Recyclable and Low Fat Products | Leetcode 1757

Welcome to the inaugural chapter of the Crack SQL Interview in 50 Questions series. This guide covers Leetcode Problem 1757: Recyclable and Low Fat Products, establishing fundamental SQL querying patterns including data selection, table schemas, logical filtering operators, and production database constraints.

1. Introduction to the 50-Question SQL Interview Series

• Series Objective: Master 50 high-frequency SQL interview questions curated to bridge basic concepts and intermediate-to-advanced database operations.
• Comprehensive Coverage: The curriculum spans standard SELECT statements, JOIN patterns, basic aggregate functions, sorting, grouping, subqueries, string manipulation, and Regular Expressions (REGEXP).
• Pedagogical Workflow: Every question is tackled using a strict three-step framework:
  1. Deconstruct the Problem: Analyze the table schema, constraints, and exact output requirements.
  2. Formulate the Query: Build the SQL query incrementally from core clauses (SELECT, FROM) to conditional filters (WHERE).
  3. In-Depth Explanation: Analyze the execution logic, keyword semantics, and underlying database theory.

2. Understanding the Problem Statement

The goal of Leetcode 1757 is to query a database table containing grocery/retail product attributes and filter out items that meet two distinct environmental and health criteria.

• Table Name: Products
• Business Logic: Find the unique identifiers (product_id) of products that are both categorized as low-fat and recyclable.

3. Database Schema & Data Types

Before writing any query, inspecting the table schema is critical to understanding how data is stored, indexed, and retrieved.

Key Database Concepts Explained

• Columns vs. Attributes: In relational databases, columns are synonymous with attributes (vertical dimensions of a relation).
• Primary Key Constraint: A PRIMARY KEY is a column (or set of columns) that uniquely identifies every row in a table. It inherently enforces two rules:
  1. Uniqueness: No two rows can share the same primary key value.
  2. Not Null: A primary key cannot contain NULL values.
• ENUM Data Type: An ENUM (Enumeration) is a string data type whose value is restricted to a list of permitted values defined explicitly at table creation (in this case, 'Y' or 'N').

4. Input vs. Expected Output Example

Let's examine how raw database rows translate into filtered query results.

Sample Input (Products Table)

Expected Output

Note: Only product_id 1 and 3 have 'Y' for both low_fats and recyclable. Products 2, 4, and 5 fail at least one condition.

5. Step-by-Step Query Construction

To retrieve the desired product IDs, we construct our SQL query using three core clauses: SELECT, FROM, and WHERE.

Step 5.1: Selecting the Target Column

We only need to return the product IDs, not the entire row of data. Therefore, we use the SELECT keyword followed by the target attribute name.

SELECT product_id

Step 5.2: Specifying the Source Table

We must tell the database engine which relation (table) contains our target attribute using the FROM keyword.

SELECT product_id
FROM Products

Step 5.3: Applying Conditional Filters

We only want rows where both the low_fats flag and the recyclable flag equal 'Y'. We use the WHERE clause combined with the logical AND operator.

SELECT product_id
FROM Products
WHERE low_fats = 'Y' AND recyclable = 'Y';

6. Complete Query Analysis

Let's dissect every component of our final production-ready query:

-- Retrieve products that meet both dietary and environmental standards
SELECT product_id
FROM Products
WHERE low_fats = 'Y' 
  AND recyclable = 'Y';

Execution Breakdown

1. SELECT product_id: Instructs the SQL engine to project only the product_id column in the final result set.
2. FROM Products: Identifies the base table scanned by the database engine.
3. WHERE low_fats = 'Y': Filters rows, retaining only those where the product is low-fat.
4. AND: A logical operator ensuring both conditions must evaluate to true simultaneously.
5. recyclable = 'Y': Further filters the subset to ensure the product is also recyclable.

7. Summary and Key Takeaways

• Structured Problem Solving: Always begin SQL problem-solving by analyzing table schemas, data types, and primary key constraints.
• The SELECT Clause: Used to specify the exact columns (attributes) you want to project in your output.
• The FROM Clause: Specifies the table or view from which the database engine retrieves records.
• The WHERE Clause: Acts as a row-level filter, screening out records that do not match specified boolean conditions.
• Logical Operators (AND): Combine multiple conditions where all criteria must be satisfied for a row to be included in the result set.
• Primary Keys: Ensure row-level uniqueness and serve as the foundational indexing mechanism in relational databases.
• ENUM Constraints: Restrict column values to a predefined set of strings, maintaining data integrity for flags like 'Y' and 'N'.
• Iterative Testing: Writing baseline queries, verifying test cases, and running submissions builds systematic proficiency for technical interviews.