How would you go about diagnosing and resolving a “Concurrency Scaling limit reached” error?

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How would you go about diagnosing and resolving a "Concurrency Scaling limit reached" error in Snowflake?

Daniel Steinhold Answered question August 16, 2023

Diagnosing and resolving a "Concurrency Scaling limit reached" error in Snowflake involves understanding the concurrency scaling feature, analyzing the query workload, and optimizing your setup. Here's a step-by-step approach:

1. **Understand Concurrency Scaling**:
- Familiarize yourself with Snowflake's Concurrency Scaling feature, which automatically adds additional compute resources (clusters) to handle spikes in query concurrency.
2. **Analyze Query Workload**:
- Review your query history to identify queries that triggered the concurrency scaling limit error. Pay attention to query complexity, join patterns, data volume, and frequency of execution.
3. **Identify Resource-Intensive Queries**:
- Identify queries that are resource-intensive and may be contributing to the concurrency scaling limit. Look for queries involving large tables, complex joins, aggregations, or subqueries.
4. **Query Optimization**:
- Optimize resource-intensive queries by rewriting them, adding appropriate indexes, improving join conditions, or utilizing materialized views to reduce the need for complex calculations.
5. **Concurrency Scaling Settings**:
- Check your concurrency scaling settings, including the minimum and maximum number of clusters and auto-pause duration. Adjust these settings based on your workload and performance requirements.
6. **Query Queues and Prioritization**:
- Implement query queues to prioritize and manage workloads. Allocate more resources to critical workloads and restrict non-essential queries from triggering concurrency scaling.
7. **Data Distribution and Clustering**:
- Evaluate data distribution and clustering keys for your tables. Poorly distributed data can lead to excessive data movement during query execution and trigger scaling.
8. **Partitioning and Pruning**:
- If applicable, consider partitioning large tables and optimizing partition pruning to reduce the amount of data scanned during queries.
9. **Monitor Warehouse Activity**:
- Monitor warehouse activity to identify peak usage periods. Adjust the concurrency scaling settings to automatically allocate more clusters during high-demand times.
10. **Review Warehouse Size**:
- If your warehouse size is too small, it may trigger concurrency scaling more frequently. Consider resizing the warehouse to handle more concurrent queries.
11. **Limit Concurrent Users**:
- If you have many concurrent users, limit the number of concurrent users or implement user-based query throttling to control the number of active queries.
12. **Monitor and Iterate**:
- Continuously monitor query performance, concurrency scaling usage, and resource consumption. Use Snowflake's performance metrics to fine-tune your setup over time.
13. **Contact Snowflake Support**:
- If you're unable to resolve the issue after optimizing your queries and adjusting settings, reach out to Snowflake support for guidance.

Remember that diagnosing and resolving a "Concurrency Scaling limit reached" error is a continuous process that requires careful analysis of your query workload, query optimization efforts, and adjusting Snowflake's concurrency scaling settings to ensure efficient and reliable performance.

Daniel Steinhold Answered question August 16, 2023

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