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Starting from scratch with RTA and struggling.
I've created an Eventhouse, a Notebook (that calls an API and returns json) and an Eventstream.
The notebook is scheduled every 5 minutes. The Eventstream has a custom end point on the left (that provides the keys i need for the notebook on publish), and on the right it pointings to a table i have set up in the KQL Database in the eventhouse manually through the UI (New table, adding column names and mappings).
The issue is with the notebook - whenever it runs it seems to work - data is ingested inside of a minute but the notebook hangs, and subsequent refreshes fail.
My code is below.
Solved! Go to Solution.
eventually took a different approach to this to solve.
tried a longer refresh schedule and jobs still ended up hanging. Used azure bus instead of the event hub producer (which i think was having trouble closing maybe) and set the whole script in a continous loop with a timer spacing the polling as opposed managing a refresh schedule in the notebook itself.
eventually took a different approach to this to solve.
tried a longer refresh schedule and jobs still ended up hanging. Used azure bus instead of the event hub producer (which i think was having trouble closing maybe) and set the whole script in a continous loop with a timer spacing the polling as opposed managing a refresh schedule in the notebook itself.
Hi @GlassShark1 ,
Thanks for reaching out to the Microsoft fabric community forum.
Please go through the below solved issue and documentations links which may help you in resolving the issue.
Solved: Ingest Data from Streaming API - Microsoft Fabric Community
Microsoft Fabric event streams overview - Microsoft Fabric | Microsoft Learn
Microsoft Fabric event streams capacity consumption - Microsoft Fabric | Microsoft Learn
If I misunderstand your needs or you still have problems on it, please feel free to let us know.
Best Regards,
Community Support Team
The notebook is scheduled every 5 minutes.
5 minutes seems to be the sweet/sour spot where it is not economical yet to keep the spark session running, and where the spin-up delay of the new session has a considerable impact. Most likely your environment is busy spinning up and tearing down, and not doing much else.