Forum Discussion
Your Fabric compute session disconnected because the notebook session completed or timed out.
Hello Guys,
can you please hel with the error like that:
Diagnostic ID: 52da8f47-7d71-4f6b-8e1c-e510bda53a70
Timestamp: 2026-06-10T10:24:21.113Z
Message: [object CloseEvent]
JSON
{
"type": "close",
"timeStamp": 318190.90000000596,
"code": 1000,
"reason": "{\"reason\":\"Session error or stopped.\",\"state\":\"session-completed\"}",
"wasClean": false,
"target": {
"url": "wss://pbippolandcen1-polandcentral.pbidedicated.windows.net/webapi/capacities/2d64c0fb-b487-43b6-b042-cd1fa4ec9f72/workloads/Notebook/Data/Automatic/api/workspaces/91ed74b0-939f-4cdc-95c0-75523dcb7ba9/artifacts/2d726226-e616-43a2-aa46-d4bbf342cff3/jupyterApi/versions/1/api/kernels/a4d4aad4-7056-4add-9585-ad81d7b5a4c3/channels?token=dummy_token&session_id=a67a5006-68be-4dec-a584-e89aaf3cf435",
"readyState": 3,
"protocolsProfile": [
7,
5508
]
},
"currentTarget": {
"url": "wss://pbippolandcen1-polandcentral.pbidedicated.windows.net/webapi/capacities/2d64c0fb-b487-43b6-b042-cd1fa4ec9f72/workloads/Notebook/Data/Automatic/api/workspaces/91ed74b0-939f-4cdc-95c0-75523dcb7ba9/artifacts/2d726226-e616-43a2-aa46-d4bbf342cff3/jupyterApi/versions/1/api/kernels/a4d4aad4-7056-4add-9585-ad81d7b5a4c3/channels?token=dummy_token&session_id=a67a5006-68be-4dec-a584-e89aaf3cf435",
"readyState": 3,
"protocolsProfile": [
7,
5508
]
},
"isTrusted": true
}
Additional info: InstanceId: dd7aa75a-794f-429c-8f92-00b240818d6e
what is happening ? This is trial 64 capacity...
How to debug it?
Best Wishes,
Jacek
Ok checked and fixed:
with an FTL4 Capacity, you need to create a Spark Pool with the configurations below in the Workspace Settings → Data Engineering/Science section:
Then you need to create an Environment Artifact and add this new Spark Pool to the Environment Artifact:
Publish the changes and then in a notebook you need to use this new Environment Artifact that has this Small Spark Pool which fits in the capacity of FTL4:
Best,
Jacek
8 Replies
Ok checked and fixed:
with an FTL4 Capacity, you need to create a Spark Pool with the configurations below in the Workspace Settings → Data Engineering/Science section:
Then you need to create an Environment Artifact and add this new Spark Pool to the Environment Artifact:
Publish the changes and then in a notebook you need to use this new Environment Artifact that has this Small Spark Pool which fits in the capacity of FTL4:
Best,
JacekHi jaryszek ,
Thank you for providing the error details. The error message suggests that the notebook session ended because the Spark request could not be accommodated by the available capacity at that moment. Specifically, the TooManyRequestsForCapacity error indicates that the Spark compute or API limits were reached when the job was submitted.
Even with an F64 Trial capacity, Spark workloads are subject to concurrency and capacity limitations. If other notebooks, Spark jobs, pipelines, or background workloads are running simultaneously on the same capacity, new requests may be throttled until resources are freed. This can result in the notebook session disconnecting and showing a session completed status.
To investigate further, please review the Monitoring Hub and Job Management section in Workspace Settings at the time of the issue. These tools can help determine if there were active or queued Spark workloads using capacity. Additionally, it would be beneficial to know if the notebook was executed interactively or via a pipeline, if multiple users share the trial capacity, and whether this behavior is consistent or isolated.
The following documentation may assist in understanding Spark concurrency, capacity limits, and workload monitoring in Fabric:
Concurrency limits and queueing in Apache Spark for Fabric - Microsoft Fabric | Microsoft Learn
Job concurrency and queue monitoring (Preview) - Microsoft Fabric | Microsoft Learn
Job queueing in Apache Spark for Fabric - Microsoft Fabric | Microsoft Learn
If you are able to provide further information, I can conduct a more detailed review to help identify the root cause.
Thank you.Thank you,
I am only 1 person using trial.
Where to find the option for job management?
I do not see it.Best,
JacekHi jaryszek ,
Thank you for confirming. Based on your screenshot, the Job Management option does not appear to be available in your current Fabric Trial environment. Feature availability may differ depending on tenant configuration and rollout status, so documentation may reference features that are not yet accessible in all environments.
As you are the sole user on the Trial capacity, the error is unlikely due to another user consuming workspace resources. However, Trial capacities are managed by Microsoft and are shared, so Spark workloads may still face capacity constraints. This can result in a TooManyRequestsForCapacity error, even with only one user active.
To assist further, it would be helpful to know if the issue occurs consistently or only at certain times. Please let us know if the error appears when starting a new notebook session, attaching to Spark, or executing a specific operation. This information will help us determine whether the issue is due to temporary capacity limitations or something specific to the notebook workload.
Thank you.Thanks,
it is just occuring all the time. I can not run anything
Best Wishes,
Jacek
Ok i have found the solution.
I needed to change spark pool as set up is as default environment because my trial has only....F4 capacity... not 64 !!
It depends on region unfortunately
Best,
Jacek
