Wednesday, March 11, 2026

Similarities and Differences of DML Commands Between PostgreSQL and SQL Server

An Easy-to-Read Essay Using the What, Why, and How Framework

Introduction

Databases play a central role in modern information systems. Businesses, governments, research institutions, and online platforms rely on databases to store and manage large volumes of structured data. To interact with this data—such as inserting new records, retrieving information, updating values, or deleting data—database users rely on Data Manipulation Language (DML) commands.

Two of the most widely used relational database management systems are PostgreSQL and Microsoft SQL Server. Both systems implement SQL (Structured Query Language) and support powerful tools for managing data. While they share many similarities due to the SQL standard, they also include important differences in syntax, performance behavior, and advanced features.

This essay explains the similarities and differences of DML commands between PostgreSQL and SQL Server using a simple and clear framework based on three key questions:

  • What are DML commands in PostgreSQL and SQL Server?

  • Why are DML commands important for database management?

  • How do PostgreSQL and SQL Server implement similar and different DML features?


What Are DML Commands in PostgreSQL and SQL Server?

Understanding Data Manipulation Language

Data Manipulation Language (DML) refers to SQL commands used to retrieve, insert, update, and delete data in database tables.

DML commands interact directly with the data stored inside database objects such as tables and views.

The most common DML commands include:

  • SELECT

  • INSERT

  • UPDATE

  • DELETE

  • MERGE

These commands allow users to manage database records efficiently.


Database Interaction Through DML

DML commands are used by many types of users:

  • database administrators

  • application developers

  • data analysts

  • data engineers

  • business intelligence specialists

Applications send SQL queries to databases, and DML commands retrieve or modify the required data.


Core DML Commands

SELECT

The SELECT command retrieves data from tables.

SELECT queries can filter, sort, and join data from multiple tables.


INSERT

The INSERT command adds new records to a database table.


UPDATE

The UPDATE command modifies existing records.


DELETE

The DELETE command removes records from tables.


MERGE

MERGE combines INSERT, UPDATE, and DELETE operations into one statement.

This command is used in complex data synchronization operations.


Why Are DML Commands Important?

DML commands are essential because they allow users and applications to interact with data stored in databases.


Managing Business Data

Organizations rely on DML commands to manage daily operations.

Examples include:

  • adding new customers

  • updating product inventory

  • retrieving financial transactions

  • deleting outdated records

Without DML commands, databases would only store data without allowing users to interact with it.


Supporting Application Functionality

Every modern application relies on database queries.

Examples include:

  • login systems retrieving user credentials

  • e-commerce systems inserting new orders

  • banking systems updating account balances

  • analytics platforms retrieving historical data

DML commands enable these operations.


Maintaining Data Accuracy

Businesses must maintain accurate and up-to-date information.

DML commands allow administrators to correct errors, update values, and remove obsolete data.


Supporting Data Analytics

Data analysts rely heavily on SELECT queries to analyze business data.

Examples include:

  • sales reports

  • customer behavior analysis

  • financial forecasting

  • operational monitoring

DML commands enable these analytical operations.


Enabling Data Integration

Organizations often integrate data from multiple systems.

DML commands allow databases to insert and update records during data integration processes.


How PostgreSQL and SQL Server Implement DML Commands

Although PostgreSQL and SQL Server follow the SQL standard, their implementations differ in certain areas.


SELECT Command

Similarities

Both PostgreSQL and SQL Server support the SELECT command for retrieving data.

Common SELECT features include:

  • filtering with WHERE clauses

  • sorting with ORDER BY

  • grouping with GROUP BY

  • joining multiple tables

  • aggregating data using functions such as COUNT, SUM, and AVG

These features allow complex queries to retrieve meaningful information from databases.


Differences

While SELECT syntax is mostly similar, there are some differences.

Limiting Results

PostgreSQL uses the LIMIT clause.

SQL Server traditionally used TOP, although modern SQL Server also supports OFFSET and FETCH.

Example concepts:

  • PostgreSQL: LIMIT number of rows

  • SQL Server: TOP clause in SELECT statement


Case Sensitivity

PostgreSQL is more strict with case sensitivity when identifiers are quoted.

SQL Server generally handles identifiers in a case-insensitive way depending on collation settings.


INSERT Command

The INSERT command adds new records to database tables.


Similarities

Both PostgreSQL and SQL Server support inserting records using similar syntax.

Developers specify:

  • table name

  • column list

  • values to insert

Both databases also support inserting multiple rows in a single statement.


Differences

Returning Inserted Values

PostgreSQL supports the RETURNING clause.

This allows queries to return inserted rows immediately.

SQL Server uses the OUTPUT clause to achieve similar functionality.


Default Values

Both databases support default column values.

However, syntax and behavior may vary slightly.


UPDATE Command

The UPDATE command modifies existing records.


Similarities

Both PostgreSQL and SQL Server support:

  • updating specific columns

  • filtering rows using WHERE conditions

  • updating multiple rows at once

This allows administrators to modify database records efficiently.


Differences

Updating with Joins

PostgreSQL and SQL Server handle joins in UPDATE statements differently.

PostgreSQL often uses a FROM clause.

SQL Server uses JOIN syntax directly inside UPDATE statements.


Expression Handling

Both systems support complex expressions, but certain functions may differ between platforms.


DELETE Command

The DELETE command removes rows from tables.


Similarities

Both databases support deleting records using conditions.

Administrators typically specify a WHERE clause to control which rows are removed.

Both systems also support deleting all rows from a table.


Differences

Deleting with Joins

PostgreSQL uses a USING clause for join-based deletes.

SQL Server allows joins directly within DELETE statements.


Performance Behavior

Query execution plans may differ between PostgreSQL and SQL Server due to differences in query optimizers.


MERGE Command

The MERGE command combines multiple operations.


SQL Server MERGE

SQL Server introduced MERGE earlier and provides extensive support for it.

MERGE is commonly used for:

  • data synchronization

  • ETL processes

  • data warehousing


PostgreSQL UPSERT

PostgreSQL uses INSERT ... ON CONFLICT for similar functionality.

This feature allows records to be inserted or updated depending on whether a conflict occurs.

This capability is often referred to as UPSERT.


Transaction Management

DML commands operate within database transactions.

Transactions ensure that data modifications occur safely.


Similarities

Both PostgreSQL and SQL Server support:

  • BEGIN transactions

  • COMMIT operations

  • ROLLBACK operations

These mechanisms ensure ACID compliance.

ACID stands for:

  • Atomicity

  • Consistency

  • Isolation

  • Durability


Differences

PostgreSQL uses MVCC (Multi-Version Concurrency Control) to manage transactions.

SQL Server supports both locking and snapshot isolation mechanisms.

These architectural differences affect concurrency and performance behavior.


Query Optimization

Both databases include powerful query optimizers.


PostgreSQL Query Optimizer

PostgreSQL uses a cost-based optimizer that selects query execution plans based on estimated resource usage.


SQL Server Query Optimizer

SQL Server also uses a cost-based optimizer but integrates deeply with its indexing and caching systems.


Stored Procedures and DML

Both systems support stored procedures that contain DML operations.

Stored procedures allow developers to automate complex database tasks.


Performance Considerations

Database performance depends on several factors including:

  • indexing strategies

  • query design

  • hardware resources

  • database configuration

Both PostgreSQL and SQL Server provide tools for analyzing query performance.


Best Practices When Using DML Commands

Database administrators should follow several best practices.


Use Indexes Carefully

Indexes improve query performance but may slow down insert and update operations.


Avoid Large Transactions

Large transactions may lock resources and impact performance.


Optimize Queries

Efficient query design improves database performance.


Use Parameterized Queries

Parameterized queries improve security and performance.


Monitor Query Performance

Monitoring tools help identify slow queries and optimize database operations.


Future Trends in Database Data Manipulation

Database technologies continue to evolve.

Modern trends include:

  • cloud-native databases

  • distributed query processing

  • real-time data analytics

  • automated query optimization

  • AI-assisted database tuning

Both PostgreSQL and SQL Server continue to adapt to these technological changes.


Conclusion

Data Manipulation Language commands are essential tools for interacting with database data. Both PostgreSQL and SQL Server provide powerful DML capabilities that allow users to retrieve, insert, update, and delete records efficiently.

Although the two systems share many similarities due to their adherence to SQL standards, they also include important differences in syntax, advanced features, and architectural design. PostgreSQL emphasizes flexibility, extensibility, and open-source innovation, while SQL Server integrates closely with enterprise technologies and Microsoft ecosystems.

Understanding the similarities and differences between DML commands in PostgreSQL and SQL Server helps developers build better applications, migrate databases more effectively, and optimize data operations. As organizations continue to rely on data-driven systems, mastering DML commands in both platforms will remain an essential skill for database professionals and software engineers.

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