Azure Synapse pipeline window transformation Queued for 1.5 hours

Question

I have a simple data flow in a Synapse pipeline, which contains a window transformation. The transformation usually completes in around 3 seconds (225 columns x 2771 rows), but sometimes it is queued for a very long time and eventually causes the pipeline to fail due to a timeout.

Nothing in the timeout message indicates why the pipeline fails.

Answer 1

Hi @Jeremy Fox
Thanks for the question and using MS Q&A platform.

The long queue times and eventual timeout you're experiencing with the window transformation in your Synapse pipeline can be caused by a few factors. Here are some steps to troubleshoot and potentially fix the issue:

1. Increase Data Integration Units (DIUs): DIUs are the virtual cores allocated to your data flow activity. By increasing the DIUs, you provide more processing power for handling complex transformations like windows. Go to the Data Flow activity settings in your pipeline and adjust the DIUs.
2. Check Parallelism: The window transformation might not be utilizing parallelism effectively. You can try increasing the degree of parallelism (DOP) in the activity settings. This allows the transformation to process data concurrently across multiple cores.
3. Optimize Window Function: Review the logic within your window function. Complex window definitions or large window sizes can lead to slower processing. Try simplifying the logic or adjusting the window size if possible.
4. Investigate Resource Bottlenecks: While the timeout message might not be specific, you can still check the Synapse Analytics workspace for resource utilization. Look for spikes in CPU or memory usage during pipeline execution. This can indicate resource limitations and suggest increasing DIUs or optimizing the data flow.
5. Review Transformation Order: The order of transformations in your data flow can impact performance. If other transformations precede the window and create complex data structures, it might slow things down. Consider reordering transformations for better efficiency. Here are related docs for improving mapping dataflow performance:
   Mapping data flows performance and tuning guide
   Optimizing data flow source performance
   Optimizing data flow sink performance

Hope this helps. Do let us know if you any further queries.

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