Forum Discussion
T-SQL Notebook vs. PySpark Notebook - Pipeline Performance Comparison
Hi [Recipient's Name],
Thank you for the update. I’m glad to hear that upgrading to Spark Runtime 1.3 has enhanced the consistency of the Pipeline refreshes.
Given that the issue was resolved following the upgrade, it’s likely that the previous runtime version experienced execution delays or metadata commit inconsistencies affecting the T-SQL operations. In contrast, PySpark may have managed metadata updates more efficiently, which could explain why it performed without any issues.
To ensure continued stability, I recommend the following actions:
- Monitor Execution Logs: Leverage Fabric’s monitoring tools to verify that all runs are complete successfully and to detect any hidden errors or delays.
- Validate Metadata Commit Times: If feasible, introduce a WAITFOR DELAY '00:00:05' statement after table creation to assess whether metadata commit timing was a factor.
- Maintain Up-to-Date Runtime Versions: Since the upgrade had a positive impact, staying on the latest stable version is advisable to mitigate the risk of similar issues in the future.
If this resolves your issue, kindly consider accepting your response as the solution. Doing so will help other community members facing similar challenges.
Thank you.
Thank you so much for the thorough response and all the great suggestions! We did evaluate/test everything you mentioned before I posted. We did just change our Spark settings today --> Runtime to 1.3 and tested the Pipeline again with new data and it refreshed on the first pipeline attempt - YAHOO!!! Only time will tell, but we are hopeful that maybe this Runtime upgrade resolved our issue.
THANK YOU again for the collaboration!!!
v-saisrao-msftCommunity Support
Hi [Recipient's Name],
Thank you for the update. I’m glad to hear that upgrading to Spark Runtime 1.3 has enhanced the consistency of the Pipeline refreshes.
Given that the issue was resolved following the upgrade, it’s likely that the previous runtime version experienced execution delays or metadata commit inconsistencies affecting the T-SQL operations. In contrast, PySpark may have managed metadata updates more efficiently, which could explain why it performed without any issues.
To ensure continued stability, I recommend the following actions:
- Monitor Execution Logs: Leverage Fabric’s monitoring tools to verify that all runs are complete successfully and to detect any hidden errors or delays.
- Validate Metadata Commit Times: If feasible, introduce a WAITFOR DELAY '00:00:05' statement after table creation to assess whether metadata commit timing was a factor.
- Maintain Up-to-Date Runtime Versions: Since the upgrade had a positive impact, staying on the latest stable version is advisable to mitigate the risk of similar issues in the future.
If this resolves your issue, kindly consider accepting your response as the solution. Doing so will help other community members facing similar challenges.
Thank you.