Performance Tuning in Snowflake
Performance optimization with Snowflake can be challenging for several reasons. First, consumption based cloud services are newer offerings focused on ease-of-use by average users, rather than advanced database engineers who are accustomed to fine-grained tuning knobs. Second, the Snowflake Data Cloud is adopted by more modern and data-driven companies. Their user base is more diverse with varying levels of database proficiency. This has increased the chances of poorly written queries, sub-optimal schemas and inefficient data ingestion patterns. Finally, the ease of scaling up/out a cloud-based data warehouse has created a shortcut for solving performance problems, causing companies to overlook proper database optimizations at the cost of paying additional compute costs.
A common misconception about Snowflake is the only solution available to improve query performance is to scale up to a bigger warehouse. In reality the best approach depends on the problem area, which is most often in ingestion, transformation or end-user queries and often the most effective solutions are based upon a design approach rather than pure query tuning. This does not mean that scaling up is not a good strategy. It is suitable when you intend on improving query performance. However, before scaling up, it is best to find the issue that might be affecting the performance of your data warehouse. It is essential to be aware of Snowflake performance tuning tactics that you can employ to boost your company’s efficiency.
Performance, But at What Costs?
- Manual Optimizations: You can always spend more time on data modeling, manually optimizing your queries, tweaking your database schemas, pre-aggregating data or materializing or caching common subqueries.
- Faster Data Warehouse Cost: Throwing more money at the problem by increasing the cluster size is not always sustainable while data volumes and usage grow.
- Data Engineering Resource Cost: Manually optimizing queries requires long data engineering hours. Tedious, time-consuming, error-prone, and brittle, these efforts can seem like a never-ending project.
- Data Engineering Opportunity Cost: More importantly, manual optimization efforts take precious time that data engineers could spend doing other essential tasks.