Window function: rank over category

Question

Use window functions to compute rank partitioned by date.

Codemia · Accepted Answer

The problem requires us to compute the rank of items partitioned by date using window functions. We will be working with a dataset that likely has at least two columns: a 'date' column representing the date of each entry and a 'category' column representing the category of the items. The desired output is a ranking of items within each category for each date, allowing us to see how items compare to one another on a daily basis.

1. **Identify the Dataset**: Assume we have a table named `sales_data` with columns: `date`, `category`, and `sales_amount`.
2. **Select Required Columns**: We will select `date`, `category`, and `sales_amount` for ranking.
3. **Use Window Function**: Utilize the `RANK()` function to compute rankings. Partition by `date` and `category`, and order by `sales_amount` in descending order.
4. **Handle NULL Values**: Use `COALESCE()` to ensure that NULL sales amounts are ranked appropriately (e.g., treating them as 0).
5. **Structure the Query**: Use a Common Table Expression (CTE) to simplify the final selection of ranked results.

```sql
WITH RankedSales AS (
    SELECT 
        date, 
        category, 
        sales_amount, 
        RANK() OVER (PARTITION BY date, category ORDER BY COALESCE(sales_amount, 0) DESC) AS sales_rank
    FROM 
        sales_data
)
SELECT 
    date, 
    category, 
    sales_amount, 
    sales_rank
FROM 
    RankedSales
ORDER BY 
    date, category, sales_rank;
```

To optimize the query for large datasets:
1. **Indexing**: Create indexes on both the `date` and `category` columns to speed up partitioning and filtering operations.
2. **Batch Processing**: If the dataset is extremely large, consider processing data in batches by date ranges to reduce memory load.
3. **Materialized Views**: If this ranking is frequently queried, consider creating a materialized view that pre-computes the rankings to avoid recalculating each time.

1. **NULL Values**: Ensure that all NULL sales amounts are handled properly with `COALESCE()`. The current implementation treats NULLs as 0 in ranking.
2. **Duplicates**: If multiple entries have the same `sales_amount`, they will receive the same rank, which is the expected behavior of the `RANK()` function. However, if unique ranks are required, consider using `ROW_NUMBER()` instead.
3. **No Sales Data**: If a category has no entries on a specific date, it will not appear in the results. This might need to be handled depending on reporting requirements, possibly by using an outer join with a calendar table.

Window function: rank over category

Microsoft

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Sharpen Your Skills on Codemia

Sample Answer

Problem Understanding

Approach

Submit Your Answer

Microsoft Data Scientist Interview Guide

Related Questions

Optimize a slow query on products

Calculate conversion rate per user from orders

Window function: rank over sessions

Optimize a slow query on orders

Write a query to find time to first action from sessions