

2. Activate High Concurrency (if applicable)

This can help improve Spark session utilization and reduce concurrency-related issues.

https://learn.microsoft.com/en-us/fabric/data-engineering/configure-high-concurrency-session-notebooks

3. Review Spark compute configuration

In fact, I ran into the same error today and immediately thought of you.

In my case, I adjusted the Spark configuration. I had a Medium node running on an F4 capacity, and I changed it to Small, also adjusting the number of nodes. That resolved the issue for me.

Give it a try and let us know if it works for you:

https://learn.microsoft.com/en-us/fabric/data-engineering/environment-manage-compute

Additional references that may help:

• https://learn.microsoft.com/fabric/data-engineering/job-queueing-for-fabric-spark

• https://learn.microsoft.com/fabric/data-engineering/autoscale-billing-for-spark-overview

• https://learn.microsoft.com/fabric/data-engineering/troubleshoot-permissions-capacity#capacity-and-related-issues

If this response has been helpful, please don't forget to give it a Like and mark it as a Solution so other community members can find it easily.

Thank you!

Hi shashwotm, 

This is a known limitation, but a fix is coming very soon! 
Check it out: Custom Live Pools for Fabric Data Engineering (Pre... - Microsoft Fabric Community

Hi shashwotm 

If your requirements are not too large in terms of data processing or you can let it run for longer. why not change the size of a spark pools to even a smaller size which will then allow it to run successfully on your smaller fabric capacity. So make sure to ensure that what you're running in Spark is using the optimal code, and that to ensure once again that it is not using as much of the Spark resources as needed.

Hi shashwotm 

Just checking in to see if your issue has been resolved. Please let us know if you need any further assistance.

Thank you.