Introduction

Arrays allow storage of multiple data values in a single column variable, facilitating efficient data manipulation in SQL. We no longer need to repeat values across rows or conduct complex joins for recurring data.

This comprehensive guide aims to equip readers with an in-depth understanding of arrays in SQL from basic to advanced features. We‘ll explore array theoretical concepts, tackle common use cases, and even craft custom array types in PostgreSQL.

So let‘s get started!

What Are Arrays in SQL?

An array is an ordered collection of elements of the same data type. The array variable stores a list of values that can be accessed via an index position.

For example, a string array holds multiple text values, while an integer array stores several whole number values.

Unlike multi-dimensional arrays, SQL arrays are linear with only a single dimension. Each element resides along the same axis.

SQL arrays have several advantages over repeating columns:

  • More efficient storage for repetitive data
  • Keeps related data within the row
  • Simpler manipulation than joins
  • Faster access than separate queries

As a full-stack developer using PostgreSQL, arrays help reduce overall schema width allowing faster table scans and joins. Queries also avoid expensive column lookups to retrieve associated data.

Native SQL Array Support

Modern database systems with native array support include:

  • PostgreSQL – Full-featured array columns and functions
  • BigQuery – Limited multi-dimensional arrays
  • Redshift – Offers array data types and operations
  • IBM Db2 – Inline array columns and integration functions
  • SQL Server – Table columns can be defined as array types

However, MySQL and MariaDB still lack native virtual array columns. We‘ll cover some array workarounds for these databases later.

For now, we‘ll focus on PostgreSQL which has the most comprehensive array capabilities.

PostgreSQL SQL Array Types

PostgreSQL allows declaring any column as an array variable without a separate array construct.

Standard array types include:

  • Integer
  • Decimal
  • Boolean
  • Text
  • Date/Time

For example:

CREATE TABLE books (
  id integer,
  tags text[]  
);

This creates a books table with a text array field called tags to store genre labels.

We could also optionally constrain array length and dimensions:

num_arr integer[3][3]

But PostgreSQL does not enforce 2D array size limits.

Creating Arrays in SQL

There are several techniques to instantiate SQL arrays:

1. Array Literal Notation

This directly initializes an array inline using curly braces and values separated by commas:

SELECT ‘{}‘::integer[] AS empty_arr;
SELECT ‘{5,8,2}‘::integer[] AS num_arr;

Drawbacks are lack of reusability and challenges with parameterization.

2. Array() Constructor

The ARRAY() constructor offers a more standardized method:

SELECT ARRAY[1,5,7] AS num_arr;

This allows passing an array to functions as a parameter.

3. String Manipulation

We can also convert string representations into arrays:

SELECT string_to_array(‘1~5~7‘, ‘~‘);

The delimiter could be any non-digit character.

4. generate_series() Function

This handy function generates an array based on a range of numbers:

SELECT generate_series(1, 10);

Output:

{1,2,3,4,5,6,7,8,9,10}

We can also customize increment steps. Extremely useful for numeric sequences!

Overall, the ARRAY() constructor provides the most flexibility to generate arrays in SQL.

Accessing SQL Array Elements

SQL arrays utilize zero-based numbering for access by index position:

SQL Array Indexes

To fetch an element, we specify the target array followed by index inside square brackets:

SELECT my_array[0] AS first;

Alternatively, PostgreSQL provides an ordinal method using curly braces:

SELECT my_array{1} AS second;

Where {1} represents the 1st element.

Key things to remember:

  • Arrays start at index 0 for brackets []
  • Curly braces {} use index starting from 1
  • Access returns NULL if index does not exist

Now we can manipulate arrays in SQL!

Determining SQL Array Length & Size

While arrays seem like simple lists, understanding the length versus size properties is key to handling boundaries properly.

The array_length() function returns the total count of elements:

SELECT array_length(num_arr, 1) AS len FROM num_arr;

But this includes NULL values.

To get the non-NULL element count, use cardinality(): 

SELECT cardinality(num_arr) AS size FROM num_arr;

Why does this distinction matter?

By separating length and size, we can detect gaps within arrays. This allows identifying missing elements by comparing the total length against non-NULL cardinality.

Pretty handy for handling sparse arrays!

Building Arrays in SQL

Beyond initial creation, arrays can be constructed gradually using the following functions:

1. array_append()

Adds elements to the end of an array:

SELECT array_append(num_arr, 11) AS appended;

2. array_prepend()

Similar to append(), but inserts elements at the front: 

SELECT array_prepend(0, num_arr) AS prepended;

3. array_cat()

Concatenates multiple arrays together into one:

SELECT array_cat(arr1, arr2) AS concat;

4. unnest()

Expands array into rows for easier manipulation:

SELECT * FROM unnest(num_arr);

These methods enable assembling arrays element-by-element in SQL.

Searching Elements Within Arrays

SQLite arrays lack traditional comparison operators that make searching for values tricky.

Instead, PostgreSQL offers several array-specific search functions:

1. array_position()

Locates the index location of a given element:

SELECT array_position(num_arr, 8);

2. array_contains()

A boolean check if a value exists in the array:

SELECT array_contains(num_arr, 5);

3. ALL() Operator

Test if all array elements match a condition:

SELECT * FROM int_arr WHERE ALL(int_arr > 10);

4. ANY() Operator

Opposite of ALL(), returns true if any one element meets criteria:

SELECT * FROM int_arr WHERE ANY(int_arr = 5);

These provide powerful methods to both find and filter arrays.

Sorting Elements Within Arrays

To reorder array data, PostgreSQL provides:

1. array_sort()

Sorts collated UTF8 elements: 

SELECT array_sort(char_arr);

2. array_agg()

Aggregates array while optionally ordering:

SELECT array_agg(id ORDER BY value) FROM unnest_arr;

For numeric data, use:

3. array_position() Compare

Locate position of each value to induce sort order:

SELECT array_position(race_times, t) AS pos
FROM race_times, unnest(race_times) AS t
ORDER BY 1;

So we have all the tools needed to reorder arrays as needed.

Array Manipulation Using Loops

Processing arrays in SQL often involves iterating through elements.

But unlike programming languages, SQL lacks traditional looping constructs that make repeated element access tricky.

We can emulate a loop using a lateral join to plug each value into a function:

SELECT 
  i,
  get_state(zip_codes[i]) AS state
FROM
  zip_array,
  generate_subscripts(zip_codes, 1) AS i;

Where:

  • generate_subscripts() – Returns array indexes
  • zip_codes[i] – Pass element into function
  • Lateral join processes row-by-row

This allows iterating without cumbersome cursors or variables.

Of course, we must ensure the get_state() function handles errors properly.

Aggregating Array Elements

Another common task is accumulating values across array elements.

For example, finding the total sum of integers stored in multiple arrays.

We can "unnest" array elements into rows using unnest():

SELECT 
  SUM(vals) AS total
FROM
  int_arrays, 
  unnest(int_arrays) AS vals;

This unwraps array elements into a column we can sum, average, filter on, and so on.

Other aggregation examples include:

SELECT
  MIN(vals),
  MAX(vals),
  COUNT(vals)
FROM 
  int_arrays, unnest(int_arrays) AS vals;

Giving tremendous flexibility to analyze array contents.

Converting Arrays to Strings

A common need is flattening arrays into strings for storage or display.

The array_to_string() function concatenates elements with a delimiter:

SELECT 
  array_to_string(arr, ‘,‘); 
FROM
  arr_table;

We can also cast arrays as string types:

SELECT 
  arr::text
FROM 
   arr_table;

The opposite operation parse strings into arrays is available via the string_to_array() function.

This takes delimiter strings and splits them accordingly.

Testing Array Equality in SQL

Determining if two arrays are identical seems like an easy comparison:

SELECT  
   array1 = array2
FROM 
   table;

But this only verifies pointers rather than values.

Instead, we need to expand arrays and check elements:

SELECT 
  array1 <@ array2 AS is_subset,
  array1 && array2 AS has_overlap, 
  array1 @> array2 AS is_superset   
FROM
  table;

These operators evaluate actual elements rather than memory only.

Some additional equivalence operators include:

  • @> – Left array contains right one
  • <@ Right array contained within left
  • && – Check if any common elements exist

This gives us the ability to deeply contrast array contents.

User-Defined Array Types

A lesser known feature of PostgreSQL is creating custom array types with validation rules.

For example, restricting elements to a specific range:

CREATE TYPE id_array AS range (subtype = integer);

CREATE FUNCTION verify_id(id_array) 
  RETURNS id_array AS
$$
BEGIN
  IF array_position(ARRAY[100,999], $1) IS NULL THEN 
    RAISE EXCEPTION ‘ID out of expected range‘; 
  END IF; 

  RETURN $1;
END; 
$$ LANGUAGE plpgsql;

This custom type only allows arrays holding integers from 100 to 999. The verify_id() function enforces the constraints.

We can attach this logic when defining columns:

CREATE TABLE users (
  names text[],
  roles int[] CHECK (verify_id(roles))  
);

Forcing all roles array to pass verification.

The key benefit over plain integers is the additional range check encapsulated into the array type. This makes the intention more semantic.

Arrays in Database Normalization

A normalized schema requires atomicity – splitting columns into singular facts.

But defining identifying attributes like tags across separate tables breaks data normalization best practices by:

  • Data duplication across child rows
  • Performance loss due to expensive joins
  • Higher storage needs
  • Data consistency risks across copies

Arrays preserve normalization while keeping values together by:

  • Avoiding repeated attributes
  • Minimizing total columns
  • Reducing join complexity
  • Enforcing single source of truth

This helps optimize read/write throughput crucial for high-volume and real-time systems.

Recommendations for SQL Arrays

Based on our exploration, some best practices emerge around leveraging arrays:

  • Use for distinct, infrequently changed data
  • Develop helper functions encapsulating logic
  • Note performance gains diminish above ~100 elements
  • Beware heavy overuse degenerating into EAV anti-pattern
  • Test edge cases like overflow, indexing errors
  • Evaluate JSON for loosely structured data

Following these tips will smooth adoption while avoiding pitfalls.

Conclusion

SQL arrays provide a lightweight method to normalize massive databases by reducing expensive joins. PostgreSQL offers comprehensive support to facilitate efficient data storage and manipulation.

We learned various techniques working with arrays:

  • Declaring array columns, types and variables
  • Constructing arrays from literals or queries
  • Accessing elements via index position
  • Useful methods to append, search and aggregate
  • Building custom types with validation rules

You‘re now equipped to harness arrays across real-world applications like analytics, scientific data and time-series analysis.

What other array operations or use cases have you found valuable? Let me know!

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