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Your Compute session is disconnected
This is my first time using it.
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!
11 Replies
TooManyRequestsForCapacity - you are running too many requests in quick succession. See if you can space your requests out a bit more.
This error indicates that the capacity has reached its request limit. Try spacing out your requests, waiting a few minutes before retrying, and checking for any other running workloads that may be consuming capacity. If you're on a trial or shared capacity, temporary throttling can occur during peak usage.
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...
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!
Hello 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 FabricThis 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.
This error is typically related to Fabric Spark capacity limits rather than the dataset itself. Try cancelling any running or queued Spark jobs, reconnecting your Spark session, and rerunning the job after a few minutes. If it continues, check the Spark session logs for additional details that could point to capacity contention or a specific job issue.
This usually happens because the Fabric trial capacity is overloaded or has too many Spark jobs running. Check the Monitoring Hub for active or queued jobs and cancel any that are stuck. If nothing is running, wait a while and try again, as trial capacities often hit usage limits.
Hi Ojicletus
I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions.
Thank you."FTL4 Trial capacity (ID: 97CA5FF2-A4EC-4515-B7EE-F72E392BC124, Central India) throwing 430 TooManyRequestsForCapacity for 2 days. Monitoring hub empty, no jobs running, changed pool settings, still same error. Please help." same problem i am facing any one can help me , [email protected]
Hi Ojicletus ,
I ran into a similar issue when trying to use Load to Table on a CSV file to load data into a Lakehouse table — I was getting the 430 error as well.
Here's an alternative approach that worked for me. Sharing it in case it helps you too:
Workaround Steps:
- Created a new Notebook in Microsoft Fabric.
- From the Data Items tab inside the notebook, added a new Lakehouse.
- Uploaded my CSV files directly to the Lakehouse.
- Started a New High Concurrency session instead of a New Standard session — the Standard session was triggering the same 430 error.
- Used PySpark to load the data into a table
df = spark.read.csv("Files/DimProducts.csv", header=True) df.write.mode("overwrite").saveAsTable("DimProducts")Do let me know if this works for you!
In the meantime, I'm also exploring the following and will update this thread if I find a resolution:
- How to get Load to Table working
- How to run successfully with a New Standard session
If this helped, ✓ Mark as Solution | 👍 Kudos appreciated
Ajitesh Churewal 🔗Connect with me on LinkedIn
Hi Ojicletus
May I check if this issue has been resolved? If not, Please feel free to contact us if you have any further questions.
Thank you
