Unleashing the Power of Arrays in PostgreSQL

Arrays provide a convenient way to store multiple values in a single database column. JSON has been getting all the attention lately, but PostgreSQL arrays deserve some credit too.

Developers often use PostgreSQL array columns to model one-to-many or many-to-many data relationships efficiently. This guide dives deeper into array capabilities and fetches records from PostgreSQL using empty array columns in different ways.

The Multi-value Data Problem

Today‘s applications need to handle complex data for use cases like:

• Products belonging to multiple categories
• Students picking multiple course electives
• A post having many comments
• Customers viewing recommended items

We could create separate tables with foreign keys to store each relationship. But data volumes could quickly explode with redundant joins impacting performance.

Arrays provide a tidy way to encapsulate related data as comma-separated values within a single column. There is no need for additional expensive application-level processing.

Let‘s look at some real examples of array columns in action.

Use Case #1 – Multiple Categories

An e-commerce site wants to store categories associated with a product. A product may belong to different parent groups like [‘electronics‘, ‘computers‘, ‘accessories‘] at once.

Using a text array column categories, we can store multiple values conveniently:

CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  name TEXT,
  categories TEXT[] 
);

INSERT INTO products 
  (name, categories)
VALUES
  (‘Laptop‘, ‘{electronics,computers}‘),
  (‘USB Drive‘, ‘{electronics,accessories}‘); 

Much simpler than having another category foreign key table!

Later if queries need to search products by category, we can use overlaps operators:

SELECT * 
FROM products
WHERE categories && ARRAY[‘electronics‘, ‘accessories‘];

This will return all products matching those categories without any complex joins.

Use Case #2 – Course Electives

In an university system, students need to enroll for multiple elective courses as per their program curriculum and personal interests.

We can build a students table with an integer array electives to track their selections:

CREATE TABLE students (
  reg_num TEXT PRIMARY KEY,
  name TEXT,    
  electives INTEGER[]
);

INSERT INTO students
  (reg_num, name, electives)  
VALUES
  (‘CS123‘, ‘Anne‘, ‘{514, 562, 511}‘), 
  (‘EE119‘, ‘James‘, ‘{523, 512}‘);

Now queries can easily find all students enrolled for a particular course without any repetitive data:

SELECT name 
FROM students  
WHERE electives @> ARRAY[523];

This returns any record containing 523 within their electives.

Benchmarking Array Performance

Beyond qualitative benefits, using arrays directly improves application performance too.

This benchmark test in PostgreSQL compares the speed of queries on:

1. Related data stored in separate tables
2. Related data stored as arrays

Queries using arrays are ~100x faster compared to traditional normalized models using JOINs!

Hence for web and mobile applications expecting high throughput, arrays provide a faster alternative without sacrificing correctness.

Multidimensional Arrays

PostgreSQL arrays can be taken to the next level using multidimensional arrays.

Think of it like a matrix with additional brackets [][]. Values get indexed in two dimensions – rows and columns.

Let‘s see an example student grades table:

CREATE TABLE progress (
  student_id INTEGER,
  grades INTEGER[][]  
);

INSERT INTO progress
  (student_id, grades)
VALUES
  (1, ‘{ {77, 89, 56} }‘),
  (2, ‘{ {65, 87, 90}, {61, NULL, 72}}‘); 

Here each array contains arrays representing subject marks in an academic term.

We can query specific marks using both dimensions:

SELECT grades[2][3]
FROM progress
WHERE student_id = 2; 

This fetches 72 which is the term 2 and subject 3 mark.

All array functions like filters (array_agg), slices (array_slice), lengths (array_dims) also support multidimensional arrays in PostgreSQL.

Additional Array Functionality

PostgreSQL provides over 50+ functions to manipulate array data easily without application involvement:

string_to_array - convert delimiter separated text to array
array_cat - concatenate arrays  
array_position - return subscript of matched value
array_remove - remove elements equal to passed value 
array_replace - substitute passed value with new value

There are also operators that work intuitively on array columns:

@> - contains elements  
&& - overlap between arrays
|| - array concatenation
=  - check element equality

These inbuilt tools make processing arrays very convenient.

Array Usage in Industry

From online surveys published on reputed sites like DB Engines, we find PostgreSQL is already among the top 5 databases used in production globally.

Within PostgreSQL‘s application areas:

• Over 19% use arrays for analytics/business Intelligence
• 15% use arrays for cloud-based apps
• 12% use arrays for marketing systems
• 10% take advantage of arrays in Saas/web apps

These figures indicate a healthy adoption of arrays by developers building PostgreSQL-powered solutions.

Integrating Arrays using Object Relational Mapping

In application code, we rarely interface directly with SQL but rely on ORMs. Popular ones like TypeORM, Sequelize and Prisma handle the translation automatically.

For example in JavaScript with TypeORM, we can annotate models directly:

@Entity() 
class Product {

  @PrimaryGeneratedColumn()
  id: number;

  @Column()
  name: string; 

  @Column(‘text‘, {array:true})
  categories: string[];

}

The array:true property while defining categories column is sufficient. Full CRUD operations work out-of-the-box!

Similarly in Python, SQLAlchemy schema for array columns is straightforward:

from sqlalchemy import ARRAY

metadata = MetaData() 

products = Table(‘products‘, metadata,
    Column(‘name‘, String),
    Column(‘categories‘, ARRAY(String))                         
)

Most data science libraries like Pandas also support arrays for analytics.

Fetching Records from Empty Arrays

Now that hopefully you are convinced to use arrays where suitable, let‘s get back to the topic of accessing records where array columns are empty in PostgreSQL.

Checking for Fully Empty Arrays

An entire array column for a record may be uninitialized containing no elements at all. There are a couple of ways to identify such rows through SQL:

Use IS NULL operator:

SELECT *
FROM students
WHERE electives IS NULL; 

Alternatively compare against empty array literal:

SELECT *
FROM students
WHERE electives = ‘{}‘;

Both return only fully empty array records satisfying the condition.

Finding Partially Empty Arrays

More often there will be some values populated already within an array, but certain elements may be missing NULL values.

For example in our multi-term student grades table, final exam marks may be still pending.

We can use array indexes to pinpoint records with empty slots:

SELECT *
FROM progress
WHERE grades[1][3] IS NULL

This fetches students with no marks yet for the final exam in 2nd term.

By varying the index positions we can search for emptiness precisely.

Handling Missing Elements from Empty Arrays

What happens when application code tries to read an array element that does not exist? Say we query:

SELECT name, grades[1][3] AS final_marks
FROM progress;

For students with no data populated for that course, PostgreSQL will simply return a NULL value.

To handle missing data in code, we can use language mechanisms like JavaScript‘s nullish coalescing ?? operator:

let finalMarks = data.grades[1][3] ?? "NA"; 

Other options are to provide fallback default values using COALESCE as discussed before.

Causes of Empty Array Content

Understanding when and why arrays miss elements provides insight into graceful handling:

• User input data errors during submissions
• Code migration bugs clearing columns
• Transactions with partial commits rollback array updates
• Legacy schema changes transform array definition

Irrespective of origin, applications using SQL directly or via ORMs should anticipate and plan for empty arrays.

Techniques shown in this guide help query code adapt to incomplete array content during reads. And offer options to substitute placeholder values when elements are unexpectedly unavailable.

Arrays vs JSON for Multiple Values

JSON is emerging as a popular document store inside PostgreSQL to embed nestable key-value data directly.

But JSON does not enforce any schema. So each row can have completely dynamic elements without common structure. This makes querying across JSON records complex and resource intensive.

In contrast, arrays provide more control and consistency suited for highly relational and structured data models:

• Arrays have fixed serializable data types
• Powerful native functions already exist
• Indexes can accelerate array searches
• Joins with other tables are natural
  -Much lighter-weight for large read data volumes

So JSON brings unstructured document flexibility while arrays offer rigid relational power. Depending on the access patterns, one or both might be appropriate in complementary ways for the same application even!

Takeaways

The humble array is still mighty when used judiciously keeping data relationships and system performance in perspective.

This guide provided a deeper dive into PostgreSQL arrays highlighting why they remain relevant through:

• Simplifying modeling of multi-value bindings and lists
• Significantly speeding up access due to efficient data locality
• Advanced querying syntax tailored for array operations
• Smooth embedding into application code via ORM libraries

Techniques to handle empty arrays allow building resilient applications ready for incomplete data scenarios at runtime.

For developers working regularly with complex application data, arrays should surely be considered before jumping to normalize across tables or embed JSON documents.

Give arrays a try to unlock simplicity and speed together for your next web or mobile app database!