A Deep Dive into Caching Strategies in Snowflake
Arabinda Mohapatra
Running Kafka streams after dark, diving into genetic code by daylight, and wrestling with Databricks and Tableflow in every spare moment—sleep is optional
What is Caching?
Caching is a technique used to store the results of previously executed queries or frequently accessed data in a temporary storage area. This allows for faster data retrieval and reduces the need to reprocess queries, saving both time and computational resources. In Snowflake, caching is a critical feature that helps optimize query performance and manage costs effectively.
Types of Caching in Snowflake
Snowflake employs three primary types of caching to enhance query performance:
1. Query Result Cache
Functionality: The Query Result Cache stores the results of previously executed queries. If the same query is submitted again, Snowflake retrieves the result from the cache instead of reprocessing the query.
Retention: Results are retained for 24 hours. If the query is re-executed within this period, the retention can extend up to 31 days.
Management: This cache is fully managed by the Cloud service layer and is available across all virtual warehouses.
User Accessibility: The cache is shared among users. If User A executes a query, the result is cached and can be used by User B if they execute the same query.
Disabling: You can disable the Query Result Cache using the command ALTER SESSION SET USE_CACHED_RESULT=FALSE;.
Warehouse State: The Query Result Cache is available regardless of whether the warehouse is active or suspended.
2. Virtual Warehouse Cache (Local Disk Cache)
Functionality: This cache stores data retrieved from remote storage in the SSD and memory of the virtual warehouse. It is also known as the raw data cache or SSD cache.
Management: Managed by the compute layer, with the service layer ensuring data freshness.
Retention: The cache is automatically dropped when the warehouse is suspended.
Usage: If data already exists in the warehouse cache, Snowflake retrieves it from there instead of accessing remote storage.
Disabling: To disable this cache, you can suspend the warehouse using ALTER WAREHOUSE <WHNAME> SUSPEND;.
Warehouse State: The Local Disk Cache is only available when the warehouse is active. When the warehouse is suspended, this cache is dropped, and data must be retrieved from remote storage upon resuming.
3. Metadata Cache
Functionality: The Metadata Cache stores metadata information such as row count, clustering details, micro-partitions, and table size.
Management: Managed by the Cloud service layer.
Usage: This cache is used for queries like SHOW TABLES, DESC TABLE <tablename>, and SELECT COUNT(*). It can be accessed even when the warehouse is suspended.
Cost Efficiency: Since metadata operations do not require a running warehouse, they do not incur additional compute costs.
Disabling: The Metadata Cache cannot be disabled.
Warehouse State: The Metadata Cache is available regardless of whether the warehouse is active or suspended.
Monitoring Cache Usage
Understanding which cache is being used can help optimize query performance. Snowflake automatically checks the Query Result Cache before executing a query. If a cached result is available, it uses it. For metadata-related queries, the Metadata Cache is utilized, and for data retrieval, the Local Disk Cache is used if the data is already present in the warehouse layer.
Cost and Performance Considerations
Caching in Snowflake offers significant cost and performance benefits:
Cost Savings: By reducing the need for repeated computations, caching helps save on compute costs. Metadata cache operations, in particular, do not require a running warehouse, further reducing costs.
Performance: Caching significantly speeds up query performance by reducing data retrieval times and leveraging precomputed results.
Balancing Act: While caching improves performance, it’s essential to balance its use with resource availability. Disabling the Query Result Cache or suspending the warehouse can help manage resource usage effectively.
Warehouse State and Caching
Understanding how caching behaves when a warehouse is active or suspended is crucial for optimizing performance and managing costs:
Active Warehouse: When a warehouse is active, all three types of caching (Query Result Cache, Local Disk Cache, and Metadata Cache) are available. The Local Disk Cache is particularly beneficial as it allows for faster data retrieval from the warehouse’s SSD and memory.
Suspended Warehouse: When a warehouse is suspended, the Local Disk Cache is dropped, and data must be retrieved from remote storage upon resuming. However, the Query Result Cache and Metadata Cache remain available, allowing for efficient query execution even without an active warehouse.
Conclusion
Caching in Snowflake boosts query performance and cuts costs by storing and reusing data. Understand the three types—Query Result, Local Disk, and Metadata Cache—and how they work when a warehouse is active or suspended. Mastering caching helps data engineers, analysts, and administrators optimize workflows and improve efficiency.