Caching in Snowflake: Result, Metadata, and Data Cache – What’s Real, What’s Not

If you’ve ever been surprised by how fast a query returned in Snowflake, you’ve already run into caching magic. But caching in Snowflake is often misunderstood – partly because there are different types of caches, each working in its own way.

Let’s cut through the noise and see what’s real, what’s not, and where you should (and shouldn’t) rely on Snowflake caching.

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1. Result Cache: Instant Replay 🎬

What it is:

• Stores the final results of a query for 24 hours.
• Scope: User + Virtual Warehouse + underlying data state.

When it works:

• If you run the same query again (with same role, warehouse, and data unchanged), Snowflake serves results directly from the cache – no computation at all.
• Queries return in milliseconds, regardless of dataset size.

Limitations:

• Changes in data (insert/update/delete/merge) or role permissions invalidate the cache.
• Slightly different queries (even whitespace or aliases) are treated as new and won’t hit the cache.

💡 Think of this as Snowflake’s “shortcut.” Great for BI dashboards that rerun identical queries.

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2. Metadata Cache: Knowing Without Scanning 📊

What it is:

• Snowflake automatically keeps track of table-level metadata: row counts, min/max values, distinct counts, etc.
• Scope: Used by the query optimizer.

When it works:

• Optimizer can prune partitions and skip scanning entire micro-partitions.
• Example: SELECT * FROM sales WHERE region = 'APAC' only touches partitions with 'APAC'.

Limitations:

• Doesn’t return results directly – just helps queries run faster by avoiding unnecessary scans.
• Metadata accuracy depends on recent statistics – ANALYZE can help refresh them.

💡 This isn’t caching in the sense of “reuse output,” but more like “cached knowledge” that Snowflake leverages to cut costs and time.

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3. Data Cache: SSD Power Under the Hood ⚡

What it is:

• When queries execute, the virtual warehouse reads data from cloud storage (S3, GCS, or ADLS).
• Frequently accessed data blocks are cached on local SSDs in the warehouse.

When it works:

• If the same warehouse runs queries repeatedly on the same data, it avoids fetching from cloud storage every time.
• Big performance boost for iterative development or repeated ETL runs.

Limitations:

• Cache is local to the warehouse and wiped when the warehouse is suspended.
• If you spin up a new warehouse, it won’t share the cache of another.

💡 Think of this like your laptop’s RAM cache – temporary but super useful while active.

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4. What’s Not Real Caching (Myth Busting)

• ❌ “Snowflake automatically caches everything forever” → Wrong. Result cache is only 24 hours. Data cache is warehouse-specific and volatile.
• ❌ “Different warehouses share cache” → Nope. Each warehouse has its own SSD-level cache.
• ❌ “Metadata cache speeds up every query” → Only queries that can use pruning benefit.

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5. So, How Should You Think About Caching?

• Result Cache → Use for BI dashboards, repeated queries, and quick testing.
• Metadata Cache → Trust Snowflake’s optimizer; refresh stats for best pruning.
• Data Cache → Keep warehouses warm for iterative workloads, but don’t expect persistence across suspends.

👉 Rule of thumb: Caching in Snowflake is a speed enhancer, not a silver bullet. For consistent performance, size warehouses properly and design queries for efficiency.