Forum Discussion
Your Compute session is disconnected
Hi Ojicletus ,
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.
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-r...
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Thank you!
Super UserHello Ojicletus,
This looks like a Spark capacity issue rather than a dataset issue.
The TooManyRequestsForCapacity (HTTP 430) error means Fabric Spark compute is unavailable or a Spark/API rate limit has been hit.
Since you're using the trial version, try these quick checks:
1. Go to Monitoring hub and cancel any running or queued Spark jobs.
2. Check Workspace settings → Job management for queued jobs or capacity contention.
3. Disconnect the current compute session, reconnect it, then rerun the job after a short wait.
Docs:
Concurrency limits and queueing in Apache Spark for Microsoft Fabric
Advocate IIThis looks more like a Fabric Spark capacity issue than a dataset problem. Trial capacities can easily hit compute or concurrency limits, causing the Try clearing any running Spark jobs, reconnecting your session, and retrying after a short wait. If others share the workspace, their jobs may also be consuming available capacity.