Best Tools for Developing Rich Data Apps with PostgreSQL

Tools can enhance PostgreSQL development, aiding database management, schema design, data migration, and application development.​ Here are some popular picks according to function:

Database Management Tools

Postgres Enterprise Manager

Postgres Enterprise Manager (PEM) is built on and extends the capabilities of the pgAdmin open source management tool for PostgreSQL databases by adding monitoring, analysis. and optimization. Postgres Enterprise Manager provides tools to keep databases running smoothly, continuously monitoring database and server health with real-time graphical dashboards and automatic alerts. When issues are detected, PEM makes it easier to pinpoint and fix performance bottlenecks with integrated query profiling, performance, and log analysis tools

pgAdmin

pgAdmin is a leading open source management tool for PostgreSQL databases. It provides a robust graphical interface that simplifies database objects’ creation, maintenance, and management. It has extensive features, such as a powerful query tool with syntax highlighting, a fast data grid for easy data entry, and graphical representation of query plans.

DBeaver

DBeaver supports a wide range of database systems, including PostgreSQL. It is praised for its rich feature set, including SQL query execution, syntax highlighting, and data visualization capabilities, allowing users to visualize geographical data directly on maps. Users can also manage connections to various database types in one application, enhancing productivity.

DataGrip

DataGrip is a comprehensive integrated development environment (IDE) for SQL developers and database administrators. It provides a smart data editor with functionalities such as auto-completion, code snippets, and robust support for managing various database systems, including PostgreSQL. It offers exceptional SQL log management, integrates tools for data extraction in multiple formats, and supports advanced PostgreSQL features such as foreign data wrappers.

Lasso

Lasso is a small multi-platform application that safely gathers relevant diagnostics data on a system where Postgres and other relevant supported software, such as Barman, is running. You can also run Lasso on systems where Postgres isn't installed to gather all relevant information about the underlying operating system.

LiveCompare

LiveCompare is designed to compare any number of databases to verify that they are identical. The tool compares the databases and generates a comparison report, a list of differences, and handy DML scripts so you can optionally apply the DML and fix the inconsistencies in any of the databases.

Postgres Workload Report

Postgres Workload Report (PWR) is a Python-based tool used for building PostgreSQL workload reports in HTML, Markdown, DOCX, and PDF mode. These reports mimic the reports provided by the Automatic Workload Repository (AWR) reporting tool from Oracle. Using a Postgres connection, you can execute Postgres Workload Report from any machine with access to the server for which you want a report. Postgres Workload Report uses Lasso for collecting data, so Lasso is a prerequisite.

Client Libraries

Psycopg2

Psycopg2 is a widely used PostgreSQL adapter for Python. It is known for its complete Python DB API 2.0 specification implementation, making it highly compatible with Python applications. Developed in C, Psycopg2 offers efficiency and thread safety, allowing multiple threads to share the same database connection without conflicts. It supports advanced PostgreSQL features such as asynchronous programming, server-side cursors, and the COPY TO/COPY FROM command, which can enhance data import and export processes.

Npgsql

Npgsql is the open source .NET data provider for PostgreSQL, allowing applications developed in C#, Visual Basic, and F# to interact seamlessly with PostgreSQL databases. Developers can manage database connections and execute SQL commands effortlessly, leveraging the ADO.NET framework. Npgsql provides a range of features, including support for LINQ queries, entity framework integration, and the ability to handle PostgreSQL-specific data types efficiently.

JDBC

JDBC, or Java Database Connectivity, is a standard API that allows Java applications to connect and interact with databases, including PostgreSQL. The PostgreSQL JDBC Driver (PgJDBC) implements this standard and enables Java programs to use a robust database connection with minimal configuration. One of the key benefits of using JDBC with PostgreSQL is its ability to provide database-independent operations, simplifying the development process when switching between different databases. The JDBC driver supports all PostgreSQL features, including stored procedures and advanced data types.

Development Frameworks

Django (with Django ORM)

Django is a high-level Python web framework encouraging rapid development and clean, pragmatic design. One of its key features is the Django ORM (Object-Relational Mapping), which allows developers to interact with databases intuitively by mapping Python objects to database tables. This abstraction layer eliminates the need to write raw SQL queries, making it easier to manage database operations. Django’s ORM seamlessly integrates with PostgreSQL, allowing for custom PostgreSQL fields, full-text search, and advanced querying capabilities while maintaining data integrity and security.

Flask

Flask is a lightweight and flexible web framework for Python that is suitable for small- to medium-sized applications. Unlike Django, which follows a more opinionated structure, Flask is minimalistic, allowing developers to structure their applications as they see fit.

When using Flask with PostgreSQL, the psycopg2 library is commonly adopted to manage database connections and execute SQL commands. Flask also supports various extensions, such as Flask-SQLAlchemy, which integrates SQLAlchemy ORM into Flask applications, allowing for more complex object-relational mapping.

Spring Boot

Spring Boot is an extension of the Spring framework that simplifies building stand-alone, production-ready Spring applications. It is well suited for Java developers looking to create microservices or RESTful APIs. Spring Boot provides a comprehensive application configuration and deployment approach, enabling quick setup with minimal boilerplate code.

To integrate PostgreSQL with Spring Boot, developers must include the PostgreSQL JDBC driver in their project’s configuration file (pom.xml). Spring Boot supports various data access strategies, including Spring Data JPA and traditional JDBC. Spring Data JPA allows for the automatic mapping of Java entities to database tables, coupled with built-in methods for handling CRUD operations efficiently.

Client libraries and APIs are necessary for integrating PostgreSQL in applications.​ They facilitate communication between applications and databases, enabling efficient data manipulation and enhancing overall functionality.

Client Libraries

Python libraries

Psycopg2 is the most widely used library for PostgreSQL in Python. It implements the Python DB API 2.0 specification and is recognized for its high performance due to its C implementation. Notable features include client-side and server-side cursors, asynchronous notifications, and using COPY TO/COPY FROM for bulk data transfers.

Psycopg3 is under active development and aims to expand on the functionalities provided by Psycopg2, introducing features including asynchronous communication and enhanced data ingestion capabilities. Other libraries, such as pg8000 and py-postgresql, are also available, although they are pure Python implementations that may be less efficient than Psycopg2.

Java libraries

In the Java ecosystem, pgJDBC is a popular library, serving as a pure Java implementation of the JDBC interface for PostgreSQL. Meanwhile, PgNio offers an asynchronous client that incorporates PostgreSQL-specific features and aims to achieve high performance.

JavaScript libraries

JavaScript developers have several effective libraries for PostgreSQL, including postgresql-client (recently renamed postgres) and node-postgres. The former is written in TypeScript and is known for its strict typing and Promise-based asynchronous API. Node-postgres supports various database features, including connection pooling, parameterized queries, and asynchronous notifications. Meanwhile, pg-promise enhances usability by offering a flexible interface for managing database queries and functionalities with robust documentation.

C# libraries

Npgsql is the primary data provider for PostgreSQL in C#. It is open source, supports ADO.NET, and integrates perfectly with Entity Framework Core, providing a seamless experience for .NET developers. Npgsql supports a rich set of PostgreSQL data types and features such as failover and load balancing, enhancing application performance.

The library can be easily installed via NuGet packages, making it a preferred choice for C# developers looking for reliable connections to PostgreSQL.

Ruby libraries

Ruby-pg is the primary client library for PostgreSQL. It supports PostgreSQL versions 9.3 and later and offers features such as type casting, thread safety, and high compatibility with Ruby on Rails. It supports various data types including JSON and JSONB and offers capabilities for defining type maps.

Go libraries

In Go, pgx and pq are the leading PostgreSQL driver options. Pgx is a pure Go driver that offers a low-level interface for direct database interactions and compatibility with the standard database/sql interface, enabling flexibility for developers.

Pq is a historically popular driver that is well tested but is in maintenance mode, making pgx the recommended option for developers requiring new features and updates. GORM and Bun are notable ORM libraries for Go, providing developer-friendly interfaces for building comprehensive database functionalities while supporting multiple database backends.

APIs and Extensions

Extensions

• PostGIS
  PostGIS is a spatial database extension that enables PostgreSQL to store and manage geographic data. It introduces support for geographic objects such as points, lines, and polygons, effectively transforming PostgreSQL into a full-fledged Geographic Information System (GIS) capable of advanced geospatial queries. PostGIS features include spatial data storage, efficient spatial indexing, and a vast range of spatial functions for analyzing and manipulating spatial data. This extension is widely used in mapping applications, urban planning, and environmental research.

• TimescaleDB
  TimescaleDB is an open source extension explicitly designed for time-series data management in PostgreSQL. Its key feature is the creation of hypertables, which automatically partition data across time intervals, facilitating efficient storage and querying of time-series data. TimescaleDB also offers advanced features including continuous aggregates that precompute and store aggregate data for faster querying, and data compression to reduce storage costs without sacrificing performance. This extension is essential for applications managing large volumes of time-series data, such as monitoring systems and IoT applications.

• pg_partman
  pg_partman is an extension that automates the management of partitioned tables in PostgreSQL. It streamlines the creation and maintenance of partitions based on time or numeric identifiers, enhancing query performance and simplifying database administration for large datasets. By automating tasks such as old partition detachment and new partition creation, pg_partman helps database administrators manage the lifecycle of partitioned data efficiently.

• Citus
  Citus is a PostgreSQL extension that allows users to scale out their database systems seamlessly. It provides features such as sharding, distributed tables, and a distributed query engine, enabling the database to efficiently handle large volumes of data and concurrent operations by distributing the workload across multiple nodes. Citus benefits multi-tenant applications and real-time analytics, providing high performance and availability.

• Foreign Data Wrapper (postgres_fdw)
  Foreign Data Wrapper (FDW) is a core feature in PostgreSQL that allows access to external data sources as if they were local tables. The postgres_fdw extension specifically enables PostgreSQL to connect and query data from other PostgreSQL databases. This capability facilitates data integration, allowing applications to aggregate data from multiple sources without performing extensive ETL operations. FDWs are crucial for applications leveraging distributed databases and data migration tasks.

PostgreSQL APIs

• PostgREST
  PostgREST is an API tool that automatically generates RESTful APIs from any PostgreSQL database schema. It lets developers quickly create APIs without writing extensive backend code, streamlining the development process. The APIs support CRUD operations, providing a seamless way for front-end applications to communicate with the database. This tool is good for rapid application development and microservices architecture.
  
  PostgREST has been added to EDB’s supported open source software list. This update unlocks more efficient and scalable web services by enabling customers to seamlessly convert their Postgres database into a RESTful API.

• Pipedream
  Pipedream is a platform that facilitates integration with PostgreSQL and more than 2,200 other applications. It allows developers to build workflows that connect PostgreSQL to various services, enabling real-time data exchange and automation of data manipulation tasks.

• Direct database APIs
  Many programming languages offer dedicated database client libraries as APIs for accessing PostgreSQL functionalities. For instance:

  • pgx and pq for Go
  • Npgsql for C#
  • pg_stat_statements provides a way to track execution statistics of SQL statements

Rich data applications can be developed more efficiently with the right tools and best practices. Database management tools such as DBeaver, client libraries including Psycopg2, and development frameworks such as Django are available. DevOps practices that include CI/CD integration, containerization, and performance tuning are also highly beneficial, as are client libraries and APIs facilitating communication between applications and databases. Data modeling, query optimization, and scalability and performance can also be furthered with the right habits, such as partitioning, prepared statements, and connection pooling.

By leveraging PostgreSQL’s advanced features, maintaining best practices, and engaging with the open source community, you can build applications that meet current demands and have the scalability to grow alongside future business needs. Embrace PostgreSQL as a fundamental part of your application development strategy to unlock its full potential for your projects.