What step can reduce data spilling in Snowflake?
What step can reduce data spilling in Snowflake?
Using a larger virtual warehouse can help reduce data spilling in Snowflake by increasing the amount of memory and local disk space available for query operations. When a warehouse runs out of memory, data spills onto local or remote storage, significantly impacting performance. By opting for a larger warehouse, the likelihood of memory exhaustion and subsequent data spilling is reduced, leading to more efficient query execution.
Answer A is correct! Adjusting the available memory of a warehouse can improve performance because a query runs substantially slower when a warehouse runs out of memory, which results in bytes “spilling” onto storage. https://docs.snowflake.com/en/user-guide/performance-query-warehouse
Answer is A: https://docs.snowflake.com/en/user-guide/ui-query-profile For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks. This spilling can have a profound effect on query performance (especially if remote disk is used for spilling). To alleviate this, we recommend: Using a larger warehouse (effectively increasing the available memory/local disk space for the operation), and/or Processing data in smaller batches.
A correct - https://community.snowflake.com/s/article/Performance-impact-from-local-and-remote-disk-spilling