Remove Duplicates in SQL | 3 Easy Ways (with code)

Remove Duplicates in SQL | 3 Easy Ways (with code)

SQL adventurers! If you’ve ever stumbled upon the treacherous terrain of relational databases, you know the pain of encountering those pesky duplicates – the sneaky culprits causing chaos in your data paradise. Fear not, for we’re here with a friendly map to guide you through the jungle of deduplication!

In this delightful expedition, we’ll unravel the mystery behind duplicate data and explore not one, not two, but several methods to bid them farewell in SQL. It’s not just about the how; we’ll embark on a journey through scenarios – from clearing out duplicates to preserving the chosen ones and even keeping the newest duplicates as VIPs.

Join us as we transform the daunting task of deduplication into a friendly conversation. No more hurdles, just a fun-filled ride through the realm of SQL magic. Let’s dive into the adventure and make those duplicates vanish like magic tricks! 🎩🐇✨

A. Brief Overview of the Importance of Removing Duplicates in SQL

Duplicate data in SQL databases can wreak havoc on the integrity, reliability, and efficiency of your data management system. Here’s why removing duplicates is crucial:

  1. Data Accuracy:

    • Duplicates can lead to inaccuracies in your analyses and reports, providing a distorted view of your data.
    • Ensuring data accuracy is fundamental for making informed business decisions.
  2. Resource Optimization:

    • Duplicate records consume unnecessary storage space, impacting the overall performance of your database.
    • Removing duplicates helps optimize storage and improves query performance.
  3. Maintaining Data Integrity:

    • Duplicate data can introduce inconsistencies and errors, compromising the reliability and trustworthiness of your database.
    • A clean dataset ensures better data integrity, crucial for any data-driven application.
  4. Improved Query Performance:

    • Queries become more efficient when executed on a dataset free of duplicates, leading to faster response times.
    • Removing duplicates enhances the overall performance of SQL queries.

B. Explanation of the Potential Issues Caused by Duplicate Data

  1. Data Redundancy:

    • Duplicate data introduces redundancy, leading to an inefficient use of storage space and complicating data maintenance.
  2. Inaccurate Analysis:

    • Analysis and reporting based on duplicate-laden data can result in misleading insights, impacting the decision-making process.
  3. Update Anomalies:

    • Duplicates can cause challenges when updating records, leading to anomalies where changes made to one duplicate may not reflect in others.
  4. Join Operation Challenges:

    • Joining tables with duplicate records can lead to unpredictable results and increased complexity in query construction.
  5. Data Consistency Issues:

    • Duplicates can create inconsistencies in data, making it difficult to establish a single source of truth and maintain a consistent dataset.

C. Overview of the Three Methods Covered in the Tutorial

  1. Method 1: Using DISTINCT Keyword

    • Description: The DISTINCT keyword is used to retrieve unique values from a specified column. It’s a straightforward method for eliminating duplicate entries.
    • Use Cases: Suitable for scenarios where you want a quick and simple way to retrieve distinct values.
  2. Method 2: Using GROUP BY and HAVING

    • Description: The GROUP BY clause groups rows that have the same values in specified columns, and HAVING filters grouped data based on specified conditions. This method is effective for managing duplicates within grouped sets.
    • Use Cases: Ideal for scenarios where you need to perform operations on groups of duplicate records.
  3. Method 3: Using ROW_NUMBER() and Common Table Expressions (CTEs)

    • Description: The ROW_NUMBER() function assigns a unique integer to each row, and CTEs help organize complex queries. This method is powerful for scenarios where you need to prioritize and keep specific instances of duplicates.
    • Use Cases: Useful when you want more control over the selection and retention of duplicate records, such as keeping the most recent ones.

Method 1: Using DISTINCT keyword

A. Explanation of the DISTINCT Keyword

The DISTINCT keyword in SQL is used within a SELECT statement to retrieve unique values from a specified column or a combination of columns. It ensures that the result set contains only distinct (unique) values, eliminating duplicate entries. It’s a straightforward and commonly used method for simplifying datasets and obtaining a clearer view of the unique values within a particular column.

B. Syntax for Using DISTINCT to Eliminate Duplicates

The basic syntax for using the DISTINCT keyword is incorporated into the SELECT statement. The general form is as follows:

SELECT DISTINCT column1, column2, ...
FROM table_name;
  • SELECT DISTINCT: Specifies that only distinct values should be returned.
  • column1, column2, …: Columns for which unique values are desired.
  • FROM table_name: Specifies the table from which the data is selected.

C. Code Example with a Simple SELECT Statement

Let’s consider a hypothetical scenario where we have a “users” table with a “country” column, and we want to retrieve a list of unique countries from this table:

-- Example SQL Query using DISTINCT
FROM users;

In this example, the result set will contain a list of unique countries found in the “users” table, eliminating any duplicate country entries.

D. Limitations and Considerations When Using DISTINCT

While DISTINCT is a powerful and easy-to-use tool, it comes with some limitations and considerations:

  1. Limited to Single Columns:

    • DISTINCT is typically applied to individual columns, and it may not be suitable for scenarios where you need unique combinations of multiple columns.
  2. Complete Row Retrieval:

    • DISTINCT operates at the column level, meaning that if there are duplicates in one column but differences in other columns, all the rows will be considered distinct.
  3. Performance Impact:

    • Using DISTINCT can have performance implications, especially on large datasets, as it requires the database engine to sort and filter the result set.
  4. No Control over Which Duplicate is Retained:

    • DISTINCT does not provide control over which specific duplicate value is retained in the result set. It simply ensures that one of them is included.
  5. Compatibility with NULL Values:

    • DISTINCT treats NULL values as distinct from each other. It may not always yield the expected results when dealing with columns that contain NULL values.

Method 2: Using GROUP BY and HAVING

A. Introduction to GROUP BY Clause

The GROUP BY clause in SQL is used to arrange identical data into groups based on specified columns. It transforms a set of rows with duplicate values in certain columns into a summarized result set where each group is represented by a single row.

B. Explanation of HAVING Clause for Filtering Grouped Data

The HAVING clause is often used in conjunction with the GROUP BY clause. While the WHERE clause filters rows before grouping, the HAVING clause filters groups after they have been formed. It is particularly useful for specifying conditions on aggregated data, allowing you to filter the results based on the results of aggregate functions (e.g., COUNT, SUM).

C. Code Example Demonstrating GROUP BY and HAVING for Duplicate Removal

Consider a scenario where we have a “sales” table with columns “product_id” and “quantity_sold.” We want to find products that have more than one sale, along with the total quantity sold for each product. Here’s an example query:

-- Example SQL Query using GROUP BY and HAVING
SELECT product_id, COUNT(*) as sale_count, SUM(quantity_sold) as total_quantity_sold
FROM sales
GROUP BY product_id

In this example, the result set will display product IDs, the count of sales for each product, and the total quantity sold, but only for products with more than one sale.

D. Comparison with DISTINCT Method and When to Choose Each Approach


    • DISTINCT: Used for retrieving unique values from individual columns.
    • GROUP BY: Used for aggregating data and creating summary results based on one or more columns.
    • Choose DISTINCT when: You want to eliminate duplicate values in one or more columns without aggregation.
    • Choose GROUP BY when: You need to aggregate data based on specific columns and perform calculations on those groups.
  • When to Choose GROUP BY and HAVING:

    • Use GROUP BY and HAVING when dealing with scenarios that require aggregation, such as counting occurrences or calculating sums, and you need to filter groups based on aggregated results.
    • Ideal for situations where you want to work with groups of duplicate records and apply conditions to those groups.

Method 3: Using ROW_NUMBER() and Common Table Expressions (CTEs)

A. Overview of ROW_NUMBER() Function

The ROW_NUMBER() function in SQL assigns a unique integer to each row within a result set based on the specified column’s order. It is often used in conjunction with the ORDER BY clause to establish a ranking or numbering for rows. This function becomes powerful when combined with Common Table Expressions (CTEs) for more complex queries.

B. Explanation of Common Table Expressions (CTEs)

A Common Table Expression (CTE) is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It is defined using the WITH keyword and allows for the creation of named, reusable subqueries. CTEs enhance the readability and maintainability of complex SQL queries.

C. Code Example Using ROW_NUMBER() and CTEs to Remove Duplicates

Imagine a scenario where you have a “customer_orders” table with duplicate entries for orders, and you want to keep only the most recent order for each customer. Here’s an example query:

-- Example SQL Query using ROW_NUMBER() and CTEs
WITH RankedOrders AS (
        ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) as row_num
    FROM customer_orders
DELETE FROM RankedOrders WHERE row_num > 1;

In this example, the CTE (RankedOrders) uses ROW_NUMBER() to assign a unique number to each row within each customer’s partition (grouped by customer_id) based on the order_date in descending order. The DELETE statement then removes all rows except the ones with row_num equal to 1, effectively keeping only the most recent orders for each customer.

D. Advantages and Scenarios Where This Method Is Preferred

  • Advantages:

    • Precision in Duplicate Selection: ROW_NUMBER() provides control over which duplicate record is retained, allowing you to choose based on specific criteria.
    • Flexibility in Conditions: CTEs allow you to define complex queries in a more modular and readable way, making it easier to manage and understand.
  • Scenarios Where This Method Is Preferred:

    • Retaining Specific Instances: When you need to keep a particular instance of duplicate records, such as the most recent or the least costly.
    • Multiple Criteria: When deduplication involves multiple columns or complex conditions that cannot be easily addressed with DISTINCT or GROUP BY methods.
    • Data Modification: When you need to delete, update, or insert records based on a ranking or numbering scheme.

This method is preferred in scenarios where fine-grained control over duplicate removal is essential and when the criteria for selecting duplicates are more intricate. ROW_NUMBER() combined with CTEs provides a powerful and flexible approach to handling duplicate data in SQL.


A. Summary of the Three Methods

In summary, we explored three effective methods for removing duplicates in SQL:

  1. Using DISTINCT Keyword:

    • Straightforward method for obtaining unique values from a specific column.
    • Ideal for simple deduplication without the need for aggregation.
  2. Using GROUP BY and HAVING:

    • Involves grouping data based on specified columns and applying conditions using the HAVING clause.
    • Suitable for scenarios requiring aggregation and filtering of grouped data.
  3. Using ROW_NUMBER() and Common Table Expressions (CTEs):

    • Utilizes the ROW_NUMBER() function for assigning unique numbers to rows.
    • Employs CTEs for creating temporary result sets and enhancing query readability.
    • Offers precision in selecting specific instances of duplicate records.

B. Considerations for Choosing the Appropriate Method

  • Nature of Data:

    • Choose DISTINCT for simple deduplication.
    • Choose GROUP BY and HAVING for grouped data scenarios requiring aggregation.
    • Choose ROW_NUMBER() and CTEs for fine-grained control over duplicate selection based on specific criteria.
  • Complexity of Scenarios:

    • Evaluate the complexity of your deduplication requirements.
    • DISTINCT is suitable for straightforward cases, while GROUP BY and ROW_NUMBER() are preferable for more intricate scenarios.

C. Best Practices for Maintaining Data Integrity in SQL Databases

  • Regular Audits:

    • Conduct regular audits to identify and address duplicates.
    • Establish automated processes for ongoing data cleanliness.
  • Data Validation:

    • Implement data validation rules to prevent the insertion of duplicate records.
    • Leverage constraints and unique indexes where applicable.
  • Documentation:

    • Document deduplication processes for future reference and collaboration.
    • Include information on chosen methods, criteria for duplicate selection, and any constraints applied.

VII. Additional Tips and Tricks

A. Handling Duplicates in Specific Columns

  • Use Conditional Logic:
    • Apply conditional logic within your chosen method to handle duplicates based on specific column values.

B. Dealing with Large Datasets Efficiently

  • Indexing:
    • Utilize indexes on columns involved in duplicate removal to enhance query performance.
    • Evaluate and optimize the indexing strategy for large datasets.

C. Performance Considerations for Each Method

  • Evaluate Query Execution Plans:
    • Understand the execution plans generated by the database engine for each deduplication method.
    • Optimize queries based on execution plan insights.

VII. Recap and Next Steps

A. Review Key Points from Each Method

  • Recap the key concepts and syntax for each deduplication method.
  • Highlight the strengths and limitations of each approach.

B. Encourage Hands-on Practice with Sample Datasets

  • Provide sample datasets for readers to practice implementing each deduplication method.
  • Encourage experimentation with different scenarios to deepen understanding.

C. Suggest Additional Resources for Further Learning and Exploration

  • Offer links to relevant documentation, tutorials, and courses for readers interested in further exploration.
  • Suggest community forums or platforms where readers can engage with others and seek assistance.

By following these conclusions and additional tips, readers will be well-equipped to navigate the world of duplicate removal in SQL and apply the most suitable method for their specific scenarios.

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