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Postgres/Functions

Postgres json_array_elements() function

Expand a JSON array into a set of rows

You can use json_array_elements function to expand a JSON array into a set of rows, each containing one element of the array. It is a simpler option compared to complex looping logic. It is also more efficient than executing the same operation on the application side by reducing data transfer and processing overhead.

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json_array_elements(json)

json_array_elements example

Suppose you have a developers table with information about developers:

developers

CREATE TABLE developers (
 id INT PRIMARY KEY,
 name TEXT,
 skills JSON
);

INSERT INTO developers (id, name, skills) VALUES
 (1, 'Alice', '["Java", "Python", "SQL"]'),
 (2, 'Bob', '["C++", "JavaScript"]'),
 (3, 'Charlie', '["HTML", "CSS", "React"]');
| id |  name   |          skills          
|----|---------|---------------------------
| 1  | Alice   | ["Java", "Python", "SQL"]
| 2  | Bob     | ["C++", "JavaScript"]
| 3  | Charlie | ["HTML", "CSS", "React"]

Now, let's say you want to extract a row for each skill from the skills JSON array. You can use json_array_elements to do that:

SELECT id, name, skill
FROM developers,
    json_array_elements(skills) AS skill;

This query returns the following result:

| id |  name   |    skill     |
|----|---------|--------------|
| 1  | Alice   | "Java"       |
| 1  | Alice   | "Python"     |
| 1  | Alice   | "SQL"        |
| 2  | Bob     | "C++"        |
| 2  | Bob     | "JavaScript" |
| 3  | Charlie | "HTML"       |
| 3  | Charlie | "CSS"        |
| 3  | Charlie | "React"      |

Advanced examples

This section shows advanced json_array_elements examples.

json_array_elements with nested data

Let's consider a scenario where we have a products table storing information about products. The table schema and data are provided below.

products

CREATE TABLE products (
 id INTEGER PRIMARY KEY,
 name TEXT,
 details JSON
);

INSERT INTO products (id, name, details) VALUES
 (1, 'T-Shirt', '{"sizes": ["S", "M", "L", "XL"], "colors": ["Red", "Blue", "Green"]}'),
 (2, 'Hoodie', '{"sizes": ["XS", "S", "M", "L", "XL"], "colors": ["Black", "Gray"]}'),
 (3, 'Dress', '{"sizes": ["S", "M", "L"], "colors": ["Pink", "Purple", "Black"]}'),
 (4, 'Jeans', '{"sizes": ["28", "30", "32", "34"], "colors": ["Blue", "Black"]}'),
 (5, 'Jacket', '{"sizes": ["S", "M", "L", "XL"], "colors": ["Black", "Brown", "Navy"]}');
| id |  name   |                                details                                 |                             
|----|---------|------------------------------------------------------------------------|
| 1  | T-Shirt | {"sizes": ["S", "M", "L", "XL"], "colors": ["Red", "Blue", "Green"]}   | 
| 2  | Hoodie  | {"sizes": ["XS", "S", "M", "L", "XL"], "colors": ["Black", "Gray"]}    |
| 3  | Dress   | {"sizes": ["S", "M", "L"], "colors": ["Pink", "Purple", "Black"]}      |
| 4  | Jeans   | {"sizes": ["28", "30", "32", "34"], "colors": ["Blue", "Black"]}       |
| 5  | Jacket  | {"sizes": ["S", "M", "L", "XL"], "colors": ["Black", "Brown", "Navy"]} |

The json_array_elements function can be used to get all the combinations of size and color for a specific product. For example:

SELECT
 id,
 name,
 size,
 color
FROM products AS p,
 json_array_elements(p.details -> 'sizes') AS size,
 json_array_elements(p.details -> 'colors') AS color
WHERE name = 'T-Shirt';

This query returns the following values:

| id |  name   | size | color  |
|----|---------|------|--------|
| 1  | T-Shirt | "S"  | "Red"  |
| 1  | T-Shirt | "S"  | "Blue" |
| 1  | T-Shirt | "S"  | "Green"|
| 1  | T-Shirt | "M"  | "Red"  |
| 1  | T-Shirt | "M"  | "Blue" |
| 1  | T-Shirt | "M"  | "Green"|
| 1  | T-Shirt | "L"  | "Red"  |
| 1  | T-Shirt | "L"  | "Blue" |
| 1  | T-Shirt | "L"  | "Green"|
| 1  | T-Shirt | "XL" | "Red"  |
| 1  | T-Shirt | "XL" | "Blue" |
| 1  | T-Shirt | "XL" | "Green"|

Filtering json_array_elements

You can use the json_array_elements function to extract the sizes from the JSON data and then filter the products based on a specific color (or size), as in this example:

SELECT *
FROM products
WHERE 'Blue' IN (
    SELECT json_array_elements_text(details->'colors')
);

This query returns the following values:

| id |   name   |                               details                                |
|----|----------|----------------------------------------------------------------------|
|  1 | T-Shirt  | {"sizes": ["S", "M", "L", "XL"], "colors": ["Red", "Blue", "Green"]} |
|  4 | Jeans    | {"sizes": ["28", "30", "32", "34"], "colors": ["Blue", "Black"]}     |

Handling NULL in json_array_elements

This example updates the table to insert another product (Socks) with one of the values in the sizes as null:

products

INSERT INTO products (id, name, details) VALUES (6, 'Socks', '{"sizes": ["S", null, "L", "XL"], "colors": ["White", "Black", "Gray"]}');
| id |  name   |                                 details                                 |
|----|---------|-------------------------------------------------------------------------|
|  6 | Socks   | {"sizes": ["S", null, "L", "XL"], "colors": ["White", "Black", "Gray"]} |

Querying for Socks shows how null values in an array are handled:

SELECT
 id,
 name,
 size
FROM products AS p,
 json_array_elements(p.details -> 'sizes') AS size
WHERE name = 'Socks';

This query returns the following values:

| id | name  | size |
|----|-------|------|
|  6 | Socks | "S"  |
|  6 | Socks | null |
|  6 | Socks | "L"  |
|  6 | Socks | "XL" |

Nested arrays in json_array_elements

You can also handle nested arrays with json_array_elements.

Consider a scenario where each product has multiple variants, and each variant has an array of sizes and an array of colors. This example uses an elecronics_products table, shown below.

electronics_products

CREATE TABLE electronics_products (
 id INTEGER PRIMARY KEY,
 name TEXT,
 details JSON
);

INSERT INTO electronics_products (id, name, details) VALUES
 (1, 'Laptop', '{"variants": [{"model": "A", "sizes": ["13 inch", "15 inch"], "colors": ["Silver", "Black"]}, {"model": "B", "sizes": ["15 inch", "17 inch"], "colors": ["Gray", "White"]}]}'),
 (2, 'Smartphone', '{"variants": [{"model": "X", "sizes": ["5.5 inch", "6 inch"], "colors": ["Black", "Gold"]}, {"model": "Y", "sizes": ["6.2 inch", "6.7 inch"], "colors": ["Blue", "Red"]}]}');
| id |    name    |                                                                                   details                                                                                    |
|----|------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|  1 | Laptop     | {"variants": [{"model": "A", "sizes": ["13 inch", "15 inch"], "colors": ["Silver", "Black"]}, {"model": "B", "sizes": ["15 inch", "17 inch"], "colors": ["Gray", "White"]}]} |
|  2 | Smartphone | {"variants": [{"model": "X", "sizes": ["5.5 inch", "6 inch"], "colors": ["Black", "Gold"]}, {"model": "Y", "sizes": ["6.2 inch", "6.7 inch"], "colors": ["Blue", "Red"]}]}   |

To handle the nested arrays and extract information about each variant, you can use the json_array_elements function like this:

SELECT
 id,
 name,
 variant->>'model' AS model,
 size,
 color
FROM
 electronics_products,
  json_array_elements(details->'variants') AS variant,
  json_array_elements_text(variant->'sizes') AS t1(size),
  json_array_elements_text(variant->'colors') AS t2(color);

This query returns the following values:

| id |    name    | model |   size   | color  |
|----|------------|-------|----------|--------|
|  1 | Laptop     | A     | 13 inch  | Silver |
|  1 | Laptop     | A     | 13 inch  | Black  |
|  1 | Laptop     | A     | 15 inch  | Silver |
|  1 | Laptop     | A     | 15 inch  | Black  |
|  1 | Laptop     | B     | 15 inch  | Gray   |
|  1 | Laptop     | B     | 15 inch  | White  |
|  1 | Laptop     | B     | 17 inch  | Gray   |
|  1 | Laptop     | B     | 17 inch  | White  |
|  2 | Smartphone | X     | 5.5 inch | Black  |
|  2 | Smartphone | X     | 5.5 inch | Gold   |
|  2 | Smartphone | X     | 6 inch   | Black  |
|  2 | Smartphone | X     | 6 inch   | Gold   |
|  2 | Smartphone | Y     | 6.2 inch | Blue   |
|  2 | Smartphone | Y     | 6.2 inch | Red    |
|  2 | Smartphone | Y     | 6.7 inch | Blue   |
|  2 | Smartphone | Y     | 6.7 inch | Red    |

Additional considerations

This section outlines additional considerations including alternative functions and JSON array order.

Alternates to json_array_elements

  • jsonb_array_elements - Consider this variant for performance benefits with jsonb data. jsonb_array_elements only accepts jsonb data, while json_array_elements works with both json and jsonb. It is typically faster, especially for larger arrays, due to its optimization for the binary jsonb format.
  • json_array_elements_text - While json_array_elements returns each extracted element as a JSON value, json_array_elements_text returns each extracted element as a plain text string.

Ordering json_array_elements output using WITH ORDINALITY

If the order of the elements is important, consider using the WITH ORDINALITY option:

SELECT
   id,
   name,
   skill,
   ordinality
FROM
   developers,
   json_array_elements(skills) WITH ORDINALITY AS t(skill, ordinality);

This query returns the following values:

| id |  name   |    skill     | ordinality |
|----|---------|--------------|------------|
|  1 | Alice   | "Java"       |          1 |
|  1 | Alice   | "Python"     |          2 |
|  1 | Alice   | "SQL"        |          3 |
|  2 | Bob     | "C++"        |          1 |
|  2 | Bob     | "JavaScript" |          2 |
|  3 | Charlie | "HTML"       |          1 |
|  3 | Charlie | "CSS"        |          2 |
|  3 | Charlie | "React"      |          3 |

The WITH ORDINALITY option in the query adds an ordinality column representing the original order of the skills in the array.

Resources

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