5 Must-Have PostgreSQL Plugins for 2025

Modern applications demand increasingly specialized database functionalities, and PostgreSQL extensions offer a powerful way to extend capabilities without modifying the core system. This guide explores essential PostgreSQL extensions poised to be highly relevant in 2025, focusing on how database administrators and software developers can leverage them for optimized performance. We examine extensions like pg_vector for efficient vector embeddings in AI applications and pg_strom to unlock GPU acceleration for complex data analytics. Discover how these tools, along with solutions like SQLFlash that automatically rewrite inefficient SQL with AI, reduce optimization costs and free you to focus on innovation.

1. Introduction: The Expanding Universe of PostgreSQL Extensions

Modern applications are becoming more complex. They need databases to do more than just store information. They need to handle different types of data and perform specialized tasks. This is where PostgreSQL extensions come in.

I. What are PostgreSQL Extensions?

PostgreSQL extensions are like add-ons for your database. 💡 They are modular packages that extend what PostgreSQL can do. Think of them as apps for your database! They let you add new data types (like ways to store information), functions (like commands you can run), operators (like symbols you can use to compare data), and index access methods (like ways to find data faster). You can do all of this without changing the main PostgreSQL code.

II. Why Use Extensions?

Using extensions gives you many advantages:

• More Flexibility: You can customize your database to fit your specific needs.
• Better Performance: Some extensions can make your database run faster for certain tasks.
• New Features: You can access cutting-edge database features that aren’t built into the core PostgreSQL system.

Here’s a simple table showing the benefits:

III. Who Should Read This Article?

This article is for database administrators (DBAs) and software developers. If you want to make your PostgreSQL databases better, faster, and more powerful, this article is for you. 🎯

IV. Key Terms Explained

Before we dive in, let’s define some important words:

• Extension: A package that adds new features to PostgreSQL.
• Vector Embeddings: A way to represent data as points in space, useful for finding similar items.
• Database Auditing: Tracking who is accessing and changing data in your database.
• Query Optimization: Making your database queries run faster.

V. What We’ll Cover

In this article, we will explore five essential PostgreSQL extensions that will be very important in 2025. These extensions will help you manage data and build applications more effectively. We’ll look at:

1. pg_vector: For working with vector embeddings.
2. pg_strom: For using GPUs to speed up data analysis.
3. pg_cron: For scheduling tasks inside your database.
4. pg_stat_statements: For identifying slow queries.
5. pg_audit: For tracking database activity.

VI. Introducing SQLFlash

Managing and optimizing SQL queries can be challenging and time-consuming. ⚠️ Manually rewriting inefficient SQL queries is a common task for DBAs and developers, but it can take a lot of time and effort.

SQLFlash automatically rewrites inefficient SQL using AI. This can reduce manual optimization costs by 90%! ✨ With SQLFlash, developers and DBAs can focus on core business innovation instead of spending hours optimizing queries. SQLFlash helps ensure your database runs efficiently and smoothly.

2. Essential Extension 1: pg_vector - Embracing the Power of Vector Embeddings

Vector embeddings are changing how we work with data. They allow us to represent complex information in a way that computers can easily understand and compare. The pg_vector extension brings this power directly into your PostgreSQL database.

I. Introduction to Vector Embeddings

Vector embeddings are a way to turn words, images, or other data into a list of numbers. 🎯 These numbers represent the meaning or characteristics of the data. Imagine you have a bunch of photos. Instead of just storing the photos, you could create a vector embedding for each one. Similar photos will have similar vectors.

Think of it like this:

• Each number in the list is a dimension.
• The value of each number represents the data’s position in that dimension.
• Similar data points are located close to each other in the multi-dimensional space.

Vector embeddings are crucial for modern AI and machine learning because they let computers understand relationships between different pieces of information.

II. pg_vector Explained

pg_vector is a PostgreSQL extension that lets you store and search vector embeddings right inside your database. This means you can build powerful AI-driven features without needing a separate vector database.

Here’s how it works:

1. Install the extension: You first need to install the pg_vector extension in your PostgreSQL database.
2. Create a table with a vector column: You create a table and add a column with the vector data type. This column will hold your vector embeddings.
3. Insert vector data: You insert your vector embeddings into the table.
4. Query using vector similarity: You can then query the table to find vectors that are similar to a given vector.

Example:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

CREATE EXTENSION vector;

CREATE TABLE items (
    id bigserial PRIMARY KEY,
    embedding vector(1536) -- Dimensions of the vector
);

INSERT INTO items (embedding) VALUES (ARRAY[0.1, 0.2, 0.3, ...]);

SELECT * FROM items ORDER BY embedding <-> ARRAY[0.1, 0.2, 0.3, ...] LIMIT 5; -- Find the 5 most similar vectors

III. Use Cases

pg_vector can be used in many different ways. Here are a few examples:

• Semantic Search: Instead of searching for exact keywords, you can search for things that have a similar meaning. For example, searching for “comfortable shoes” might also return results for “cozy sneakers.”
• Recommendation Systems: Suggesting products or content to users based on what they’ve liked before. If a user liked a movie with a certain vector embedding, you can recommend other movies with similar embeddings.
• Image Similarity: Finding images that are visually similar. This is useful for things like reverse image search or identifying objects in images.

Here’s a table summarizing the use cases:

IV. Performance Considerations

To get the best performance from pg_vector, you need to think about indexing and distance metrics. ⚠️

• Indexing: Indexing helps PostgreSQL quickly find the vectors that are most similar to your query. A popular indexing method for vector embeddings is HNSW (Hierarchical Navigable Small World).
• Distance Metrics: Distance metrics tell PostgreSQL how to measure the similarity between two vectors. Common options include:
  • Euclidean distance: Measures the straight-line distance between two vectors.
  • Cosine similarity: Measures the angle between two vectors. This is often used for text embeddings.

Choosing the right distance metric depends on your data and use case.

Here’s a table summarizing the considerations:

By understanding these concepts, you can use pg_vector to build powerful and efficient applications that leverage the power of vector embeddings.

3. Essential Extension 2: pg_strom - Unleashing GPU Acceleration for Data Analytics

Modern data analysis demands speed. We need to process huge amounts of information quickly to make smart decisions. pg_strom helps PostgreSQL do just that by using the power of GPUs.

I. The Need for Accelerated Analytics

Businesses today rely on data more than ever. They need to analyze sales figures, customer behavior, and website traffic to stay competitive. Traditional CPUs can struggle to keep up with the growing حجم of data. This is where accelerated analytics comes in. Faster processing means faster insights, which leads to better decisions and a competitive edge.

II. pg_strom Introduction

pg_strom is a PostgreSQL extension that uses GPUs (Graphics Processing Units) to make queries run much faster. 💡 GPUs are designed to handle many calculations at the same time, making them perfect for data analysis tasks. pg_strom lets you use this power within your PostgreSQL database. It’s like adding a super-fast engine to your database!

III. How it Works

pg_strom works by taking parts of your SQL queries and running them on the GPU. When you run a query, pg_strom figures out which parts would benefit most from GPU acceleration. It then sends those parts to the GPU for processing. The GPU does the calculations much faster than the CPU could. Finally, pg_strom sends the results back to PostgreSQL. This whole process happens behind the scenes, so you don’t have to change your SQL queries much.

IV. Supported Operations

pg_strom can speed up many common SQL operations:

• Filtering: Selecting rows based on certain conditions.
• Aggregation: Calculating sums, averages, and other statistics.
• Joins: Combining data from multiple tables.
• Group By: Grouping rows with the same values in certain columns.
• Order By: Sorting the result set.

Here’s a table showing some example operations and how pg_strom can help:

V. Use Cases

pg_strom is useful in many situations where speed is important:

• Real-time Analytics: Imagine you’re tracking website traffic. pg_strom can help you analyze that data in real-time, so you can quickly identify trends and respond to problems.
• Data Warehousing: Data warehouses store huge amounts of historical data. pg_strom can speed up complex queries in these environments, allowing you to get insights faster.
• Geospatial Analysis: If you’re working with maps or location data, pg_strom can help you perform fast geospatial calculations, such as finding all the stores within a certain radius of a customer.

Here are some specific examples:

VI. Implementation Notes

To use pg_strom, you’ll need a few things:

• NVIDIA GPU: pg_strom requires an NVIDIA GPU. The more powerful the GPU, the better the performance. ⚠️
• CUDA Drivers: You’ll need to install the NVIDIA CUDA drivers. These drivers allow PostgreSQL to communicate with the GPU.
• pg_strom Extension: You’ll need to download and install the pg_strom extension for PostgreSQL.

Setting up pg_strom can be a bit technical, but the performance gains can be significant. Make sure to check the official pg_strom documentation for detailed instructions.

4. Essential Extension 3: pg_cron - Scheduling Tasks Within Your Database

Databases often need to perform tasks automatically. This could include cleaning up old data, creating reports, or backing up the database. pg_cron helps you schedule these tasks directly within PostgreSQL.

I. The Importance of Task Scheduling

Imagine you have to manually run a database backup every day. That’s time-consuming and prone to errors. Automated task scheduling solves this problem. 💡 It lets you set up tasks to run at specific times or intervals, without needing you to do anything. This is important for:

• Database Maintenance: Regularly cleaning and optimizing your database.
• Report Generation: Creating and distributing reports on a schedule.
• Data Backups: Ensuring your data is safe with automated backups.
• Data Synchronization: Keeping data consistent between different systems.

Without task scheduling, these important jobs might get forgotten or done incorrectly.

II. pg_cron Overview

pg_cron is a simple job scheduler for PostgreSQL. It uses the familiar “cron” syntax, which is a standard way to schedule tasks on computers. 🎯 Instead of relying on external tools, pg_cron lets you schedule database tasks directly from within PostgreSQL. This makes it easier to manage and monitor your scheduled jobs.

III. Key Features

pg_cron has several key features that make it a great choice for scheduling database tasks:

• Cron Syntax: Uses standard cron syntax for scheduling. If you’ve used cron before, you’ll feel right at home.
• Database Integration: Runs directly within PostgreSQL, giving it direct access to your data.
• Security: Integrates with PostgreSQL’s security model, so only authorized users can schedule tasks.
• Logging: Logs all job executions, making it easy to track what’s happening.
• Parallel Execution: Can run multiple jobs at the same time.

Here’s a quick example of cron syntax:

IV. Use Cases

pg_cron can be used for many different tasks. Here are a few examples:

A. Database Maintenance

You can use pg_cron to schedule routine database maintenance tasks like VACUUM and ANALYZE. These tasks help keep your database running smoothly.

1
2

SELECT cron.schedule('0 3 * * *', 'VACUUM FULL;');
SELECT cron.schedule('0 4 * * *', 'ANALYZE;');

These examples schedule a full vacuum at 3:00 AM every day and an analyze at 4:00 AM every day.

B. Report Generation

If you need to generate reports regularly, pg_cron can help. You can schedule a query to run and save the results to a file.

1

SELECT cron.schedule('0 8 * * 1', 'SELECT * FROM sales_data WHERE sale_date > now() - interval \'1 week\'; COPY (SELECT * FROM sales_data WHERE sale_date > now() - interval \'1 week\') TO \'/tmp/weekly_sales.csv\' WITH CSV HEADER;');

This example runs a query every Monday at 8:00 AM to get the sales data from the last week and saves it to a CSV file.

C. Data Synchronization

You can use pg_cron to synchronize data between different databases or systems. For example, you might want to copy data from a production database to a reporting database.

1

SELECT cron.schedule('0 2 * * *', 'INSERT INTO reporting_db.sales_data SELECT * FROM production_db.sales_data WHERE sale_date > now() - interval \'1 day\';');

This example runs a query every day at 2:00 AM to copy the sales data from the production database to the reporting database.

V. Alternatives and Considerations

While pg_cron is a great option for scheduling tasks within your database, there are other alternatives. You could use external cron jobs or other task scheduling tools. However, pg_cron has some advantages:

• Direct Database Access: pg_cron has direct access to your database, making it easy to run queries and modify data.
• Centralized Management: You can manage all your scheduled tasks from within PostgreSQL.
• Security Integration: pg_cron integrates with PostgreSQL’s security model, so you can control who can schedule tasks.

⚠️ Before using pg_cron, consider the potential impact on your database performance. Long-running tasks can affect other database operations. It’s also important to secure your pg_cron configuration to prevent unauthorized access. Always test your scheduled tasks in a non-production environment before deploying them to production.

Recommended reading

• SQL Optimization Course
• SQL optimization knowledge
• Database industry knowledge

What is SQLFlash?

SQLFlash is your AI-powered SQL Optimization Partner.

Based on AI models, we accurately identify SQL performance bottlenecks and optimize query performance, freeing you from the cumbersome SQL tuning process so you can fully focus on developing and implementing business logic.

How to use SQLFlash in a database?

• SQLFlash + MySQL

• SQLFlash + PostgreSQL