Find top 3 products sold based on total quantity.

To find the top 3 products sold based on total quantity, you must first aggregate the total quantity for each product and then rank or limit the results.

Mechanics and "Why"

The fundamental approach involves two steps: aggregation and ranking/limiting.

Aggregation: Use GROUP BY product_id (or product_name) and SUM(quantity) to calculate the total quantity sold for each unique product. This consolidates all individual sales records into a single sum per product.

Ranking/Limiting:

* ORDER BY with LIMIT: The simplest method is to ORDER BY the total_quantity_sold in descending order and then use LIMIT 3. This is straightforward but doesn't gracefully handle ties if multiple products share the same quantity at the 3rd position, potentially excluding some or arbitrarily picking.
Window Functions (RANK(), DENSE_RANK()): A more robust and often preferred method for "top N" problems is to use a window function like RANK() or DENSE_RANK(). These functions assign a rank to each product based on its total quantity. RANK() assigns the same rank to tied values and then skips the next rank(s), creating gaps (e.g., 1, 1, 3). DENSE_RANK() assigns consecutive ranks to tied values (e.g., 1, 1, 2), ensuring no gaps. For "top N" where you want to include all* items tied for the Nth spot, DENSE_RANK() is usually more appropriate. You then filter for ranks less than or equal to 3.

Concrete Example

Using DENSE_RANK() provides a clear, tie-aware solution:

WITH ProductSales AS (
    SELECT
        product_id,
        SUM(quantity) AS total_quantity_sold
    FROM
        sales_table
    GROUP BY
        product_id
),
RankedProductSales AS (
    SELECT
        product_id,
        total_quantity_sold,
        DENSE_RANK() OVER (ORDER BY total_quantity_sold DESC) as product_rank
    FROM
        ProductSales
)
SELECT
    product_id,
    total_quantity_sold
FROM
    RankedProductSales
WHERE
    product_rank <= 3
ORDER BY
    product_rank, product_id;

Trade-offs and Scalability

While LIMIT is simpler, window functions are more powerful for complex ranking scenarios and handling ties. Performance-wise, both ORDER BY (for LIMIT) and window functions (which implicitly require sorting) can be resource-intensive on very large datasets. In distributed systems like Apache Spark, these operations often trigger data shuffles across nodes, which can be expensive network-wise. In cloud data warehouses like Snowflake, the query optimizer will attempt to leverage micro-partition metadata for efficient sorting, but a full sort on a massive dataset will still consume significant warehouse compute. The choice between RANK() and DENSE_RANK() depends on whether you want to skip ranks after ties or maintain consecutive ranks.

In the interview, also mention…

Discuss how you'd handle potential data quality issues (e.g., inconsistent product_id spellings) and the implications of data volume on query performance, referencing distributed processing concepts like shuffles or specific database optimizations.