The effects of query pruning can be observed by evaluating which statistics? (Select TWO).

The correct answer is A. Partitions scanned C. Bytes scanned. Query pruning (micro-partition pruning) allows Snowflake to skip partitions that cannot contain relevant data based on filter predicates. Its effectiveness is measured by: Partitions scanned (A) - the number of micro-partitions actually read versus the total available, and…

Performance Management

Question

Options

APartitions scanned
BPartitions total
CBytes scanned
DBytes read from result
EBytes written

How the community answered

(28 responses)

A
93% (26)
B
4% (1)
D
4% (1)

Explanation

Query pruning (micro-partition pruning) allows Snowflake to skip partitions that cannot contain relevant data based on filter predicates. Its effectiveness is measured by: Partitions scanned (A) - the number of micro-partitions actually read versus the total available, and Bytes scanned (C) - the total data volume read after pruning. A large gap between total partitions and partitions scanned confirms effective pruning. 'Bytes read from result' refers to cached result reuse, not pruning.

Topics

#Query Pruning#Performance Optimization#Query Statistics#Micro-partitions

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