PostgreSQL, often written as "Postgres" and pronounced "Poss-gres", is an open source relational database implementation frequently used by Python applications as a backend for data storage and retrieval.
PostgreSQL is the default database choice for many Python developers, including the Django team when testing the Django ORM. PostgreSQL is often viewed as more feature robust and stable when compared to MySQL, SQLServer and Oracle. All of those databases are reasonable choices. However, because PostgreSQL tends to be used by Python developers the drivers and example code for using the database tend to be better documented and contain fewer bugs for typical usage scenarios. If you try to use an Oracle database with Django, you'll see there is far less example code for that setup compared to PostgreSQL backend setups.
PostgreSQL's open source license allows developers to operate one or more databases without licensing cost in their applications. The open source license operating model is much less expensive compared to Oracle or other proprietary databases, especially as replication and sharding become necessary at large scale. In addition, because so many people ranging from independent developers to multinational organizations use PostgreSQL, it's often easier to find developers with PostgreSQL experience than other relational databases. There is also ancedotal evidence that PostgreSQL fixes bugs faster than MySQL, although to be fair there has not been a comprehensive study comparing how the two projects handle defect resolution.
To work with relational databases in Python you need to use a database driver, which is also referred to as a database connector. The most common driver library for working with PostgreSQL is psycopg2. There is a list of all drivers on the PostgreSQL wiki, including several libraries that are no longer maintained. If you're working with the asyncio Python stdlib module you should also take a look at the aiopg library which wraps psycopg2's asychronouos features together.
To abstract the connection between tables and objects, many Python developers use an object-relational mapper (ORM) with to turn relational data from PostgreSQL into objects that can be used in their Python application. For example, while PostgreSQL provides a relational database and psycopg is the common database connector, there are many ORMs that can be used with varying web frameworks, as shown in the table below.
Learn more about Python ORMs on that dedicated topic page.
If you're on Linux it's easy to get PostgreSQL installed using a package manager. However, once the database is installed and running your responsibility is just beginning. Before you go live with a production application, make sure to:
When possible have someone qualified do a PostgreSQL security audit to identify the biggest risks to your database. Small applications and bootstrapped companies often cannot afford a full audit in the beginning but as an application grows over time it becomes a bigger target.
The data stored in your database is the lifeblood of your application. If you have ever accidentally dropped a production database or been the victim of malicious activity such as SQL injection attacks, you'll know it's far easier to recover when a bit of work has been performed beforehand on backups, replication and security measures.
Many quickstarts and tutorials exist specifically for Django, Flask and other web application frameworks. The ones below are some of the best walkthroughs I've read.
Setting up PostgreSQL with Python 3 and psycopg on Ubuntu 16.04 provides instructions for getting a fresh Ubuntu install working with PostgreSQL and Python 3.
This post on using PostgreSQL with Django or Flask is a great quickstart guide for either framework.
This article explains how and why PostgreSQL can handle full text searching for many use cases. If you're going down this route, read this blog post that explains how one developer implemented PostgreSQL full text search with SQLAlchemy.
django-postgres-copy is a tool for bulk loading data into a PostgreSQL database based on Django models. Say hello to our new open-source software for loading bulk data into PostgreSQL is an introduction to using the tool in your own projects.
How to speed up tests in Django and PostgreSQL explains some hacks for making your schema migration-backed run quicker.
o Postgres Joins and Django Querysets
is a well done post with a specific example of how a standard
Django ORM query can lead to degraded performance due when obtaining
data from many related tables. The
prefetch_related command and
database performance monitoring tools can help analyze and alleviate
some of the issues in these unoptimized queries.
Loading Google Analytics data to PostgreSQL using Python is a quality tutorial that combines API calls with psycopg and PostgreSQL to take data from Google Analytics and save it in a PostgreSQL database.
PostgreSQL tutorials not specific to Python are also really helpful for properly handling your data.
Why PostgreSQL? (5 years later) covers the improvements that have been made to PostgreSQL over the past five years. It's amazing to see how far this project has come and how it continues to evolve.
PostgreSQL Weekly is a weekly newsletter of PostgreSQL content from around the web.
An introduction to PostgreSQL physical storage provides a solid walkthrough of where PostgreSQL files are located on disk, how the files store your data and what mappings are important for the underlying database structure. This post is an easy read and well worth your time.
Braintree wrote about their experiences scaling PostgreSQL. The post is an inside look at the evolution of Braintree's usage of the database.
There is no such thing as total security but this IBM article covers hardening a PostgreSQL database.
Handling growth with Postgres provides 5 specific tips from Instagram's engineering team on how to scale the design of your PostgreSQL database.
Inserting And Using A New Record In Postgres shows some SQL equivalents to what many developers just do in their ORM of choice.
Following a Select Statement Through Postgres Internals provides a fascinating look into the internal workings of PostgreSQL during a query.
Locating the recovery point just before a dropped table and logging transactions that dropped tables are two posts that show you how to recover from an accidentally dropped table. In the first post the author shows how recovery is possible with recovery points while the second post shows how to put logging in place to assist in future recoveries.
awesome-postgres is a list of code libraries, tutorials and newsletters focused specifically on PostgreSQL.
While you can use a graphical interface for working with PostgreSQL, it's best to spend some time getting comfortable with the command-line interface.
Backing up databases is important because data loss can and does happen. This article explains how to back up a PostgreSQL database hosted on an Amazon Web Services EC2 instance if managing your own database on a cloud server is your preferred setup.
How to fix undead PostgreSQL queries shows a bit of a hack for what to do when you can't kill certain PostgreSQL queries.
Is bi-directional replication (BDR) in PostgreSQL transactional? explores a relatively obscure topic with the final result that BDR is similar to data stores with eventual consistency rather than consistency as a requirement.
PostgreSQL-metrics is a tool built by Spotify's engineers that extracts and outputs metrics from an existing PostgreSQL database. There's also a way to extend the tools to pull custom metrics as well.
This slideshow on high availability for web applications has a good overview of various database setups common in production web applications.
The JSONB data type was introduced in PostgreSQL 9.4 to make it easier to store semi-structured data that previously NoSQL databases such as MongoDB covered. However, there are times when using JSONB isn't a good idea and this blog post covers when to avoid the column type.
Monitoring one or more PostgreSQL instances and trying to performance tune them is a rare skillset. Here are some resources to get you started if you have to handle these issues in your applications.
This guide to PostgreSQL monitoring is handy for knowing what to measure and how to do it.
Craig Kerstiens wrote a detailed post about understanding PostgreSQL performance.
The Practical Guide to PostgreSQL Optimizations covers using cache sizes, restore configurations and shared buffers to improve database performance.
This article on performance tuning PostgreSQL shows how to find slow queries, tune indexes and modify your queries to run faster.
What PostgreSQL tells you about its performance explains how to gather general performance metrics and provides the exact queries you should run to get them. The article also covers performance monitoring and how to analyze trigger functions.
PostgreSQL monitoring queries is a simple GitHub repository of SQL queries that can be run against a PostgreSQL instance to determine usage, caching and bloat.
PgSQL Indexes and "LIKE" examines why LIKE queries do not take advantage of PostgreSQL indexes when the locale is set to something other than the default "C", which is for the North American UNIX default. The gist is that you need to build a special index to support LIKE whenever you use a locale other than "C".
The PostgreSQL page on PopSQL has a ton of useful syntax snippets categorized by type of action you want to perform using SQL.
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