7 Reasons PostgreSQL is a Great Choice for AI Projects

The database is the foundation of machine learning, powering everything from training AI models to delivering business insights. But with so many options to choose from, how can you know which one truly aligns with your goals? In this article, we’ll dive into the specifics of PostgreSQL and learn why it has become so popular today.

Challenging Times

Developing AI projects and blending them into existing ecosystems is a complex process with multiple operational implications. Many challenges need to be considered before picking the right database.

ML projects often require vector databases to handle AI workloads. These databases create data silos, increase latency, and pose risks due to scalability requirements and compliance issues. These things add up fast. Companies then start facing higher costs, longer development times, and significant management challenges in regulated industries.

Industry experts who are managing petabytes of data for clients on open-source platforms like Postgres, Cassandra, and Spark now believe the former is the hands-down best choice for modern AI projects.

See also: Bridging the Gap: Scaling GenAI for Real Business Growth & Impact

7 Reasons to Opt for PostgreSQL

Here are seven benefits that can be experienced from the get-go.

1.     Vector Search and AI Integration

Vector similarity search is vital for AI tasks like recommendation systems and Gen AI models. This kind of workload acceleration is very easy with extensions like pgvector, which allows you to store, query, and index vectors seamlessly. This streamlines AI deployment by eliminating the need for separate data stores or complex data transfers.

The latest pgvector version 0.8.0, released in late 2024, added support for iterative index scans and improved cost estimation for better index selection when filtering. You also get performance improvements, especially with HNSW index scans, HNSW inserts, and on-disk index builds. Support for Postgres 12 was dropped after this update.

2.     Advanced Indexing for AI Workloads

PostgreSQL checks available indexes to determine their usefulness when a query is executed. If and when a suitable index is found, Postgres leverages it to deliver faster results. This level of optimization elevates performance by optimizing search and retrieval for both structured and unstructured AI datasets.

PostgreSQL supports many index types. Here are a few common ones:

• B-tree Index – This is PostgreSQL’s default index that’s created automatically if no type is specified. It organizes data in a tree-like structure, with the root node having pointers to child nodes.
• Hash Index – Hash indexes are typically used for fast key-value lookups. They allow rapid data retrieval for equality checks by using a hash function to pinpoint the exact data location.
• Hash Indexes – Block Range Index (BRIN) is ideal for large, sorted tables. It stores the minimum and maximum values, optimizing speed for sequential or naturally ordered data like time series.
• GiST and SP-GiST Indexes – These PostgreSQL indexes support diverse data types and complex searches, including spatial data. GiST, in particular, excels at speeding up full-text searches.

PostgreSQL also allows users to create custom indexes using user-defined functions, providing flexibility to tailor indexing strategies to unique AI application needs and improve query performance.

3.     Native JSON and NoSQL Capabilities

Postgres can be used for NoSQL-like functionality via features such as JSON/JSONB columns, table partitioning, and HStore. This allows the storage of semi-structured data efficiently and essentially turns into a hybrid SQL-NoSQL database. Combining structured SQL queries with JSONB storage allows AI models to work smoothly.

Postgres can function as a NoSQL database by using JSONB for doc storage, HStore for key-value pairs, and advanced indexing for high performance. NoSQL flexibility with relational power is a potent combo.

4.     Parallel Processing and Query Execution

With query optimization taking center stage, PostgreSQL allows parallel query execution by making use of multi-core machines for fast(er) data processing. This is a real game changer because the database splits queries into tasks executed concurrently by threads, leading to significant performance boosts and optimized resource usage.

Just make sure you adjust your parallel-related settings:

• max_parallel_workers: This parameter sets the maximum number of parallel workers that can be used by the database.
• max_parallel_workers_per_gather: This defines the maximum number of parallel workers that can be started by a single Gather or Gather Merge node.
• Min_parallel_table_scan_size: This parameter controls when a parallel scan is initiated.
• Min_parallel_index_scan_size: Same as above.

Make sure you are working with newer PostgreSQL versions for better parallel processing. Also, performance may vary based on queries and data, so test new configurations and adopt an iterative approach.

5.     Scalable and Distributed Computing

With the exponential rise in the number of AI applications and projects, the demand for distributed PostgreSQL deployments is growing. This is where variations like Multi-Master Asynchronous Replication, Multi-Master Sharded PostgreSQL with Coordinator, and Multi-Master Shared-Nothing architectures come into the picture.

• Multi-Master Sharded PostgreSQL with a Coordinator: Here, the data is shared across multiple standalone Postgres instances. A coordinator node handles app connections and directs requests.
• Multi-Master Asynchronous Replication: This involves multiple standalone PostgreSQL instances with asynchronous replication and conflict resolution mechanisms.
• Multi-Master Shared-Nothing PostgreSQL: This variation essentially utilizes a true distributed database that is feature- and runtime-compatible with PostgreSQL.

Distributed Postgres is trending as AI app devs seek scalable databases with zero data loss (RPO=0), rapid failover, and global distribution to achieve compliance, meet low-latency needs, and optimize efficiency.

6.     AI Data Security and Compliance

PostgreSQL offers multiple layers of security with AI data. Data access control is important when it comes to AI applications. Besides the obvious Access Control Lists (ACLs) that can be implemented with GRANT and REVOKE SQL commands, you get Row Level Security (RLS). This allows you to define row visibility based on specific roles.

There is also Transparent Data Encryption (TDE) on offer. This allows the encrypted storage of data-at-rest. The data blocks are then decrypted on-demand as they are read. Postgres also supports security auditing with audit logging options like The pgAudit extension and custom triggers that allow you to create your own customized audit flows.

7.     AI-ready Open Source (OS) Flexibility

The rise of AI apps that leverage the capabilities unlocked by LLMs means that developers now need dynamic, versatile, and secure databases. PostgreSQL’s extensibility allows smooth integration with popular AI frameworks and makes it truly stand out as a cost-effective alternative to proprietary AI-specific databases.

Also, community is the name of the game when it comes to PostgreSQL. Make sure you check out Postgres’s community-driven extension network to make your AI applications shine.

Still not convinced? Don’t take my word for it.

PostgreSQL has been named the Most Popular Database in the 2024 Stack Overflow Developer Survey. There’s no reason to believe that this trend will stop anytime soon. So what are you waiting for?