Warehouse cache, also known as local disk cache, is a performance optimization feature in Snowflake that stores query results and intermediate data on the local SSD storage of virtual warehouse compute nodes. This caching mechanism significantly improves query performance by reducing the need to re…Warehouse cache, also known as local disk cache, is a performance optimization feature in Snowflake that stores query results and intermediate data on the local SSD storage of virtual warehouse compute nodes. This caching mechanism significantly improves query performance by reducing the need to retrieve data from remote cloud storage repeatedly.
When a virtual warehouse executes queries, it reads data from Snowflake's cloud storage layer. The warehouse cache automatically stores this retrieved data on the local solid-state drives (SSDs) attached to the compute nodes. Subsequent queries that require the same data can access it from this local cache, which provides much faster read speeds compared to fetching data from remote storage.
The warehouse cache operates at the micro-partition level, storing raw table data that has been accessed during query execution. This cache persists as long as the virtual warehouse remains running and active. When a warehouse is suspended, the local disk cache is cleared, and upon resumption, the cache must be rebuilt through subsequent query operations.
Key characteristics of warehouse cache include automatic management by Snowflake, meaning users do not need to configure or maintain it manually. The system intelligently determines which data to cache based on usage patterns and available local storage capacity. The cache uses a least recently used (LRU) eviction policy to manage space when the cache becomes full.
For optimal performance, it is recommended to keep warehouses running when executing repeated queries against the same datasets. This allows the cache to remain populated and serve subsequent queries more efficiently. Organizations should balance the cost of keeping warehouses active against the performance benefits gained from maintaining a warm cache.
The warehouse cache works alongside Snowflake's result cache and metadata cache to provide a comprehensive caching strategy that minimizes data retrieval latency and improves overall query response times across the platform.
Warehouse Cache (Local Disk Cache) - Complete Guide
Why Warehouse Cache is Important
Warehouse cache is a critical performance optimization feature in Snowflake that significantly reduces query execution time and costs. Understanding this concept is essential for the SnowPro Core exam because it directly impacts how you design efficient data warehousing solutions and optimize query performance.
What is Warehouse Cache?
Warehouse cache, also known as local disk cache or SSD cache, is a caching mechanism that stores query results and intermediate data on the solid-state drives (SSDs) of the virtual warehouse compute nodes. This cache persists as long as the virtual warehouse remains running and is specific to each warehouse.
Key characteristics: • Stored on local SSD storage attached to compute nodes • Persists while the warehouse is running (even when suspended) • Cleared when the warehouse is resized or dropped • Separate from the result cache (which is at the cloud services layer)
How Warehouse Cache Works
1. Initial Query Execution: When a query runs for the first time, data is read from remote cloud storage (S3, Azure Blob, or GCS) and cached locally on the warehouse's SSDs.
2. Subsequent Queries: When similar queries access the same data, Snowflake first checks the local disk cache. If the required data is found, it reads from the fast SSD storage instead of fetching from remote storage.
3. Cache Invalidation: The cache is invalidated when: • The warehouse is resized (adding or removing clusters) • The warehouse is dropped and recreated • The underlying table data changes (DML operations)
4. Cache Retention: The cache remains available even when a warehouse is suspended, making subsequent resume operations more efficient for repeated workloads.
Performance Benefits
• Reduced latency: SSD reads are much faster than remote storage reads • Lower costs: Fewer data transfer operations from cloud storage • Improved throughput: Better performance for repetitive analytical workloads • Consistent performance: Predictable query times for cached data
Warehouse Cache vs Result Cache
It is important to distinguish between these two caching mechanisms:
Warehouse Cache: • Stores raw data on local SSDs • Requires compute resources to process • Warehouse-specific • Consumes compute credits
Result Cache: • Stores complete query results • No compute required for exact matches • Available across all warehouses • No compute credits consumed
Exam Tips: Answering Questions on Warehouse Cache
1. Remember the persistence rules: Warehouse cache survives suspension but is cleared on resize or drop. This is a frequently tested concept.
2. Know the difference from result cache: Exam questions often test whether you understand that warehouse cache still requires compute resources, while result cache does not.
3. Understand cache warming: Running representative queries after starting a warehouse can populate the cache for better subsequent performance.
4. Cost implications: Questions may ask about cost optimization - remember that leveraging warehouse cache reduces remote storage reads but still consumes compute credits.
5. Multi-cluster considerations: Each cluster in a multi-cluster warehouse has its own local cache. Scaling out creates new clusters with empty caches.
6. Watch for trick questions: If a question mentions resizing a warehouse, remember that this action clears the cache.
7. Data freshness: Warehouse cache automatically invalidates when underlying data changes through DML operations, ensuring data consistency.
8. Storage layer awareness: Remember that warehouse cache operates at the compute layer, not the storage layer. The three-layer architecture (storage, compute, cloud services) is fundamental to understanding caching behavior.