When I trigger the notebook with pipeline, the runtime takes too long.

[ahmetyilmaz]

Hi, the runtime of the notebook is 2 minutes but when I trigger it with pipeline it takes 14 minutes, which makes unnecessary waiting. What is the reason for this?

[v-prasare]

We are following up once again regarding your query. Could you please confirm if the issue has been resolved through the support ticket with Microsoft?

If the issue has been resolved, we kindly request you to share the resolution or key insights here to help others in the community. If we don’t hear back, we’ll go ahead and close this thread.

Should you need further assistance in the future, we encourage you to reach out via the Microsoft Fabric Community Forum and create a new thread. We’ll be happy to help.

Thank you for your understanding and participation.

[lbendlin]

Are you using a non-default spark pool?

[ahmetyilmaz]

Hi thank you @lbendlin . No I use the default. even if the notebook finishes running I guess the pipeline cannot detect it. At the moment, the notebook finished running at 8 minutes, but the pipeline shows the notebook running even though it has been 25 minutes.

[v-prasare]

Hi @ahmetyilmaz,

In this scenario if your blocked on this i suggest you to raise a support ticket here. so, that they can assit you in addressing the issue you are facing. please follow below link on how to raise a support ticket:

How to create a Fabric and Power BI Support ticket - Power BI | Microsoft Learn

Thanks,

Prashanth Are

MS Fabric community support

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly and give Kudos if helped you resolve your query

[v-prasare]

Hi @ahmetyilmaz,

Thanks for reaching MS Fabric community support.

The most probable causes for the pipeline taking taking longer time to run are:

1. Cluster Startup Delay – The pipeline may be provisioning a new Spark cluster, adding extra time. If the notebook runs on an already active cluster, this delay is avoided.

2. Different Compute Resources – The pipeline might be using a lower-tier or different Spark configuration than your interactive session, slowing execution.

3. Pipeline Overhead – Additional checks, dependency loading, and data validation in the pipeline might introduce extra execution time.

additionally, Check the pipeline logs and compute settings to identify the bottleneck.

please refer to similar community questions raised: Solved: Long Connection Time When Triggering a Notebook in... - Microsoft Fabric Community

Thanks,

Prashanth Are

MS Fabric community support

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly and give Kudos if helped you resolve your query