🚀 Snowflake Caching: The Hidden Engine Behind Fast Queries

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🚀 Snowflake Caching: The Hidden Engine Behind Fast Queries

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Source: https://www.linkedin.com/pulse/snowflake-caching-hidden-engine-behind-fast-queries-mohapatra-y6wyf?trackingId=xP9nN9ecSTuF1gxQ9dl6jQ%3D%3D

🚀 Snowflake Caching: The Hidden Engine Behind Fast Queries

Arabinda Mohapatra

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

  • Snowflake’s caching system is a silent performance booster—accelerating queries, reducing compute costs, and making workloads feel snappy.
  • It works across multiple layers, each designed to minimize redundant data access and optimize query execution.
  • One of Snowflake’s most underrated strengths is its multi-layer caching system, which quietly improves query speed and reduces cost.

🔍 What Is Caching in Snowflake?

Caching is the process of storing intermediate or final results so they can be reused—avoiding redundant computation or data retrieval. Snowflake uses three distinct types of cache, each serving a different purpose:

One of Snowflake’s cache layers, the local disk cache, is indeed tied to the virtual warehouse level.

🔹 Local Disk Cache (Warehouse-Level)

  • Where it lives: On the SSDs attached to the virtual warehouse nodes.
  • What it stores: Recently accessed table data (micro-partitions).
  • When it helps: During active sessions, repeated queries, or iterative scans.
  • What happens on suspend: Cache is lost. When a warehouse auto-suspends, its local cache is wiped.

👉 This means transient warehouses or frequent suspends can reduce cache effectiveness

🧠 The Three Layers of Snowflake Cache

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⚡ How Caching Boosts Performance

  • Faster query execution: Result cache can return answers in milliseconds.
  • Reduced compute usage: Avoids reprocessing data unnecessarily.
  • Smarter query planning: Metadata cache helps Snowflake skip scanning irrelevant partitions.
  • Improved concurrency: Warehouses reuse cached data, reducing pressure on compute.

🔧 How It Works Internally

  1. Query submitted → Cloud Services checks result cache.
  2. If cache hit → Returns result instantly.
  3. If cache miss → Parses SQL, uses metadata cache to prune partitions.
  4. Warehouse executes → Reads only needed micro-partitions.
  5. Local disk cache stores data for reuse during session.
  6. Result cache updated → Available for future identical queries.

👉 Caches are automatically managed—you don’t configure them manually. But understanding them helps you design smarter queries and avoid unnecessary compute.

💡 Best Practices to Leverage Caching

  • Use filters and WHERE clauses to enable pruning.
  • Avoid SELECT *—target specific columns.
  • Don’t overuse transient warehouses—they lose local cache on suspend.
  • Reuse queries when possible—result cache kicks in.

Refer-https://community.snowflake.com/s/article/Caching-in-the-Snowflake-Cloud-Data-Platform

#SnowflakeDataCloud #QueryPerformance #CloudWarehousing #DataEngineering #SmartCaching #WarehouseOptimization #TechLeadership #ModernDataStack


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