Picking the right database can make or break your AI project. Besides strong integration capabilities, cost-effectiveness and scalability are also key requirements today. Enter PostgreSQL, also known as Postgres.
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 factors can quickly escalate costs, extend development timelines, and introduce significant management challenges, especially in regulated industries.
Industry experts managing petabytes of data for clients on open-source platforms like Postgres, Cassandra, and Spark now believe that PostgreSQL stands out as the best choice for modern AI projects.
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 such as recommendation systems and generative AI models. This workload acceleration is made effortless with extensions like pgvector, which allows for seamless storage, querying, and indexing of vectors. This capability 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, introduced support for iterative index scans and improved cost estimation for better index selection when filtering. Performance enhancements, particularly with HNSW index scans, HNSW inserts, and on-disk index builds, have also been included. Note that 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 a suitable index is found, Postgres leverages it to deliver faster results. This optimization elevates performance by enhancing search and retrieval for both structured and unstructured AI datasets.
PostgreSQL supports various index types, including:
- B-tree Index – The default index created automatically if no type is specified, organizing data in a tree-like structure.
- Hash Index – Used for fast key-value lookups, allowing rapid data retrieval for equality checks.
- BRIN Index – Ideal for large, sorted tables, storing minimum and maximum values to optimize speed for sequential data.
- GiST and SP-GiST Indexes – Supporting diverse data types and complex searches, including spatial data, with GiST excelling at full-text searches.
Additionally, PostgreSQL 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 function similarly to NoSQL databases through features such as JSON/JSONB columns, table partitioning, and HStore, enabling efficient storage of semi-structured data. This hybrid SQL-NoSQL capability allows AI models to operate smoothly by combining structured SQL queries with JSONB storage.
4. Parallel Processing and Query Execution
With query optimization taking center stage, PostgreSQL enables parallel query execution, utilizing multi-core machines for faster data processing. This capability allows the database to split queries into tasks executed concurrently by threads, resulting in significant performance boosts and optimized resource usage.
To leverage parallel processing effectively, users should adjust settings such as:
- max_parallel_workers: Sets the maximum number of parallel workers that can be used by the database.
- max_parallel_workers_per_gather: Defines the maximum number of parallel workers that can be initiated by a single Gather or Gather Merge node.
- min_parallel_table_scan_size: Controls when a parallel scan is initiated.
- min_parallel_index_scan_size: Similar to the above, but for index scans.
Using newer PostgreSQL versions enhances parallel processing capabilities, though performance may vary based on queries and data, so testing configurations is advisable.
5. Scalable and Distributed Computing
As the demand for AI applications surges, the need for distributed PostgreSQL deployments grows. Variations such as Multi-Master Asynchronous Replication, Multi-Master Sharded PostgreSQL with Coordinator, and Multi-Master Shared-Nothing architectures are emerging to meet this demand.
- Multi-Master Sharded PostgreSQL with a Coordinator: Data is shared across multiple standalone Postgres instances, with a coordinator node managing app connections and directing requests.
- Multi-Master Asynchronous Replication: Involves multiple standalone PostgreSQL instances with asynchronous replication and conflict resolution mechanisms.
- Multi-Master Shared-Nothing PostgreSQL: Utilizes a true distributed database that is feature- and runtime-compatible with PostgreSQL.
Distributed Postgres is increasingly favored by AI developers seeking scalable databases that ensure zero data loss, rapid failover, and global distribution to meet compliance and optimize efficiency.
6. AI Data Security and Compliance
PostgreSQL provides multiple layers of security for AI data. Data access control is crucial for AI applications, and in addition to Access Control Lists (ACLs) implemented with GRANT and REVOKE SQL commands, Row Level Security (RLS) allows for defining row visibility based on specific roles.
Transparent Data Encryption (TDE) is also available, enabling encrypted storage of data-at-rest, with data blocks decrypted on-demand as they are accessed. PostgreSQL supports security auditing through options like the pgAudit extension and custom triggers to create tailored audit flows.
7. AI-ready Open Source (OS) Flexibility
The rise of AI applications leveraging capabilities unlocked by large language models necessitates dynamic, versatile, and secure databases. PostgreSQL’s extensibility allows for smooth integration with popular AI frameworks, making it a cost-effective alternative to proprietary AI-specific databases.
Community engagement is vital for PostgreSQL. Developers are encouraged to explore the community-driven extension network to enhance their AI applications.
PostgreSQL has been recognized as the Most Popular Database in the 2024 Stack Overflow Developer Survey, indicating a strong trend that shows no signs of waning. The potential for PostgreSQL in AI projects is vast, and its capabilities continue to evolve.
