Performance Tuning Guide
This guide contains information how to get the best performance out of QPR ProcessAnalyzer system while taking into account incurred infrastructure costs. Performance optimization is entirely different in Snowflake and in-memory models, so there are separate chapters for them.
Snowflake models
In Snowflake, calculations are performed in virtual warehouses (https://docs.snowflake.com/en/user-guide/warehouses). There are two methods to affect the performance: warehouse size and multiclustering.
If there are individual queries that take considerably longer than other queries, the query acceleration service might be helpful (https://docs.snowflake.com/en/user-guide/query-acceleration-service).
If running machine learning with large datasets, warehouses might run out of memory. If that occurs, consider Snowpark-optimized warehouse which has more memory (https://docs.snowflake.com/en/user-guide/warehouses-snowpark-optimized).
Snowflake scaling
- Larger warehouse
- Multi-cluster warehouses
In-memory models
Server resources
- Memory
- Processors
- Database