Answer-first summary for fast verification
Answer: The data is pruned by micro-partitions based on column values, then rows within the remaining micro-partitions are filtered.
Option B is correct because it accurately describes Snowflake's two-stage optimization process. First, Snowflake uses micro-partition metadata (min/max values, distinct counts) to prune entire micro-partitions that don't contain relevant data for the query. Second, within the remaining micro-partitions, Snowflake filters rows based on query conditions. This approach minimizes I/O by eliminating unnecessary data reads at both the partition and row levels. Option A is incorrect because pruning happens at the micro-partition level first, not by column. Option C is incorrect because column pruning happens after micro-partition pruning, not before. Option D is incorrect because Snowflake doesn't sort data within micro-partitions by rows during query execution - the data is already stored in sorted columnar format.
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How does Snowflake optimize query performance by leveraging the sorted data and storage structure within micro-partitions?
A
The data is pruned by column to eliminate unnecessary micro-partitions, then the rows within the remaining micro-partitions are filtered.
B
The data is pruned by micro-partitions based on column values, then rows within the remaining micro-partitions are filtered.
C
Any micro-partitions that are not needed for the query are pruned, then the data within the remaining micro-partitions is pruned by column.
D
The data within micro-partitions is sorted by rows, then the columns are filtered based on the query conditions.