Table: Sales
+-------------+-------+
| Column Name | Type |
+-------------+-------+
| sale_id | int |
| product_id | int |
| year | int |
| quantity | int |
| price | int |
+-------------+-------+
(sale_id, year) is the primary key (combination of columns with unique values) of this table.
product_id is a foreign key (reference column) to Product table.
Each row of this table shows a sale on the product product_id in a certain year.
Note that the price is per unit.
Table: Product
+--------------+---------+ | Column Name | Type | +--------------+---------+ | product_id | int | | product_name | varchar | +--------------+---------+ product_id is the primary key (column with unique values) of this table. Each row of this table indicates the product name of each product.
Write a solution to select the product id, year, quantity, and price for the first year of every product sold.
Return the resulting table in any order.
The result format is in the following example.
Example 1:
Input: Sales table: +---------+------------+------+----------+-------+ | sale_id | product_id | year | quantity | price | +---------+------------+------+----------+-------+ | 1 | 100 | 2008 | 10 | 5000 | | 2 | 100 | 2009 | 12 | 5000 | | 7 | 200 | 2011 | 15 | 9000 | +---------+------------+------+----------+-------+ Product table: +------------+--------------+ | product_id | product_name | +------------+--------------+ | 100 | Nokia | | 200 | Apple | | 300 | Samsung | +------------+--------------+ Output: +------------+------------+----------+-------+ | product_id | first_year | quantity | price | +------------+------------+----------+-------+ | 100 | 2008 | 10 | 5000 | | 200 | 2011 | 15 | 9000 | +------------+------------+----------+-------+
This approach involves using an aggregate function to determine the first year a product was sold, and then filtering the sales data to include only those records with the corresponding year. This approach leverages SQL's ability to aggregate and join tables, making it efficient for relational data.
The SQL query first calculates the earliest year of sale for each product ID using a subquery. The result of this subquery is then joined with the Sales table to filter out records that match these years, effectively selecting only the first year's records for each product.
Time Complexity: O(N log N), due to the aggregation and join operations.
Space Complexity: O(N), due to storing intermediate results in the join.
This approach uses a nested query to find the minimum year for each product, which is then used to filter the main sales data. This results in extracting the desired columns for each product's first sale year.
This solution finds the smallest year for each product ID using a subquery and applies an IN clause to filter records in the Sales table. The result is that only records meeting the criteria of being the first sale year for their respective product are included.
Time Complexity: O(N log N), primarily due to the aggregation and filtering operations.
Space Complexity: O(N), as temporary storage is used to hold the results of the subquery.
To solve this problem, we need to extract details for the first year when each product was sold. We can do this by selecting the minimum year for each product from the 'Sales' table and joining it back to the original data to filter the first year details. Using a subquery will help us identify the first sale year for each product.
This SQL solution involves a subquery that finds the minimum year for each product_id by grouping them and calculates the minimum year as the first_year. The main query retrieves sale details matching this first year condition.
Time Complexity: O(n + m), where n is the number of rows in the main table and m in the subquery; Space Complexity: O(n), due to storing results of the distinct years per product.
Another way to solve this problem is by utilizing Common Table Expressions (CTEs). By employing CTEs, we can first compute the minimum year for each product_id and then join this result with the original Sales table to get the desired data for each product's initial sale year.
This SQL solution uses a CTE to first calculate the minimum year for each product_id. Then it joins this result back with the Sales table to filter out the relevant rows corresponding to that first year.
Time Complexity: O(n), due to a single pass over the Sales data; Space Complexity: O(n), mainly due to intermediate CTE storage.
| Approach | Complexity |
|---|---|
| SQL Aggregation and Filtering | Time Complexity: O(N log N), due to the aggregation and join operations. |
| Nested Query and Join | Time Complexity: O(N log N), primarily due to the aggregation and filtering operations. |
| Approach using SQL Subquery | Time Complexity: O(n + m), where n is the number of rows in the main table and m in the subquery; Space Complexity: O(n), due to storing results of the distinct years per product. |
| Approach using SQL Common Table Expression (CTE) | Time Complexity: O(n), due to a single pass over the Sales data; Space Complexity: O(n), mainly due to intermediate CTE storage. |
Product Sales Analysis III | Leetcode 1070 | Crack SQL Interviews in 50 Qs #mysql #leetcode • Learn With Chirag • 5,394 views views
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