Efficient Data Deduplication in SQL

Data deduplication is a crucial process for maintaining data integrity and optimizing storage. This challenge asks you to implement a SQL query that identifies and removes duplicate rows from a given table, ensuring that only unique records remain. This is vital for accurate reporting, efficient data analysis, and reduced storage costs.

Problem Description

You are given a table named Customers with the following columns: CustomerID, FirstName, LastName, Email, and City. Your task is to write a SQL query that identifies and removes duplicate rows based on the combination of FirstName, LastName, and Email. The CustomerID is assumed to be a unique identifier and should be preserved. The query should return a result set containing only the unique rows, effectively deduplicating the data. If multiple rows have the same FirstName, LastName, and Email, only one should remain. The specific row kept among duplicates is not important; any one of the duplicates is acceptable.

Key Requirements:

• Identify duplicate rows based on FirstName, LastName, and Email.
• Remove duplicate rows, retaining only one instance of each unique combination.
• Preserve the CustomerID for the remaining unique rows.
• The query should be efficient and performant, especially for large datasets.

Expected Behavior:

The query should return a table with the same columns as the original Customers table, but with all duplicate rows removed based on the specified criteria. The CustomerID should be maintained for the remaining unique rows.

Edge Cases to Consider:

• Empty table: The query should return an empty result set.
• No duplicates: The query should return the entire original table.
• All rows are duplicates: The query should return a single row with the first encountered CustomerID for that unique combination.
• Null values in FirstName, LastName, or Email: Null values should be treated as equal for the purpose of deduplication.

Examples

Example 1:

Input:
Customers Table:
CustomerID | FirstName | LastName | Email | City
----------|-----------|----------|-------|-------
1          | John      | Doe      | john.doe@example.com | New York
2          | Jane      | Smith    | jane.smith@example.com | London
3          | John      | Doe      | john.doe@example.com | Chicago
4          | Peter     | Jones    | peter.jones@example.com | Paris
5          | Jane      | Smith    | jane.smith@example.com | Berlin

Output:
CustomerID | FirstName | LastName | Email | City
----------|-----------|----------|-------|-------
1          | John      | Doe      | john.doe@example.com | New York
2          | Jane      | Smith    | jane.smith@example.com | London
4          | Peter     | Jones    | peter.jones@example.com | Paris

Explanation: Rows with CustomerID 3 and 5 are duplicates of rows 1 and 2 respectively, based on FirstName, LastName, and Email.  Only one instance of each unique combination is retained.

Example 2:

Input:
Customers Table:
CustomerID | FirstName | LastName | Email | City
----------|-----------|----------|-------|-------
1          | John      | Doe      | john.doe@example.com | New York
2          | John      | Doe      | john.doe@example.com | Chicago
3          | John      | Doe      | john.doe@example.com | London

Output:
CustomerID | FirstName | LastName | Email | City
----------|-----------|----------|-------|-------
1          | John      | Doe      | john.doe@example.com | New York

Explanation: All rows are duplicates. Only the first encountered row (CustomerID 1) is retained.

Example 3: (Edge Case - Empty Table)

Input:
Customers Table: (Empty)

Output:
(Empty Result Set)

Explanation:  The query should gracefully handle an empty table and return an empty result set.

Constraints

• The table Customers will contain at least one row.
• The CustomerID column is guaranteed to be unique.
• The table may contain a large number of rows (up to 1,000,000). Performance is a consideration.
• The database system is assumed to be a standard SQL implementation (e.g., MySQL, PostgreSQL, SQL Server). The solution should be as portable as possible.
• The FirstName, LastName, Email, and City columns are of standard string data types (e.g., VARCHAR, TEXT).

Notes

Consider using window functions (e.g., ROW_NUMBER()) or subqueries to efficiently identify and remove duplicate rows. The goal is to find a solution that is both correct and performs well, especially when dealing with large datasets. Think about how to minimize the number of table scans and comparisons required. Different SQL dialects may have slightly different syntax for window functions or other features; aim for a solution that is broadly compatible. The specific CustomerID that is retained when duplicates exist is not important.