Explanation:
The question focuses on achieving optimal query performance for JSON data containing numerous dates and arrays. According to Snowflake documentation and the community discussion, when semi-structured data includes dates/timestamps and arrays, flattening the data into structured relational columns (Option A) provides better pruning and storage efficiency. This approach allows Snowflake to leverage micro-partition clustering and native data type optimizations for dates, which are stored as strings in VARIANT columns, leading to improved query performance. Option B (storing in VARIANT) is flexible but less performant for this specific data pattern. Option C (VARIANT with STRIP_NULL_VALUES) does not address the core performance issues with dates and arrays. Option D (external stage with views) introduces unnecessary complexity without performance benefits. The community consensus, supported by upvoted comments and Snowflake's recommendations, strongly favors Option A for optimal performance in this scenario.
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How can optimal query performance be achieved when processing a JSON file containing numerous dates and arrays in Snowflake?
A
Flatten the data and store it in structured data types in a flattened table. Query the table.
B
Store the data in a table with a VARIANT data type. Query the table.
C
Store the data in a table with a VARIANT data type and include STRIP_NULL_VALUES while loading the table. Query the table.
D
Store the data in an external stage and create views on top of it. Query the views.