Forum Discussion
Error message on notebook sql
I got the following error when I ran SQL on the notebook:
Diagnostic ID: 3d62864c-dc22-4d20-9b33-304aba1aac98
Timestamp: 2026-05-26T20:00:31.978Z
Message: [object CloseEvent]
JSON
{
"type": "close",
"timeStamp": 16735.19999998808,
"code": 1000,
"reason": "{\"reason\":\"Session error or stopped.\",\"state\":\"session-completed\"}",
"wasClean": false,
"target": {
"url": "wss://pbipcanc3-canadacentral.pbidedicated.windows.net/webapi/capacities/0a466b55-b214-4517-a99d-6128acb2e811/workloads/Notebook/Data/Automatic/api/workspaces/1d6102c8-0bd2-499b-a84f-e32baee5b879/artifacts/1ab656ca-2ea5-4184-9296-59aefc0b23a2/jupyterApi/versions/1/api/kernels/380cca33-3c48-4c1d-a0f5-5e1ad853c8df/channels?token=dummy_token&session_id=48c075f2-5d1d-4e80-a6cc-832c86633937",
"readyState": 3,
"protocolsProfile": [
7,
4427
]
},
"currentTarget": {
"url": "wss://pbipcanc3-canadacentral.pbidedicated.windows.net/webapi/capacities/0a466b55-b214-4517-a99d-6128acb2e811/workloads/Notebook/Data/Automatic/api/workspaces/1d6102c8-0bd2-499b-a84f-e32baee5b879/artifacts/1ab656ca-2ea5-4184-9296-59aefc0b23a2/jupyterApi/versions/1/api/kernels/380cca33-3c48-4c1d-a0f5-5e1ad853c8df/channels?token=dummy_token&session_id=48c075f2-5d1d-4e80-a6cc-832c86633937",
"readyState": 3,
"protocolsProfile": [
7,
4427
]
},
"isTrusted": true
}
Additional info: InstanceId: e5d99308-5127-4d09-b939-7dfa5ce9892d
Hi Krishsaba ,
This error means your Fabric capacity has hit the concurrent Spark jobs limit. It's a SKU-level throttling issue, not a problem with your code.
Learn : https://learn.microsoft.com/fabric/data-engineering/spark-job-concurrency-and-queueing
I see this:
Activate high concurrency: https://learn.microsoft.com/es-es/fabric/data-engineering/configure-high-concurrency-session-notebooks-in-pipelines#configure-high-concurrency-mode
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 slots without doing anything useful. https://learn.microsoft.com/fabric/data-engineering/job-concurrency-queue-monitoring
2. Wait and retry
If the load is temporary (end of day, scheduled pipelines firing at the same time), waiting a few minutes and retrying is usually enough. The timestamp is 20:00h, which may coincide with scheduled workloads.
3. Check the Monitor Hub
Filter by "Running" status and check how many Spark jobs are active simultaneously. You can also see if there's contention from other workspaces on the same capacity (noisy neighbor scenario).
More:
https://learn.microsoft.com/fabric/data-engineering/job-queueing-for-fabric-spark .
https://learn.microsoft.com/fabric/data-engineering/autoscale-billing-for-spark-overview
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!
5 Replies
Hi Krishsaba ,
This error means your Fabric capacity has hit the concurrent Spark jobs limit. It's a SKU-level throttling issue, not a problem with your code.
Learn : https://learn.microsoft.com/fabric/data-engineering/spark-job-concurrency-and-queueing
I see this:
Activate high concurrency: https://learn.microsoft.com/es-es/fabric/data-engineering/configure-high-concurrency-session-notebooks-in-pipelines#configure-high-concurrency-mode
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 slots without doing anything useful. https://learn.microsoft.com/fabric/data-engineering/job-concurrency-queue-monitoring
2. Wait and retry
If the load is temporary (end of day, scheduled pipelines firing at the same time), waiting a few minutes and retrying is usually enough. The timestamp is 20:00h, which may coincide with scheduled workloads.
3. Check the Monitor Hub
Filter by "Running" status and check how many Spark jobs are active simultaneously. You can also see if there's contention from other workspaces on the same capacity (noisy neighbor scenario).
More:
https://learn.microsoft.com/fabric/data-engineering/job-queueing-for-fabric-spark .
https://learn.microsoft.com/fabric/data-engineering/autoscale-billing-for-spark-overview
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 Krishsaba
We wanted to follow up to check if you’ve had an opportunity to review the previous responses. If you require further assistance, please don’t hesitate to let us know.
Hi Krishsaba
Have you had a chance to look through the responses shared earlier? If anything is still unclear, we’ll be happy to provide additional support.
