Top 12 PostgreSQL Extensions You Should Know About 2025

PostgreSQL continues to grow quickly in enterprise environments. Many companies choose it because it improves performance and adds new features through extensions. Recent surveys show that nearly half of developers prefer it, and large organizations rely on it for production workloads.

• PostgreSQL holds a 16.85% share of the relational database market.

• About 11.9% of companies with over $200 million in revenue use it in production.

• Developers highlight its popularity in recent surveys.

Extensions support a wide range of workloads. For example, PostGIS boosts geospatial analysis, pgvector helps with AI tasks, and pg_strom speeds up analytics using GPU acceleration. Readers can expect practical insights on how these tools benefit real-world projects.

About PostgreSQL Extensions

What They Are

PostgreSQL extensions are add-ons that expand the core database’s features. Developers use them to add new data types, functions, and tools without changing the main system. Extensions help PostgreSQL adapt to different needs, from handling geospatial data to supporting machine learning tasks.

The table below shows some of the most common PostgreSQL extensions and their main capabilities:

Why They Matter

Extensions play a key role in modern database projects. They help PostgreSQL overcome its core limitations and boost both performance and scalability. For example:

• Extensions like PostGIS, pg_strom, and PostgresML improve query speed and support advanced data types.

• Specialized extensions allow complex data processing, making PostgreSQL suitable for analytics, AI, and IoT workloads.

• Features such as table partitioning and high-performance transaction handling support large-scale applications.

• Tools like pg_cron and pgAudit add scheduling and logging, which are important for enterprise use.

PostgreSQL extensions let teams build solutions that meet today’s demands. They enable new workloads, improve efficiency, and help organizations scale their data infrastructure with confidence.

1. PostGIS

Overview

PostGIS stands as the leading geospatial extension for PostgreSQL. It transforms the database into a powerful spatial data engine. Developers use PostGIS to store, query, and analyze geographic objects directly in their databases. This extension supports both simple and complex spatial operations, making it essential for projects that need mapping or location-based features.

Features

PostGIS offers a wide range of geospatial tools. It supports the ‘Simple Features’ standard from the OpenGIS Consortium, which allows users to work with points, lines, and polygons. The extension also includes all objects and functions from the OGC ‘Simple Features for SQL’ specification. PostGIS goes further by embedding SRID (Spatial Reference System Identifier) information and supporting advanced coordinate formats like 3DM, 3DZ, and 4D. These features help users handle complex spatial data with accuracy.

Use Cases

Many industries rely on PostGIS for its mapping and spatial analysis power. City planners use it to manage zoning and infrastructure data. Logistics companies track shipments and optimize delivery routes. Environmental scientists map habitats and analyze changes in land use. Businesses with location-based services, such as ride-sharing or real estate platforms, depend on PostGIS to deliver accurate results. By integrating PostGIS, teams can unlock advanced geospatial analytics within their PostgreSQL databases.

2. pg_stat_statements

Overview

pg_stat_statements helps database administrators and developers understand query performance. This extension collects statistics about SQL statements that run on the server. It tracks execution counts, total time, and resource usage for each query. Teams use pg_stat_statements to find slow queries and improve database speed. The extension works with PostgreSQL and does not require changes to application code.

Features

pg_stat_statements offers several important features for monitoring and tuning:

• Tracks execution statistics for all SQL statements.

• Groups similar queries together, even if they use different values.

• Records total execution time, average time, and number of calls.

• Shows information about I/O and memory usage.

• Allows users to reset statistics when needed.

Tip: pg_stat_statements makes it easy to spot inefficient queries. Teams can use this data to optimize indexes or rewrite slow SQL.

The table below summarizes key features:

Use Cases

Many organizations rely on pg_stat_statements for performance tuning. Database administrators use it to identify queries that slow down applications. Developers review statistics to improve SQL code and reduce resource consumption. Teams monitor production systems to catch problems before they affect users. pg_stat_statements supports regular health checks and helps maintain fast, reliable databases.

• E-commerce sites use it to keep checkout processes quick.

• Financial services monitor query speed for real-time reporting.

• SaaS companies track performance to ensure smooth user experiences.

pg_stat_statements provides valuable insights for anyone who manages or develops with PostgreSQL.

3. TimescaleDB

Overview

TimescaleDB extends PostgreSQL to handle time-series data efficiently. Many organizations choose it for projects that require fast data ingestion and real-time analytics. TimescaleDB transforms the database into a powerful engine for storing and analyzing data that changes over time. This extension supports workloads such as IoT, monitoring, and financial analytics. It provides features that standard PostgreSQL lacks, making it a top choice for time-series applications.

Features

TimescaleDB introduces several advanced features for time-series data management:

• Hypertables: TimescaleDB uses hypertables to partition data automatically. This structure improves query speed by excluding irrelevant data.

• Data Compression: The extension reduces storage needs by up to 90%, making it cost-effective for large datasets.

• Query Performance: TimescaleDB can boost query speed by up to 1,000x compared to standard PostgreSQL.

• Retention Policies: Users can set automatic data retention, which helps manage storage and compliance.

• Columnar Storage: The extension supports columnar storage, which enhances analytics and reporting.

• Horizontal Scalability: TimescaleDB scales out across multiple servers, supporting growing workloads.

Note: TimescaleDB outperforms native partitioning in many scenarios and maintains high ingest performance, especially with batch inserts of 10,000 to 15,000 rows.

Use Cases

TimescaleDB supports a wide range of real-world applications:

• IoT sensor data management for smart devices and industrial systems

• Infrastructure and DevOps monitoring, including application performance monitoring (APM)

• Financial market analytics, such as tracking stock prices and trading volumes

• Real-time analytics dashboards for business intelligence

• Recording and analyzing system metrics for alerting and troubleshooting

• Detecting patterns and anomalies in log data

These use cases show why TimescaleDB has become essential for teams that need to manage and analyze time-series data at scale. Its specialized features make it a strong choice for modern data-driven projects.

4. pg_partman

Image Source: unsplash

Overview

pg_partman helps PostgreSQL users manage table partitions with ease. Many teams face challenges when handling large datasets that grow quickly. pg_partman automates the creation, maintenance, and removal of partitions. This extension supports both time-based and serial-based partitioning. It works well for tables that collect data over time, such as logs or sensor readings. By using pg_partman, database administrators can avoid manual partition management and keep their systems running smoothly.

Features

pg_partman offers several features that simplify partition management and improve database performance. The extension creates new partitions automatically as data grows. It drops old partitions based on retention policies, which helps control storage costs. Queries run faster because they only scan relevant partitions. Administrators spend less time on routine tasks, since pg_partman handles partition creation and cleanup.

The table below highlights key features:

pg_partman also integrates with tools like pg_cron. This combination allows databases to scale as data volumes increase. The extension pre-creates partitions, which reduces downtime and keeps systems efficient.

Use Cases

Many organizations use pg_partman for tables that collect large amounts of data. Teams in finance track transactions and market activity. IT departments store logs from servers and applications. IoT companies manage sensor readings from devices. pg_partman automates partition management, allowing databases to grow without manual intervention.

• Automates partition management, allowing for seamless scaling as data grows.

• Reduces manual intervention, which enhances overall system efficiency.

• Supports retention policies, helping teams control storage costs and keep datasets lean.

pg_partman improves query speed and reduces downtime. It helps administrators focus on important tasks instead of routine maintenance. This extension makes PostgreSQL a strong choice for projects that require reliable, scalable data storage.

5. citus

Overview

Citus transforms PostgreSQL into a distributed database. It allows teams to scale out by spreading data and queries across multiple servers. This extension helps organizations handle large datasets and high traffic without slowing down. Citus works well for businesses that need fast analytics and real-time data processing. Many companies choose Citus when their workloads grow beyond the limits of a single database server.

Features

Citus offers several features that make PostgreSQL more powerful for big data projects. It divides tables into smaller pieces called shards and distributes them across a cluster. This approach improves storage efficiency. For example, the combined size of Citus shards is 50% smaller than traditional PostgreSQL tables. Each shard uses only 29 MB, while a standard table uses 54 MB. This reduction lowers storage costs and makes data management easier.

Citus also boosts performance. Simple multi-shard queries run up to 20 times faster. The extension supports better transaction handling, especially when mixing single-shard and multi-shard queries. Error messages become more informative, helping users troubleshoot problems quickly. Citus improves resource management by allowing better configuration of connections to worker nodes. It also increases fault tolerance, so the system stays reliable even when some nodes reach their connection limits.

Citus distributes data across a cluster, making it easier to manage large datasets and run real-time analytics.

Use Cases

Many organizations use Citus for demanding workloads. Helsinki Region Transport Authority logs real-time location data for thousands of vehicles and matches it with timetable data. This setup improves traffic monitoring and helps enforce service agreements. The Windows team at Microsoft uses Citus to scale analytics dashboards on Azure. Their system tracks metrics from over 800 million devices and handles more than 6 million queries each day with hundreds of users at once.

Citus excels in real-time analytics by parallelizing SQL queries over many nodes. Teams can keep their systems responsive, even when working with terabytes of data. Citus helps businesses grow without worrying about database limits.

6. pg_cron

Overview

pg_cron adds native job scheduling to PostgreSQL. This extension lets users automate routine database tasks using simple SQL commands. pg_cron runs jobs inside the database, so teams do not need external schedulers. It works well for regular maintenance, data refreshes, and backups. Administrators find pg_cron easy to set up and manage. The extension supports flexible scheduling, similar to the classic Linux cron utility.

Features

pg_cron offers several features that make scheduling tasks inside PostgreSQL straightforward. Users can create, modify, and delete jobs using SQL. The extension supports full ACID compliance, which means scheduled jobs run safely within database transactions. pg_cron handles errors gracefully and logs job results for easy troubleshooting. Teams can schedule jobs at any interval, from minutes to months.

The table below compares pg_cron with other popular scheduling tools:

Tip: pg_cron lets teams schedule jobs directly in the database, reducing the need for external scripts and tools.

Use Cases

Many organizations use pg_cron to automate important database tasks. The extension helps teams keep systems running smoothly and reduces manual work. Common scheduling tasks include:

1. Scheduling a Daily Backup: pg_cron automates database backups every day at 2 AM.

2. Refreshing a Materialized View: The extension refreshes materialized views every six hours to keep data current.

3. Automated Data Cleanup: pg_cron deletes old records daily, such as logs older than 30 days.

Developers use pg_cron to update summary tables, send alerts, and synchronize data between systems. Administrators rely on it for regular maintenance, such as vacuuming tables or rebuilding indexes. pg_cron improves reliability by running jobs inside PostgreSQL, so teams do not worry about external failures. This extension helps organizations save time and keep their databases healthy.

7. hstore

Overview

hstore gives PostgreSQL the ability to store sets of key-value pairs in a single column. This extension first appeared in PostgreSQL 8.2, marking a major step forward for handling unstructured and semi-structured data. Developers use hstore when they need flexibility in their database schema. The extension allows each row to have different keys and values, which helps teams manage data that does not fit neatly into tables. Although jsonb now offers more advanced features and better performance, hstore remains useful for projects that require backward compatibility or simple key-value storage.

hstore made PostgreSQL more flexible by allowing users to store unstructured data directly in the database.

Features

hstore provides several features that make it valuable for certain workloads:

Developers can use hstore to add or remove keys without changing the table structure. The extension supports fast lookups and updates, which helps teams build responsive applications.

Use Cases

Many modern applications use hstore for tasks that need quick access and updates. Common scenarios include:

• Maintaining a cache for frequently accessed data.

• Fast read and update operations, such as tracking API rate limits.

• Storing scores or simple time-series data.

Teams choose hstore when they need a simple way to manage changing data structures. For example, a developer might use hstore to store user preferences, where each user has different settings. Another team might use it to log events with varying attributes. hstore works well for applications that need speed and flexibility without the overhead of more complex data types.

8. pg_trgm

Overview

pg_trgm is a PostgreSQL extension that helps users search and compare text efficiently. It works by breaking down strings into small groups of characters called trigrams. This method allows the database to find similar words or phrases, even if there are typos or slight differences. Many developers use pg_trgm to improve search features in their applications. The extension does not require extra infrastructure, making it a practical choice for teams who want fast text search inside PostgreSQL.

Features

pg_trgm offers several important features for text search and similarity matching:

• Supports fast fuzzy search by comparing trigrams in text.

• Works with GIN and GiST indexes to speed up similarity queries.

• Allows users to set a similarity threshold for more accurate results.

• Integrates with standard SQL queries for easy use.

To improve performance, users can set a higher similarity threshold:
SET pg_trgm.similarity_threshold = 0.5;
This adjustment can lead to better matching results.

Key performance improvements include:

• GIN and GiST indexes significantly speed up similarity search queries.

• Adjusting the similarity threshold filters results more effectively, which enhances performance.

Use Cases

Many organizations use pg_trgm to optimize search in production environments. For example:

• A case study with 8.9 million Amazon reviews showed a dramatic speedup, reducing search time from 360 seconds to just over 100 milliseconds.

• pg_trgm provides competent text search without the need for dedicated systems like Elasticsearch.

• Teams choose pg_trgm when they want to avoid extra infrastructure but still need fast, flexible search.

Common use cases include:

• Autocomplete features in web applications.

• Finding similar product names or user entries.

• Detecting duplicates or near-matches in large datasets.

pg_trgm helps teams deliver responsive search experiences directly within PostgreSQL.

9. PLV8

Overview

PLV8 is a PostgreSQL extension that lets users write stored procedures and functions in JavaScript. Many developers prefer JavaScript for its flexibility and familiarity. PLV8 brings this popular language into the database, allowing teams to process data and build logic without switching to PL/pgSQL. This extension works well for projects that need advanced data manipulation or want to use JavaScript’s expressive features directly inside PostgreSQL.

Features

PLV8 offers several features that make it a strong choice for modern database projects:

• Developers can write functions in JavaScript instead of PL/pgSQL.

• The extension supports PostgreSQL’s native JSON data type, making it easy to handle schema-less or document-style data.

• PLV8 provides a trusted environment, which helps keep data safe during execution.

• Teams can run unit tests close to the data, improving code quality and reliability.

• JavaScript’s mapping and transformation capabilities allow for advanced data handling.

PLV8 makes problem-solving easier for those who know JavaScript. It also helps manage documents and run tests directly in the database.

The table below highlights key benefits:

Use Cases

Many teams use PLV8 to solve problems that are hard to address with SQL alone. For example:

• Building complex data transformations using JavaScript’s array and object methods.

• Managing and querying JSON documents in applications that need flexible data models.

• Running validation or business logic close to the data for better performance.

• Writing unit tests for database functions, ensuring code works as expected.

Developers who already know JavaScript can quickly create powerful database functions. PLV8 helps organizations bridge the gap between application logic and data storage, making PostgreSQL even more versatile.

10. HypoPG

Overview

HypoPG gives PostgreSQL users a way to test new index ideas without changing the actual database. This extension lets developers and administrators create hypothetical indexes that exist only in memory. These indexes do not use disk space or affect production data. HypoPG works by showing how a new index might change the query plan. It helps teams make smarter decisions about indexing before making any real changes.

HypoPG acts as a safe playground for index experimentation. Teams can see the impact of new indexes instantly, without risk.

Features

HypoPG stands out for its ability to simulate indexes and optimize query planning. The extension supports several index types, including B-tree, BRIN, Hash, and Bloom indexes. It integrates with PostgreSQL’s EXPLAIN planner, so users can view how a hypothetical index would affect query execution.

• HypoPG creates virtual indexes in memory, so there is no extra disk usage.

• The extension appears only during EXPLAIN statements, keeping the real schema unchanged.

• Users can drop these indexes instantly, making testing fast and safe.

Use Cases

Many developers and database administrators use HypoPG to improve database performance. They can test if a new index will speed up one or more queries before making changes to the schema. HypoPG helps teams avoid unnecessary CPU, disk, or memory usage during testing.

• Developers evaluate the impact of different index configurations on query speed.

• Administrators check if dropping an index will slow down important queries.

• Teams make informed decisions about indexing without risking production data.

HypoPG enables safe, cost-free experimentation. It helps organizations optimize their PostgreSQL databases with confidence.

11. pg_repack

Overview

pg_repack is a PostgreSQL extension that helps database administrators remove table bloat and reorganize tables without locking them for long periods. Table bloat happens when frequent updates and deletes leave unused space in tables and indexes. Over time, this can slow down queries and waste storage. pg_repack solves this by rebuilding tables and indexes in the background. Users can keep their databases running while pg_repack works, which makes it a popular choice for busy systems.

Features

pg_repack offers several features that make it valuable for maintaining database health:

• Reorganizes tables and indexes online, so users experience minimal downtime.

• Frees up unused space, which improves storage efficiency.

• Works with large tables that standard VACUUM FULL cannot handle easily.

• Supports both manual and scheduled operations.

• Integrates with PostgreSQL’s extension system for easy installation.

The following table highlights the main benefits of using pg_repack:

Tip: pg_repack can reorganize even the largest tables without interrupting service, making it ideal for production environments.

Use Cases

Many organizations rely on pg_repack to keep their PostgreSQL databases fast and efficient. Large OLTP systems use it to manage table bloat without stopping service. E-commerce platforms often run pg_repack during peak traffic to maintain high availability. Financial applications value the tool because it minimizes downtime, which is critical for transaction-heavy workloads.

The table below shows where pg_repack is most frequently used:

Teams often schedule pg_repack jobs during maintenance windows or low-traffic periods. Some use it for one-time reorganizations when tables become too large or slow. By using pg_repack, administrators keep their databases lean and responsive, even as data grows.

12. pgvector

Image Source: unsplash

Overview

pgvector has become a key extension for PostgreSQL, especially in projects that use artificial intelligence and machine learning. This tool lets databases store and search vector data, which is important for tasks like image recognition, recommendation systems, and natural language processing. Many teams now use pgvector to build applications that need fast and accurate similarity searches. The extension started as a niche solution but now appears in mainstream PostgreSQL environments. Developers value its ability to handle vector operations directly in SQL queries, which improves both speed and flexibility.

pgvector helps PostgreSQL support AI workloads by making vector search and storage simple and efficient.

Features

pgvector offers several advanced features that make it popular for modern data projects:

• Advanced Indexing Methods

  • GPU-accelerated similarity search

  • Hierarchical clustering indices

  • Multi-modal vector support

  • Dynamic index optimization

• Storage Improvements

  • Product quantization for efficient storage

  • Adaptive precision for better accuracy

  • Streaming vector ingestion

  • Automatic index tuning

• Ecosystem Integration

  • Direct integration with machine learning frameworks

  • Real-time analytics support

  • Advanced security features

PostgreSQL 17 and newer versions add improved parallel query processing. This upgrade speeds up vector operations, including index builds and similarity searches. Memory management also improves, which helps with large embedding datasets.

Use Cases

Teams use pgvector in many real-world scenarios:

1. Intelligent automation for database operations

2. Enhanced analytics for deeper insights and predictions

3. Adaptive query processing for dynamic workloads

Developers build recommendation engines, search platforms, and AI-powered chatbots using pgvector. Companies use it to analyze customer behavior, match images, and process natural language. The extension supports seamless integration of vector operations, which helps organizations unlock new possibilities in data science and machine learning.

• pgvector now stands as a mainstream tool for AI and vector search in PostgreSQL.

• It supports natural language processing, image analysis, and predictive analytics.

• Many businesses rely on pgvector to deliver smarter, faster, and more flexible applications.

Install Extensions

Methods

Installing extensions in PostgreSQL follows a clear process. Users often start by connecting to their database with the psql command-line tool. The steps below outline a standard approach:

1. Connect to the database using psql.

2. List available extensions with:

   1 2

   \dx

3. Install an extension by running:

   1 2 3

   CREATE EXTENSION extension_name;

4. Confirm the installation by checking the list of installed extensions.

Many users also rely on their operating system’s package manager, such as apt or yum, to install extension packages. Package managers handle dependencies and updates, making the process reliable. Official repositories like PGDG offer a wide range of pre-packaged extensions, which simplifies installation and maintenance.

Tip: Always check if the extension is available in your PostgreSQL version before installing.

Compatibility

Compatibility can present challenges when working with extensions. Some extensions may not work well together. For example, about 17% of tested extension pairs have shown incompatibility, which can cause errors or crashes. The table below highlights common compatibility issues:

Users should review documentation and test extensions in a safe environment before deploying them in production.

Cloud Tips

Cloud platforms like AWS RDS or Google Cloud SQL often restrict which extensions users can install. Providers may modify extensions to remove risky features. Before deploying, users should:

• Check the list of supported extensions for their cloud provider.

• Test extension behavior in a staging environment.

• Monitor for updates, as cloud providers may change extension availability or features.

Note: Not all extensions are available in managed cloud services, so plan deployments accordingly.

Manage PostgreSQL Extensions

Managing extensions helps database administrators keep systems healthy and efficient. Good management practices prevent errors and support smooth upgrades.

List Extensions

To see which extensions are active in a database, users can run a simple command. This step helps teams track installed tools and check for outdated versions.

1. Connect to the database using a tool like psql.

2. Run the following command to list all installed extensions:

   1 2

   \dx

3. Review the output. The table will show the extension name, version, and description.

Tip: Regularly checking installed extensions helps teams spot unused or outdated tools.

Upgrade/Remove

Upgrading and removing extensions keeps the database secure and up to date. The following steps guide users through these tasks:

To upgrade an extension:

1. Check for available updates with:

   1 2	SELECT * FROM pg_available_extensions WHERE installed_version IS DISTINCT FROM default_version;

2. Upgrade an extension by running:

   1 2	ALTER EXTENSION extension_name UPDATE;

To remove an extension:

1. Make sure no critical data depends on the extension.

2. Remove it with:

   1 2	DROP EXTENSION extension_name;

Always test changes in a non-production environment first. Back up the database before upgrading or removing extensions.

Following these steps helps teams manage PostgreSQL extensions safely and efficiently.

Choose the Right Extensions

Assess Needs

Selecting the right extension starts with understanding the project’s requirements. Teams should look at the type of data, expected workload, and performance goals. They must decide if the project needs geospatial analysis, time-series data, or advanced search features. Each extension solves a specific problem.

Steps to assess needs:

1. Identify the main data types in use.

2. List the core features the application must support.

3. Estimate the size and growth rate of the database.

4. Check if the project needs real-time analytics or AI capabilities.

5. Review compatibility with existing tools and cloud platforms.

Tip: Teams should test extensions in a development environment before using them in production.

The table below helps match common project needs with recommended extensions:

Best Practices

Safe and effective use of extensions requires careful planning. Teams should follow best practices to avoid problems and keep systems secure.

• Always back up the database before installing or removing extensions.

• Test upgrades in a staging environment.

• Monitor extension performance and update regularly.

• Review documentation for each extension.

• Limit the number of extensions to those that meet clear needs.

Note: Extensions can affect database stability. Teams should monitor changes and keep PostgreSQL updated.

A checklist for best practices:

• Backup data before changes.

• Test in non-production environments.

• Monitor for compatibility issues.

• Keep documentation handy.

• Update extensions and PostgreSQL as needed.

PostgreSQL extensions change how teams build and manage databases. They unlock new features, boost performance, and support advanced workloads. Readers can:

• Explore each extension to find the best fit for their projects.

• Experiment with new tools to improve database results.

• Share experiences or tips with the PostgreSQL community.

Stay updated with the latest extension releases to keep PostgreSQL deployments strong and flexible.

Recommended reading

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

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