How does Snowflake utilize clustering information to improve query performance?

The correct answer is A. It prunes unnecessary micro-partitions based on clustering metadata. Snowflake stores metadata (min/max values) for each column in every micro-partition. When a table has a clustering key, related data is co-located in fewer micro-partitions. At query time, Snowflake reads this metadata to skip (prune) micro-partitions that cannot contain rows…

Snowflake Cloud Data Platform Features and Architecture

Question

Options

AIt prunes unnecessary micro-partitions based on clustering metadata.
BIt compresses the data within micro-partitions for faster querying.
CIt automatically allocates additional resources to improve query execution.
DIt organizes clustering information to speed-up data retrieval from storage

How the community answered

(19 responses)

A
89% (17)
B
5% (1)
C
5% (1)

Explanation

Snowflake stores metadata (min/max values) for each column in every micro-partition. When a table has a clustering key, related data is co-located in fewer micro-partitions. At query time, Snowflake reads this metadata to skip (prune) micro-partitions that cannot contain rows matching the WHERE clause filter, drastically reducing the data scanned. Clustering does not compress data (B), auto-allocate resources (C), or change how data is retrieved from storage at a physical level beyond pruning (D).

Topics

#Clustering#Micro-partitions#Query Optimization#Data Pruning

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