Severe Slowdown of Synapse Workspace after Pipeline Run

Question

The execution of my pipeline has caused a significant performance degradation within the Azure Synapse platform, resulting in prolonged response times and rendering the platform unusable in some instances.

Answer 1

@Diakhate, Djibril

Thanks for using MS Q&A platform and posting your query.

Here are a few ways to diagnose and address the performance degradation caused by your pipeline in Azure Synapse Analytics. Here's what you can do:

• Query Performance: If the slowdown is due to slow queries within the pipeline, you can use the built-in monitoring tools. Azure Synapse provides query execution details that pinpoint slow-running queries. Look for patterns like:
• Incorrect data distribution: Data skew can overload specific compute nodes. (https://techcommunity.microsoft.com/t5/azure-synapse-analytics/azure-synapse-dedicated-sql-pool-performance-problem/td-p/4005715)
• Outdated statistics: Outdated statistics can lead to inefficient query plans. Consider refreshing statistics on your tables. (https://learn.microsoft.com/en-us/azure/synapse-analytics/)
• Suboptimal indexing: Review your indexing strategy. Clustered columnstore indexes are often beneficial for large tables in Synapse. (https://learn.microsoft.com/en-us/azure/synapse-analytics/)

Resource Optimization:

• Serverless vs Dedicated Pools: If using the serverless pool, consider its resource limitations. Parallel queries can overload resources. Tools like Power BI refreshes might need scheduling optimization to avoid concurrency issues. (https://learn.microsoft.com/en-us/troubleshoot/azure/synapse-analytics/serverless-sql/query-perf/ssql-perf-optimize-querying)
• Resource Scaling (Dedicated Pools): For dedicated pools, consider scaling compute resources if the workload demands more.

General Troubleshooting:

• Monitor Pipeline Execution: Use Azure Monitor to track pipeline execution and identify specific stages causing delays.
• Simplify Pipeline Logic: Break down complex pipelines into smaller, more manageable steps.
• Parallelization (if applicable): If your pipeline processes data independently, explore parallelization options to improve throughput.

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