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Hello Reader, Welcome to Understanding PostgreSQL Data Types!

As an experienced database developer and PostgreSQL power user myself, I know firsthand how crucial choosing the optimal data types in PostgreSQL is for application success.

The data types form the building blocks of your database schema design – impacting everything from storage space, query performance, data integrity to scalability and functionality of your app.

This comprehensive guide will explain the commonly used 13 PostgreSQL data types in detail with expert advice on using them effectively. My goal is to help you become a PostgreSQL data type pro! 😊

Why Understanding Data Types Matters

But before we jump into the types, it‘s important to understand why data types matter so much in a robust relational database like PostgreSQL.

In short, data types enable PostgreSQL to validate, process and store your data intelligently. By utilising suitable data types you can reap the following key benefits:

✅ Data Integrity – Catch bad data during insertion itself
✅ Storage Optimization – Fit data precisely without wasted space
✅ Query Performance – Process data faster based on data types
✅ Data Validation – Constraints to accept only genuine data
✅ Developer Agility – Add new features easily thanks to flexible data structures

Now that I‘ve convinced you of how vital getting data types right is, let me reveal the superpowers certain PostgreSQL types bring to the table.

Numeric Types – Integers, Floats, Decimals & More!

PostgreSQL offers a complete suite of numeric data types – whole numbers, decimals, fractions, floating points – you name it!

Let me introduce you to this number family:

  • Integers – SMALLINT, INTEGER, BIGINT
  • Decimals – DECIMAL, NUMERIC
  • Floating Points – REAL, DOUBLE

Each numeric type lets you fine tune parameters like acceptable value range, decimal precision etc when creating table columns:

-- Allow any large whole number with max precision 
salary BIGINT  

-- Allow decimal numbers with 1000 digit precision & 2 decimal places
expense NUMERIC(1000,2)

But with great configurability comes great responsibility!

Tip 1️⃣: When storing financial data use DECIMAL for accuracy instead of float/real types since they can cause rounding errors during calculations.

Tip 2️⃣: Define upper bounds wisely keeping growth in mind. Don‘t be too narrow to allow future expansion.

Now that you know about storing exact numbers, let me tell you about my favorite workhorse data type…

TEXT Type – Superfast String Storage!

For general unbounded length strings, TEXT is my go-to in PostgreSQL. This variable length text type has been powering many enormous string columns flawlessly over the years with no arbitrary limits.

Some key advantages my fellow developers appreciate:

✅ No length limits – can store text of any size
✅ Higher limit than VARCHAR – max 1GB per value
✅ Faster retrieval than VARCHAR for large text blocks

Let me convince you of TEXT‘s might with an example.

Say you are building a blogging platform database. Your posts table would ideally use a TEXT column for storing article content:

CREATE TABLE posts (
  id SERIAL PRIMARY KEY,
  title VARCHAR(255),
  content TEXT -- unbounded length 
);

This allows flexibility to store anything from short tweets to long research posts without worrying about max length constraints!

Tip 💡: Avoid using TEXT for short distinct values needing lookups like usernames, as it can be slower than VARCHAR.

On that note, let‘s look at…

VARCHAR – Variable Length Strings

While TEXT stores arbitrarily long strings, the VARCHAR(n) data type meets the need for shorter but variable length texts like names, paragraphs etc.

By declaring a max length limit, you can size VARCHAR precisely as per your data requirements: 

author_name VARCHAR(50) -- 50 chars enough for author name
article_excerpt VARCHAR(1024) -- max 1KB excerpts

The limit also allows PostgreSQL to optimize storage and retrieval better than unbounded TEXT.

A couple of VARCHAR tips from my developer playbook:

Tip 1️⃣: Set length limits reasonably higher than the actual expected string sizes.

Tip 2️⃣: For fixed width data, use CHAR instead of VARCHAR padding.

That concludes the best practices for dealing with text! We have covered quite a lot haven‘t we? Let‘s switch topics and dive into one of my favorites…

Arrays – Store Lists, Sets, Collections

While traditional relational tables store data in rows and columns, sometimes you need more… flexibility.

This is where PostgreSQL array columns come in super handy!

These allow storing an array of values in a single column efficiently:

CREATE TABLE books (
  id INTEGER,
  tags TEXT[] -- array of text
)

INSERT INTO books 
  VALUES 
    (123, ‘{"Fiction", "Science"}‘), -- array of string tags
    (456, ‘{44, 22, 167844}‘); -- array of numbers

Benefits of using array columns:

✅ Cleaner schema design in many cases
✅ Easy manipulation of data in arrays with built-in functions
✅ Avoid expensive JOINs in certain use cases

For instance, searching books by tags becomes a simple WHERE tags @> ‘{"Fiction"}‘ query compared to a slow JOIN approach.

Tip 💡: Avoid arrays for very large column data – use normalization instead.

Now that you‘ve seen arrays in action, let me demo my next wondrous weapon – the JSON data type!

JSON Type – Flexible Semi-Structured Data

While relational data storage excels for normalized data, highly nested, schema-less data is easier managed via JSON documents.

PostgreSQL‘s JSON & JSONB data types let you store JSON seamlessly, with many query and indexing benefits.

See the JSONB type in action:

CREATE TABLE employee (
  id INTEGER, 
  details JSONB -- nested JSON structure 
);

INSERT INTO employee
  (id, details)
VALUES
  (1, 
   ‘{"name":"John", "contacts": {"phone":"88888888","email":"[email protected]"}}‘
  );

With JSONB columns, you can directly query properties nested deep down in the JSON document like so:

SELECT * FROM employee WHERE details->contacts->>phone = ‘88888888‘;

Benefits I really like:

✅ No need for rigid schemas – be flexible!
✅ Query & index JSON directly
✅ Useful for modern web/mobile applications

Do note that complex JSON queries can get inefficient at scale, so balance flexibility with performance.

We have covered a ton of ground working with all kinds of data – text, numbers, arrays, JSON. Let‘s mix things up and enter a whole new geometric dimension!

Geometric Types – Maps & Spatial Queries

Spatial data is exploding – whether it‘s GPS coordinates, retail store locations or complex contours.

This is where PostGIS (PostgreSQL Extension) packs a strong punch with its geometric data types:

  • Point
  • LineString
  • Polygon
  • More shapes!

These allow you to define columns to store geographic objects and then run advanced spatial queries efficiently:

CREATE TABLE cities (
  name VARCHAR(50),
  location POINT
);

INSERT INTO cities
  VALUES
    (‘Los Angeles‘, POINT(118.25, 34.05)); 

SELECT * FROM cities
  WHERE location <@> POINT(118, 34); -- find nearby

By integrating geographic types into your data model you unlock exceptional location-based querying that SQL alone cannot match!

In Conclusion – The Data Typescape is Your Oyster!

We have equipped you with expert knowledge on 13 essential PostgreSQL data types and how to pick the optimal ones for your needs confidently & wisely!

Here‘s a quick cheat sheet for you as a parting gift:

For Text

  • VARCHAR – Variable length strings
  • TEXT – Unlimited length strings
  • JSON/JSONB – Nested, flexible documents

For Numbers

  • INT – Integer numbers
  • DECIMAL – User-defined precision decimals
  • FLOAT – Approximate precision floating points

For Collections

  • ARRAY – Lists, sets of homogeneous values

For Geospatial

  • Point, Polygon, LineString – Geometric types & measurements

I hope this guide helps you become a PostgreSQL data type expert capable of designing stellar database schemas! Feel free to bookmark this page for quick reference later.

Yours databasely,
Leslie Postgre 🤓

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