As some of you may already know, I started working with PostgreSQL a few months ago to gain a better understanding of other relational database engines. Additionally, several of my customers are currently migrating to PostgreSQL. Therefore, it’s time for me today to share my top 5 key differences between SQL Server and PostgreSQL.

When comparing SQL Server and PostgreSQL, two of the most widely used relational database management systems (RDBMS), there are fundamental differences in their architectures, logging mechanisms, concurrency models, indexing strategies, and licensing approaches. These differences reflect the design philosophies behind these systems and significantly impact their performance, usability, and cost for specific use cases.

1. Query Architecture: SQL Server Uses Threads, PostgreSQL Uses Processes

The architecture of SQL Server and PostgreSQL differs fundamentally in how they handle concurrent connections and queries.

• SQL Server: Uses a thread-based model where all client connections are managed by threads within a single process. SQL Server’s architecture allows each client session to be handled by a lightweight thread, which makes memory usage more efficient. Since SQL Server is designed to run on Windows (though Linux is now supported since SQL Server 2017), its threading model leverages Windows’ native thread management, which is efficient in handling a large number of connections without significant overhead.
• PostgreSQL: Uses a process-based model, meaning that each client connection spawns a separate operating system process. Each PostgreSQL backend (client connection) is isolated from the others in its own process, and PostgreSQL relies on the OS for process management. This approach is more memory-intensive since processes are heavier than threads, but it offers strong isolation between connections. If one process crashes, it doesn’t take down the entire system, which provides better stability in certain scenarios.

SQL Server’s threading model might be more attractive for environments where memory efficiency is a priority, especially when handling large numbers of concurrent connections. On the other hand, PostgreSQL’s process-based model provides greater resilience and better isolation, making it more stable in situations where individual client failures could impact other connections.

2. WAL Logging: SQL Server Uses a Transaction Log for Each Database, PostgreSQL Uses a WAL Log Across the Whole Cluster (Instance)

Both SQL Server and PostgreSQL implement write-ahead logging (WAL) to ensure data durability and recovery, but they manage their logs differently.

• SQL Server: Each database in SQL Server has its own transaction log. The transaction log is used to record all changes made to the database before the changes are written to the actual data files. This per-database log ensures that transactions are isolated at the database level, and recovery is specific to each individual database. SQL Server’s transaction log plays a critical role in point-in-time recovery, backup, and restore operations.
• PostgreSQL: Uses a WAL (Write-Ahead Log) at the instance level. This means there is one WAL log for the entire PostgreSQL instance (cluster) which contains multiple databases. The WAL records changes for all databases within the cluster, which simplifies log management but requires more careful handling during backup and recovery operations. WAL logging in PostgreSQL ensures crash recovery for the entire cluster, and point-in-time recovery (PITR) is cluster-wide.

While SQL Server’s per-database logging structure allows for more granular control and recovery, PostgreSQL’s unified WAL simplifies overall management but necessitates careful planning for recovery across multiple databases.

3. Locking & Blocking: SQL Server Uses Pessimistic Concurrency by Default, PostgreSQL Uses MVCC

Concurrency control is handled very differently in SQL Server and PostgreSQL, which impacts how transactions, locking, and blocking are managed.

• SQL Server: By default, SQL Server uses pessimistic concurrency control, where transactions acquire locks on data (rows or pages) as they read and write to ensure consistency. This can lead to blocking if multiple transactions try to access the same data, which is why SQL Server also provides row versioning and snapshot isolation to enable optimistic concurrency when enabled. Optimistic concurrency, when enabled, allows transactions to work on a version of the data without blocking others, improving read performance in highly concurrent environments. Pessimistic concurrency in SQL Server is also highly efficient but requires careful management to avoid deadlocks or excessive blocking.
• PostgreSQL: Uses MVCC (Multiversion Concurrency Control) by default, which is a form of optimistic concurrency control. In MVCC, every transaction works on a snapshot of the database, allowing readers and writers to operate concurrently without locking each other out. Each row in the table can have multiple versions, and PostgreSQL manages visibility based on transaction IDs. This approach minimizes locking and blocking, as readers never block writers and vice versa. VACUUM is periodically run to clean up old row versions and reclaim space.

For those dealing with heavy transaction loads, SQL Server’s default concurrency model can be effective, but you may need to manage locking issues more proactively. PostgreSQL’s MVCC model provides an inherent advantage in environments with frequent reads and writes, allowing smoother concurrency management without as much manual intervention.

4. Indexing: SQL Server Can Store Data in Clustered Tables or Heap Tables, PostgreSQL Always Uses Heap Tables

The way data is stored and indexed in SQL Server and PostgreSQL is another key area of differentiation.

• SQL Server: Allows tables to be stored either as clustered tables or heap tables. A clustered index in SQL Server means that the table data is physically ordered according to the clustered index key, and there can be only one clustered index per table. Alternatively, heap tables are unorganized collections of rows without any particular order, with secondary (non-clustered) indexes pointing to the rows.
• PostgreSQL: Always stores table data in heap tables. This means that the physical storage of rows is not ordered based on any index. PostgreSQL uses secondary indexes (such as B-tree, GIN, GiST, etc.) to reference data stored in the heap. While PostgreSQL supports the CLUSTER command to reorder a table based on an index, this is a one-time operation, and the physical order is not maintained automatically with future changes to the table.

For applications that benefit from physical data ordering, SQL Server’s clustered indexes provide a distinct advantage, especially for range queries or queries with frequent sort operations. PostgreSQL’s reliance on heap tables paired with secondary indexes allows for more flexible and diverse indexing strategies, even though it does not offer the physical data organization of clustered indexes.

5. Licensing and Cost Differences: SQL Server is Commercial, PostgreSQL is Open-Source

Perhaps one of the most significant differences between SQL Server and PostgreSQL lies in their licensing models and cost.

• SQL Server: Is a commercial, closed-source product owned by Microsoft. It is licensed per-core or per-server, and the costs can become significant depending on the version and scale of the deployment. SQL Server comes in several editions (Express, Standard, Enterprise), each with different features and pricing. Enterprise-level features such as advanced analytics, clustering, and large-scale deployments require more expensive licenses.
• PostgreSQL: Is open-source and distributed under the PostgreSQL License, a permissive open-source license similar to the MIT License. It is completely free to use, modify, and distribute, with no licensing fees, making it a very attractive option for startups, small businesses, and large enterprises looking for a cost-effective database solution.

While SQL Server offers comprehensive enterprise features backed by Microsoft’s support, the associated licensing fees can be prohibitive for small businesses or startups. PostgreSQL’s open-source model makes it a cost-effective choice for companies seeking to reduce database-related expenses without sacrificing features or performance.

Summary

While SQL Server and PostgreSQL both offer powerful relational database management systems, their underlying architectures, concurrency models, storage mechanisms, and licensing models differ significantly. SQL Server tends to be better suited for organizations that require a robust, commercial solution with rich enterprise features (though at a higher cost), while PostgreSQL offers an open-source, flexible, and cost-effective alternative with high performance, especially in highly concurrent environments.

Thanks for your time,

-Klaus