Understanding PostgreSQL

An elephant in a high-tech data center in neon colors.

Written by Dylan Paulus

PostgreSQL is one of the world's most widely used open-source database systems, used by giants like Microsoft and Amazon. Its popularity stems from its robust feature set, reliability, and active development community. Learning about PostgreSQL is absolutely crucial for any developer, analyst, or database administrator. In this guide, we'll introduce you to PostgreSQL, exploring its strengths and weaknesses and how it can power your applications.

PostgreSQL Overview

What is PostgreSQL?

PostgreSQL, an open-source object-relational database management system (ORDBMS), traces its roots back to POSTGRES, a relational database developed at the University of California, Berkeley Computer Science Department in 1986. Over the years, it has evolved and grown, adding SQL query language support in 1994 and becoming the PostgreSQL we know today. Its versatility is evident in its use across diverse industries, from underground mining to archiving space missions, enhancing eldercare, providing infrastructure in Japan, and beyond.

As mentioned, PostgreSQL is considered an ORDBMS database. An ORDBMS is a superset of a relational database (RDBMS), which stores data in tables, rows, and columns related to each other. ORDBMS adds support for object-oriented features like user-defined data types and inheritance.

At its core, PostgreSQL follows a client-server architecture. The server (PostgreSQL) stores the data, manages ACID (Atomicity, Consistency, Isolation, and Durability) compliance, and processes queries. Clients, like applications or command-line tools, connect to the PostgreSQL server and submit SQL commands to modify or retrieve data. This architecture has the benefit of allowing multiple clients to access the database concurrently.

A diagram representing the client-server connection in PostgreSQL.

PostgreSQL pros and cons

Like any technology, PostgreSQL comes with its own set of advantages and limitations.

Pros:

  • Rich features and extensions: PostgreSQL provides a wide array of features out-of-the-box, including transactions, foreign keys, views, triggers, and sophisticated data types like arrays and JSON. Its extensible nature allows for adding custom data types, functions, and even programming languages.

  • Open source: As an open-source project, PostgreSQL benefits from a large and active community of developers who contribute to its continued improvement and maintenance.

  • Performance, reliability, and scalability: PostgreSQL is known for its exceptional performance, reliability, and scalability, making it suitable for handling large datasets and high-traffic applications.

  • Proven: PostgreSQL has been used in some of the largest companies in all different industries for over 30+ years.

Cons:

  • Single intake: PostgreSQL can only ingest data from a single source at a time, which may limit its throughput for high-volume data ingestion scenarios

  • Configuration overload: PostgreSQL is highly customizable—which is one of its advantages, but this makes it tough for newcomers to get the most out of the database (for example, fine-tuning autovacuum can be cumbersome)

  • Column storage limits: PostgreSQL stores data in a row-based format, which can be less efficient for analytical workloads that primarily access a subset of columns.

"Postgres for Everything"

Over the years, PostgreSQL has evolved into a highly reliable and feature-rich database system thanks to its active development community and extensive collection of extensions. Extensions augment PostgreSQL's capabilities, allowing it to tackle many use cases beyond a traditional relational database. 

For example, TimescaleDB is built on top of PostgreSQL, adding time-series support. Pgvector turns PostgreSQL into a vector database—useful for machine learning and AI—while pgai and pgvectorscale complement pgvector to make PostgreSQL a better AI database. PostGIS adds geospatial data and querying to PostgreSQL. Finally, PostgreSQL comes built-in with JSON/B support, allowing it to support NoSQL schemas.

Combining extensions with PostgreSQL's extensive feature set means that PostgreSQL can handle any use case you throw at it. You no longer need a wide set of databases specially built for narrow use cases. PostgreSQL does it all without compromising speed, security, and reliability!

A diagram that shows a complex setup with multiple tools, and a much simpler one where you build your application with Postgres.

To learn more about PostgreSQL's versatility and the diverse array of extensions available, check out our article on "Postgres for Everything."

PostgreSQL Development

Now that we know what PostgreSQL is, let's look at how to use it. In this section, we'll quickly examine how to use Python to connect to and query PostgreSQL and practices around data management.

Building with PostgreSQL and Python

Python is one of the world's most popular languages for developing applications. When combined with PostgreSQL, developers can leverage the power of both technologies to build robust and scalable solutions.

There are many different libraries out there that allow us to talk to PostgreSQL using Python. The psycopg library, a PostgreSQL adapter for Python, is the most popular. 

To start, make sure Python is installed and that an instance of PostgreSQL is running. Install psycopg using pip (pip install psycopg or python -m pip install psycopg). Once installed, we can connect to and query PostgreSQL using the example below (replace psycopg.connect(".....") with a valid connection string to your PostgreSQL instance: import psycopg

# Connect to PostgresQL
with psycopg.connect("dbname=mydb user=postgres password=secret") as conn:

    # Create a cursor to perform database operations
	with conn.cursor() as cursor:

		# Execute a SQL query 
		cursor.execute("SELECT * FROM my_table;") 

		# Fetch the results from PostgreSQL, and bring it into memory
		results = cursor.fetchall() 

		# Print all resulting rows from the SQL query   
		for row in results:
			print(row)

This code snippet demonstrates how to establish a connection, execute an SQL query, and fetch the results. For more in-depth examples and best practices, refer to our guide on Building Python Apps with PostgreSQL and psycopg3.

Data management with PostgreSQL

Effective data management is crucial for ensuring the integrity, availability, and performance of your PostgreSQL-based applications. Our guide on Postgres Data Management Best Practices outlines four key pillars: usability, security, documentation, and automation.

1. Usability: your data in PostgreSQL should be accessible and quick to query. Design database schemas for your exact use case, and use proper and consistent naming conventions.

2. Security: security is essential in establishing and maintaining customer trust—any decision around data management should start with security.

3. Documentation: there is no better way of communicating best practices and technical decisions than through documentation. Having clear and accurate documentation ensures your data is understandable and maintainable over time.

4. Automation: once the other pillars are established, we can automate them to enforce best practices, test for security vulnerabilities, create documentation, and more.

The Data Lifecycle Management (DLM) process is necessary when handling large amounts of generated data, for example, in time-series data. DLM is broken down into five steps:

1. Ingest and store: efficiently ingesting and storing data in PostgreSQL.

2. Recent data queries: optimizing queries for accessing recent data.

3. Aggregated rollups: creating and maintaining aggregated data rollups for analytical workloads.

4. Archive data: archiving and managing historical data effectively.

5. Delete data: identifying and securely deleting obsolete data.

The data management lifecycle.

Following these best practices, you can build robust, secure, and scalable applications using PostgreSQL.

PostgreSQL Performance

As applications become more complex and data volumes increase, optimizing PostgreSQL's performance becomes crucial for ensuring smooth operation and a positive user experience.

PostgreSQL performance

Our guide on PostgreSQL Performance covers key aspects of table design, including the following:

Table design

Proper table design plays a significant role in PostgreSQL's performance. For example, normalizing is the process of organizing your data into smaller, related tables to reduce redundancy and improve data integrity. However, there is a balancing act between normalization and query complexity. Excessive normalization can lead to complex joins and potentially degrade performance. Striking the right balance between narrow and wide tables for your specific use case can be huge for fast queries in PostgreSQL.

At a certain point, your PostgreSQL database will grow too large and encounter performance problems. Table partitioning is a technique that can help break down a large table into smaller physical pieces using criteria like date ranges or geographical regions.

Query design

It's important to design our tables efficiently, but we also need to optimize how we query PostgreSQL. When using SELECT statements, avoid using * to return all columns and return only the data needed (e.g., SELECT id, first_name FROM people;). We can speed up queries that utilize WHERE clauses and joins by applying proper indexes on tables and columns. Rewriting complex queries to use CTEs (Common Table Expressions) allows the query planner to cache results.

All of these techniques will not only make your queries very fast but also reduce the amount of memory and I/O PostgreSQL uses.

Scaling PostgreSQL

As your applications' data and demand grow, you will need to scale PostgreSQL to maintain performance and availability. In our guide on Best Practices for Scaling PostgreSQL, we touch on topics like multi-process and batch ingestion, materialized views, and partitioning.

  • Multi-Process and batch ingest: Utilizing multiple processes and batch ingestion to increase data ingestion throughput.

  • Materialized views: Creating and maintaining materialized views to improve query performance for analytical workloads.

  • Partitioning: Partitioning tables based on criteria like time ranges or geographical regions to improve query performance and maintenance efficiency.

Timescale improves on PostgreSQL's existing scaling features by adding:

  • Storage Space Optimizations

    • Tiered storage stores data on different hardware based on how frequently it's accessed—optimizing cost and freeing up space.

    • Column compression combines multiple rows of data to improve query speed, reduce costs, and reduce disk space.

  • Hypertable Partitioning

    • Hypertables enable seamless partitioning of time-series data across time and an optional additional attribute.

  • Continuous Aggregates

Conclusion

PostgreSQL is proven to be a robust, feature-rich, and highly extensible database capable of powering a wide range of applications across various industries. By understanding its strengths, limitations, and best practices, you can leverage its full potential to build secure, efficient, and scalable solutions. This is just the start of our journey. Timescale offers a diverse collection of articles and guides to amplify your PostgreSQL mastery.

To learn more about how Timescale can supercharge your time-series workloads, check out our blog post on PostgreSQL and TimescaleDB: 1,000x Faster Queries, 90% Data Compression, and Much More.