PostgreSQL vs. SQL Server: What to Choose?

Alexandr Kirpichny

Chief Product Officer, Ispirer Systems

Among the variety of databases available on the market, PostgreSQL and SQL Server are among the most popular. Many organizations choose SQL Server because of the use of a large number of Microsoft products. However, in pursuit of the advantages of an open-source system and extensive capabilities, many organizations choose PostgreSQL. But how to choose the right database for your business?

This article will discuss the benefits of both systems and compare them based on important business criteria. Ready? Let’s start.

What is PostgreSQL?

PostgreSQL is an open-source, object-oriented database system. This advanced database supports JSON (non-relational) and SQL (relational) querying. It is used to handle and store data for business intelligence, data analytics, and machine learning services in enterprises.

PostgreSQL is known for its ability to handle complex data types effectively. It also performs well when compared to commercial databases like Microsoft SQL Server and Oracle. Aside from being free and open-source, PostgreSQL also offers a great deal of extensibility. You can create custom functions and data types, and write code in different programming languages without recompiling your database.

Here is a brief history of PostgreSQL:

• In 1986, Postgres was created by Michael Stonebraker and his team.
• By 1990, the developers had enhanced PostgreSQL with support for PL/pgSQL and ACID compliance.
• The first open-source RDBMS, Postgres95, emerged, evolving into PostgreSQL.
• In the 2000s PostgreSQL introduced advanced features, like point-in-time recovery and improved Windows support.
• In 2012 Version 9.2 added native support for JSON data types, aligning with modern application needs.
• In 2014, the PGconf marked the beginning of a fresh chapter for PostgreSQL users.

PostgreSQL is an excellent database that is a perfect fit for a number of use cases:

• Machine Learning Platforms. PostgreSQL is less common in machine learning compared to other databases. However, it is capable of storing and handling metadata, configuration settings, and non-performance-critical data in machine learning workflows.
• Content Management Systems (CMS). CMS platforms such as WordPress, Drupal, and Joomla often use PostgreSQL as a backend database to store content, user data, and configurations.
• Compatibility with Diverse Web Frameworks. PostgreSQL works fine with all modern web frameworks including but not limited to: Django (Python), node.js (JavaScript), Hibernate (Java), Ruby on rails, PHP, and a lot more. Due to PostgreSQL’s replication capabilities, websites can easily be scaled out to as many database servers as you need.
• Financial services. PostgreSQL is fully ACID compliant and therefore ideal for OLTP (Online Transaction Processing) workloads. PostgreSQL is used in finance for storing transactions, managing accounts, and supporting financial apps.

What is SQL Server?

Microsoft SQL Server is a database system that stores and manages data. It has strong analysis and reporting features. SQL Server supports ANSI SQL, which is the standard SQL (Structured Query Language) language.

However, SQL Server comes with its own implementation of the SQL language, T-SQL (Transact-SQL). T-SQL is a Microsoft proprietary language known as Transact-SQL. It provides further capabilities of declaring variables, exception handling, stored procedure, etc.

Brief history of MSSQL:

• Microsoft SQL Server started in 1989 as SQL Server 1.0, a collaboration between Sybase and Microsoft.
• Microsoft released SQL Server 6.0 in 1994, marking their independent development of the database system, separate from Sybase.
• SQL Server's integration with Windows OS boosted its popularity, especially with releases like SQL Server 2000 and 2005.
• SQL Server 2016 allows easy connection between local databases and Microsoft Azure cloud services.
• A major shift occurred with SQL Server 2017, which added support for Linux.

Various cases use SQL Server, making it one of the most popular databases on the market. Here are the main ones:

• Enterprise Resource Planning Systems. SQL Server is used for storing and managing data for ERP systems. It handles various business processes such as finance, HR, and supply chain management.
• Customer Relationship Management Systems. CRM applications use SQL Server to store and manage customer information, interactions, and sales data.
• Business Intelligence and Analytics. SQL Server provides robust support for business intelligence and analytics applications, facilitating data warehousing, reporting, and analysis.
• E-commerce Platforms. E-commerce applications commonly use SQL Server to manage product information, inventory, and handle secure transactions.

PostgreSQL vs. SQL Server: a detailed comparison

Let's begin with the fundamentals and contrast PostgreSQL with SQL Server. The elements outlined below can aid you in identifying the most appropriate database management system for your needs.

Platform support

As an open-source platform, PostgreSQL lends its support to a variety of major operating systems. It finds compatibility across Linux, macOS, Windows, BSD, and Solaris. Deployment options extend beyond traditional setups to encompass Docker containers and Kubernetes environments.

As for SQL Server, it supports such operating systems as Microsoft Windows, or Linux.

RDBMS vs. ORDBMS

Let's start by figuring out these terms.

RDBMS is a relational database management system based on a traditional relational model of data. Such databases store data in tables and connect these tables to each other using constraints.

ORDBMS (Object Relational Database Management System) is an improved version that uses an Object-Oriented Database Management System. ORDBMS is a DBMS that allows using OO programming language techniques in data management.

While there are advantages such as improved performance and reduced costs, there are also disadvantages to consider. These include additional expenses for computing and the possibility of design errors and inconsistent data. ORDBMS provides solutions to these issues.

MSSQL Server is a database system that is good for managing and processing data in traditional applications. Conversely, PostgreSQL is ORDBMS which perfectly handles complex objects. For example, it can easily handle various types of data such as audio, video, and images. However, RDBMS is not suitable for storing these files because they can negatively impact performance.

Architecture

PostgreSQL, a powerful open-source relational database management system, distinguishes itself through its utilization of the PL/pgSQL procedural programming language. This enhances standard SQL with user-friendly features, including user-defined types, custom modules, extensions, JSON support, and advanced options for triggers.

In terms of system architecture, PostgreSQL adopts a unique strategy where processes are treated as isolated OS processes. Each database operates within its dedicated memory space, simplifying monitoring and management. However, scaling multiple databases may present challenges due to the isolated nature of their processes.

SQL Server, on the other hand, employs T-SQL, a language extending standard SQL with advanced features. This includes enhanced support for data manipulation, string processing, procedural programming, and the use of local variables.

The system architecture of SQL Server revolves around a buffer pool mechanism, adjustable to accommodate varying processing requirements. This pool serves as temporary storage for frequently accessed data, optimizing query performance. Additionally, SQL Server introduces features for high availability and disaster recovery, such as Always On Availability Groups, offering improved resilience through database replication across multiple instances.

SQL

PostgreSQL extends its SQL capabilities by embracing PL/pgSQL, providing a procedural programming language that enriches the overall user experience. Beyond standard SQL, PostgreSQL supports user-defined types, custom modules, extensions, and JSON. Additionally, it offers advanced options for triggers, enabling users to implement complex functionalities seamlessly. On the other side, SQL Server employs T-SQL to enhance data manipulation, string processing, and procedural programming within the SQL Server environment. This extension of standard SQL allows for a more sophisticated approach to data handling, offering users a comprehensive set of tools.

Tables

PostgreSQL introduces support for temporary tables, a valuable feature for handling interim results from intricate logic and complex procedures. This capability enhances the efficiency of managing branched complex logic within the database, contributing to improved organization and performance. Temporary tables serve as a crucial tool for segregating intermediary information from essential data.

SQL Server tables are contained within database object containers that are called Schemas. The schema also works as a security boundary, where you can limit database user permissions to be on a specific schema level only. You can imagine the schema as a folder that contains a list of files. When you design a database table, the properties that are assigned to the table and the columns within the table will control the allowed data types and data ranges that the table accepts. Proper table design will make it easier and faster to store data into and retrieve data from the table.

Partitioning and sharding

When striving for enhanced performance in handling extensive databases, and your stored procedures have reached their limits, coupled with a hardware upgrade, the imperative is to distribute the workload across diverse servers. This is where the strategic implementation of partitioning and sharding becomes pivotal.

While both partitioning and sharding involve the segmentation of a voluminous dataset into more manageable subsets, sharding specifically denotes the dispersion of data across multiple computers, whereas partitioning does not necessarily imply this distributed aspect.

PostgreSQL introduces declarative partitioning support starting from version 10.0, offering the flexibility of partitioning by range, list, or hash.

What concerns MS SQL Server, horizontal partitioning takes precedence, involving the division of a table with numerous rows into multiple tables with fewer rows. Additionally, MS SQL Server facilitates sharding through federation. The concept of "Federated Partitioned Views" entails the distribution of tables across distinct servers to optimize processing load equilibrium.

For retrieving records from these servers, specific commands known as distributed partitioned views come into play. Leveraging typical SQL statements, augmented by the UNION keyword, these commands efficiently draw data from all distributed servers.

Moreover, Data Manipulation Language (DML) statements such as INSERT, UPDATE, and DELETE can be harnessed by adhering to prescribed rules on the underlying tables. It's important to highlight that only enterprise editions support federated partitioned views.

While the implementation of federated partitioned views is plausible on other editions, the absence of a distinct syntax prevents their recognition as such. The rules designating a view as partitioned across servers are exclusively accessible with enterprise editions.

Employing this partitioning technique typically results in a performance boost ranging from 20% to 30% in most applications, making it an invaluable tool, particularly for businesses dealing with substantial volumes of data.

Indexes

Indexes are the all important database object that makes it much easier to search for data in the database without having to search for a specific row. Indexes can also be utilized to reference multiple columns or rows. The same index can be assigned to files, displayed in various locations within the database, and all these fragments can be gathered using a single search. Therefore, indexes are a litmus test for the usability of the database.

PostgreSQL provides a diverse range of index types, giving users flexibility in optimizing query performance. These include B-tree, hash, Generalized Search Tree (GiST), Space Partitioned GiST, Generalized Inverted Index (GIN), and Block Range Index (BRIN). Moreover, PostgreSQL supports expression indexes, allowing the creation of indexes based on expressions or functions rather than simple column values. The option of partial indexes is also available, enabling the indexing of specific segments of a table, which can be advantageous for optimizing certain queries.

In contrast, SQL Server employs a more streamlined approach to indexing, offering clustered and non-clustered indexes. Clustered indexes organize the actual data rows within the table or view based on key values, effectively determining the physical order of the data. Notably, each table can have only one clustered index. On the other hand, non-clustered indexes store their key values separately from the table data, with each entry containing a pointer to the corresponding data.

SQL Server automatically generates non-clustered indexes when defining PRIMARY KEY or UNIQUE constraints on table columns. Specifically, the UNIQUE constraint results in a non-clustered index, while the PRIMARY KEY constraint leads to the creation of a clustered index unless one is already present. This indexing strategy provides a balance between simplicity and efficiency in managing data retrieval in SQL Server.

Replication

Considering that replication is of utmost importance for data security, PostgreSQL provides several options for database replication:

1. Streaming replication involves continuously copying the changes (transactions) happening on a primary PostgreSQL database to one or more standby (replica) databases. How does it work? It uses the streaming mechanism to ship changes from the primary to the replica in real-time.

2. Physical replication involves copying the entire data files or blocks from the primary server to the replica servers.

3. Logical replication operates on a publish-subscribe framework. Alterations are determined by the data replication's identity, such as its primary key, instead of its physical position.

SQL Server replication copies data from a publishing server to a subscribing one. It can be either synchronous or asynchronous, contingent on the SQL Server version. It provides three kinds of replications, specifically: transactional replication, snapshot replication, and merge replication.

Transactional replication is typically employed in server-to-server configurations, where modifications are transferred from the publisher to the subscriber in real-time. This ensures real-time updates between the servers.

Merge replication is employed in scenarios where conflicts may arise. It's suitable for server-to-client environments or situations where data can be modified and tracked on both the subscriber and publisher, with subsequent synchronization resolving any discrepancies.

Snapshot replication is chosen when data changes infrequently, doesn't require incremental updates, or needs to be duplicated exactly as it appears at a specific point in time.

Scalability

PostgreSQL excels in scalability, leveraging numerous features to enhance performance. One notable aspect is its ability to harness multiple CPU cores, enabling the parallel execution of queries for swift and efficient processing. This capability allows PostgreSQL to efficiently scale with the hardware, making it suitable for handling substantial workloads by distributing tasks across available CPU resources.

Scaling a PostgreSQL database with clusters involves distributing the workload across multiple nodes to enhance performance and availability. One common approach is using streaming or logical replication to create standby servers for read scalability and high availability. Another method is sharding, which horizontally partitions data across multiple nodes, suitable for large datasets. Connection pooling, load balancing, and containerization with orchestration tools like Kubernetes further contribute to efficient scaling by managing connections, distributing queries, and automating deployment.

SQL Server, while also utilizing multiple CPU cores for enhanced performance, has specific limitations depending on the edition. The standard version is capped at twenty-four CPU cores, whereas the enterprise version offers scalability with unlimited CPU cores. Furthermore, SQL Server introduces a hyper-scale feature that provides dynamic scalability. This feature empowers users to define both lower and upper limits, allowing for flexible scaling according to the database requirements. Whether scaling up or down, SQL Server's hyper-scale feature provides adaptability to varying workloads, making it a versatile solution for organizations with evolving data processing needs.

Performance

PostgreSQL outshines SQL Server in performance through various mechanisms. While both databases provide partitioning, PostgreSQL offers this feature free of charge, with enhanced efficiency. Additionally, PostgreSQL demonstrates superior concurrency, allowing multiple processes simultaneous access and modification of shared data. Its Multi-Version Concurrency Control (MVCC) minimizes deadlock occurrences, only blocking when two queries attempt simultaneous modifications to the same row.

In contrast, SQL Server exhibits underdeveloped concurrency, potentially leading to deadlocks. SQL Server creates new versions of updated rows instead of overwriting, gradually moving older versions to the tempdb database. PostgreSQL has better indexing support for extensions, which improves database performance. SQL Server lacks in refining indexing implementation and incorporating widely used variable types like arrays.

Security

PostgreSQL stands out in its provision of robust data encryption measures, empowering users to leverage secure sockets layer (SSL) certificates for safeguarding data traversing the web or public networks. Furthermore, PostgreSQL offers the flexibility to implement client certificate authentication tools as an optional security measure. Notably, cryptogenic functions can be employed to store encrypted data within PostgreSQL, supporting both symmetric-key and public-key encryption methodologies.

On the other hand, MS SQL Server introduces distinctive data encryption capabilities, including transparent data encryption (TDE), always encrypted, and column-level encryption. TDE employs the sophisticated Advanced Encryption Standard (AES) method to secure physical files, which includes both data and log files. The always encrypted feature takes data security a step further, enabling the encryption of specific columns in both static (at rest) and dynamic (in motion) states, ensuring data remains encrypted in memory.

PostgreSQL vs. SQL Server: What should you choose?

In order to simplify the task of choosing a suitable database for you, we have summarized the entire article and presented you with the two lists of advantages of both databases.

So, as you can see both databases have their own advantages. When it comes to choosing between PostgreSQL and SQL Server, which one is better? After looking into it, PostgreSQL seems to come out on top in many situations. It's not just free and open-source, but it also has features that are easy to get and use, unlike Microsoft SQL Server.

One cool thing about PostgreSQL is its great way of handling many things happening at once, like when lots of stuff needs to be changed in shared data at the same time.

If you're running a small business, PostgreSQL could be a good choice because it's free, has useful features, and is easy to set up on different types of computers. But if your business is heavily into using Microsoft SQL Server, it might still be the better pick because of its advantages over PostgreSQL.

If you are comparing PostgreSQL and Microsoft SQL Server because you are planning a database migration from one to the other, it is worth considering an automatic migration. This method makes the transition process much easier and is much faster than a manual conversion. If you want to migrate a database from SQL Server to PostgreSQL, then you should try Ispirer Toolkit. Our tool supports migration to more than 20 databases, including in the PostgreSQL to SQL Server direction.