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Analyzing Sales Trends with Window Frames

This challenge focuses on using SQL window frame specifications to analyze sales data and identify trends over time. Understanding how to apply window frames is crucial for calculating moving averages, cumulative sums, and other time-series analyses, which are common in business intelligence and data analytics. You will be provided with a sales dataset and asked to calculate various metrics using window frame clauses.

Problem Description

You are given a table named sales with the following columns:

  • sale_date (DATE): The date of the sale.
  • product_id (INTEGER): The ID of the product sold.
  • sale_amount (DECIMAL): The amount of the sale.

Your task is to write SQL queries that utilize window frame specifications to calculate the following:

  1. 7-Day Moving Average: Calculate the 7-day moving average of sale_amount for each product. The moving average should include the current day and the 6 preceding days.
  2. Cumulative Sales: Calculate the cumulative sum of sale_amount for each product up to each sale_date.
  3. Rank Sales by Date: Rank each product's sale_amount within its own product group, ordered by sale_date. The ranking should be calculated using a window frame that considers all sales for that product.

Key Requirements:

  • Use window frame specifications (e.g., ROWS BETWEEN ... AND ...) to define the window for each calculation.
  • Ensure the calculations are performed correctly for each product individually.
  • Handle edge cases where there might be fewer than 7 days of data available for the moving average calculation. In such cases, use all available data.
  • The cumulative sales should be calculated correctly even if the data is not sorted by date.

Expected Behavior:

The queries should return tables with the following structure:

  • 7-Day Moving Average: sale_date, product_id, moving_average
  • Cumulative Sales: sale_date, product_id, cumulative_sales
  • Rank Sales by Date: sale_date, product_id, sale_amount, rank

Examples

Example 1: 7-Day Moving Average

Input:
sales table:
sale_date | product_id | sale_amount
----------|------------|-------------
2023-01-01| 1          | 100
2023-01-02| 1          | 120
2023-01-03| 1          | 110
2023-01-04| 1          | 130
2023-01-05| 1          | 140
2023-01-06| 1          | 150
2023-01-07| 1          | 160
2023-01-08| 1          | 170

Output:
sale_date | product_id | moving_average
----------|------------|----------------
2023-01-01| 1          | 110.00
2023-01-02| 1          | 115.00
2023-01-03| 1          | 117.50
2023-01-04| 1          | 122.50
2023-01-05| 1          | 127.50
2023-01-06| 1          | 135.00
2023-01-07| 1          | 145.00
2023-01-08| 1          | 157.50

Explanation: The moving average is calculated by averaging the sale amount for the current day and the 6 preceding days. For the first few days, fewer than 6 preceding days are available, so all available days are used.

Example 2: Cumulative Sales

Input:
sales table:
sale_date | product_id | sale_amount
----------|------------|-------------
2023-01-02| 1          | 120
2023-01-01| 1          | 100
2023-01-03| 1          | 110

Output:
sale_date | product_id | cumulative_sales
----------|------------|------------------
2023-01-01| 1          | 100.00
2023-01-02| 1          | 220.00
2023-01-03| 1          | 330.00

Explanation: The cumulative sales are calculated by summing the sale amounts up to each sale date for each product. The data is not initially sorted by date, but the cumulative sum is calculated correctly.

Constraints

  • The sales table will contain at least 100 rows.
  • sale_date will be a valid date.
  • product_id will be a positive integer.
  • sale_amount will be a non-negative decimal number.
  • The queries should be efficient enough to execute within 10 seconds on a moderately sized database.

Notes

  • Consider using the AVG(), SUM(), and RANK() window functions.
  • Pay close attention to the order of operations within the window frame specification.
  • The PARTITION BY clause is essential for calculating metrics separately for each product.
  • The ORDER BY clause within the window frame is crucial for calculations like moving averages and ranking.
  • Think about how to handle edge cases where the window frame extends beyond the available data. Using ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW is often helpful for cumulative sums.
  • The specific SQL dialect (e.g., PostgreSQL, MySQL, SQL Server) may have slight variations in syntax, but the core concepts of window frames remain the same. Assume a standard SQL environment. Pseudocode for the 7-day moving average:
FOR each product_id in sales:
  FOR each sale_date in sales:
    Calculate the 7-day window:
      Start date = sale_date - 6 days
      End date = sale_date
    Calculate the sum of sale_amounts within the window.
    If the number of days in the window is less than 7, use all available days.
    Calculate the moving average = sum / number of days in the window
    Output sale_date, product_id, moving_average

Pseudocode for the cumulative sales:

FOR each product_id in sales:
  cumulative_sum = 0
  FOR each sale_date in sales:
    cumulative_sum = cumulative_sum + sale_amount
    Output sale_date, product_id, cumulative_sum

Pseudocode for the rank sales by date:

FOR each product_id in sales:
  FOR each sale_date in sales:
    Calculate the rank of the sale_amount within the product group, ordered by sale_date.
    Output sale_date, product_id, sale_amount, rank
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