Error message on notebook sql

[Krishsaba]

I got the following error when I ran SQL on the notebook:

InvalidHttpRequest

[TooManyRequestsForCapacity] [TooManyRequestsForCapacity] HTTP Response code 430: This Spark job can’t be run because you’ve hit a Spark compute or API rate limit. To proceed, cancel an active Spark job through the Monitoring hub, choose a larger capacity SKU, or try again later. For more visibility and control, go to Workspace settings → Job management (Job Concurrency & Queue Monitoring) to review running and queued Spark jobs, understand capacity contention, and take action as needed. [Learn more at 'https://go.microsoft.com/fwlink/?linkid=2356970&clcid=0x409']. HTTP status code: 430.

=====================

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

[GilbertQ]

Hi @Krishsaba 

What I also recommend is to look to see if you can optimize your SQL code in your notebook so that it can run faster.

[tayloramy]

Hi @Krishsaba, 

This is a capacity limit. 
What size capacity do you have? 
Are you using the defauly spark pools? 

For small capacities like F2 or F4, the default pool with medium nodes is too big, so you will need to create a custom pool using small nodes and no autoscale. 

[arabalca]

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-noteboo...

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

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!