Data nesting is a way of organizing data in a hierarchical or nested structure, where one piece of data is related to another in a parent-child or one-to-many relationship. This approach can be particularly useful for storing complex data structures in databases and is commonly used in NoSQL databases such as MongoDB.
In PostgreSQL, nested tables refer to tables that are organized in a hierarchical or nested structure. This means that one table can have one or more sub-tables or child tables that are associated with it.
Nested tables are often used to represent complex data structures such as arrays, JSON, and XML data. For example, you could have a table that stores information about customers and a child table that stores information about their orders. This allows you to retrieve and manage related data in a more organized and efficient way.
Overall, nested tables in PostgreSQL provide a flexible and powerful way to store and manage complex data structures. Composite data types can be a useful approach when designing a data model in PostgreSQL. Here are factors to consider before implementing them:
- Data complexity: If the data is relatively simple, there may be no need to use composite data types. However, if the data contains multiple related attributes or sub-objects, then composite data types can simplify data management.
- Data redundancy: If data needs to be repeated frequently in a table, composite data types can reduce redundancy and simplify data entry.
- Data normalization: If the data is normalized, composite data types can help maintain data integrity by grouping related data together.
- Data access patterns: Consider how the data will be accessed and queried. If a single attribute is commonly queried independently of the others, then it may be more efficient to store it separately rather than in a composite type.
- Database performance: Composite data types can impact database performance, particularly when they are used in large tables or frequently updated. Therefore, it’s important to test and analyze the performance impact of composite data types on the database.
By considering these factors, you can determine whether composite data types are appropriate for your data model and how to use them effectively.
Fast forward to the JSONs and XML Types
Nested Tables with JSON Data
Another common use case for nested tables in PostgreSQL is with JSON data. Here’s an example of how you can create a table with nested JSON data:
CREATE TABLE products (
id serial PRIMARY KEY,
name varchar(50),
details json
);
INSERT INTO products (name, details) VALUES (
'Product 1',
'{"price": 10.99, "colors": ["red", "blue", "green"]}'
);
In this example, the “details” column is a JSON data type that contains nested data. You can query the nested data using the JSON functions in PostgreSQL, like this:
SELECT name, details->'price' AS price, details->'colors' AS colors FROM products;
As you see, it returns the name of the product, the price, and the colors from the nested JSON data.
Nested Tables with XML Data
You can also use nested tables in PostgreSQL with XML data. Here’s an example of how you can create a table with nested XML data:
CREATE TABLE books (
id serial PRIMARY KEY,
title varchar(50),
content xml
);
INSERT INTO books (title, content) VALUES (
'Book 1',
'<book>
<chapter>
<title>Chapter 1</title>
<paragraph>Paragraph 1</paragraph>
<paragraph>Paragraph 2</paragraph>
</chapter>
<chapter>
<title>Chapter 2</title>
<paragraph>Paragraph 1</paragraph>
<paragraph>Paragraph 2</paragraph>
</chapter>
</book>'
);
In this example, the “content” column is an XML data type that contains nested data. You can query the nested data using the XML functions in PostgreSQL, like this:
SELECT title, (xpath('//chapter/title/text()', content))[1] AS chapter_title FROM books;
It returned the title of the book and the title of each chapter in the nested XML data.
DMLs with Nested tables having Composite Data types
Have you ever encountered the challenge of managing a table within a table that contains composite data types in Postgres? If so, you’re not alone. Fortunately, the following part of the post will guide you through the process of handling nested tables using composite types.
To illustrate, let’s consider the scenario of a school student and their report card. The goal is to create a table with one row for each student, where each row contains a nested table with two columns: the subject name and the corresponding grade earned by the student. Consider the case of a student and their academic performance record. Our objective is to establish a table that holds a distinct row for every student, and each of these rows comprises a nested table with two columns representing the subject name and the student’s corresponding grade.
CREATE TYPE subjects_enum AS enum ('ENGLISH', 'MATHS', 'SCIENCE', 'SOCIAL',
'MORALSCIENCE' );
CREATE TYPE grades_enum AS enum ('DISTINCTION', 'FIRST_DIVISION',
'SECOND_DIVISION', 'THIRD_DIVISION' );
CREATE TYPE subject_grades_tbl AS (subjects subjects_enum, grades grades_enum );
CREATE TABLE student_grades
(
id SERIAL NOT NULL PRIMARY KEY,
name TEXT NOT NULL,
subject_grades_t SUBJECT_GRADES_TBL[]
);
#Inserting sample data:
INSERT INTO student_grades
(NAME,
subject_grades_t)
VALUES ('Iron Man',
'{"(ENGLISH,FIRST_DIVISION)","(MATHS,FIRST_DIVISION)","(SCIENCE,SECOND_DIVISION)","(SOCIAL,THIRD_DIVISION)","(MORALSCIENCE,DISTINCTION)"}'
);
INSERT INTO student_grades
(NAME,
subject_grades_t)
VALUES ('Dr Strange',
'{"(ENGLISH,SECOND_DIVISION)","(MATHS,THIRD_DIVISION)","(SCIENCE,FIRST_DIVISION)","(SOCIAL,THIRD_DIVISION)","(MORALSCIENCE,DISTINCTION)"}'
);
# Updating Data in various ways
UPDATE student_grades
SET subject_grades_t = array[('ENGLISH','FIRST_DIVISION')::subject_grades_tbl]
WHERE id = 1;
UPDATE student_grades
SET subject_grades_t = array[('ENGLISH','FIRST_DIVISION')::subject_grades_tbl , ('MATHS','SECOND_DIVISION')::subject_grades_tbl]
WHERE id = 1;
UPDATE student_grades
SET subject_grades_t = '{"(ENGLISH,THIRD_DIVISION)","(MATHS,SECOND_DIVISION)","(SCIENCE,FIRST_DIVISION)","(SOCIAL,THIRD_DIVISION)","(MORALSCIENCE,DISTINCTION)"}'
WHERE id = 1;
# Updating Data programmatically using a simple piece of the code block
DO $$
<<first_block>>
DECLARE l_subject_grades_t TEXT := '{"(ENGLISH,THIRD_DIVISION)","(MATHS,SECOND_DIVISION)"}';BEGIN
UPDATE student_grades
SET subject_grades_t = l_subject_grades_t::subject_grades_tbl[]
WHERE id = 1;
END first_block $$;
Fetching & Filtering Data
SELECT * FROM (
SELECT id,
NAME ,
(Unnest(subject_grades_t)::subject_grades_tbl).subjects ,
(unnest(subject_grades_t)::subject_grades_tbl).grades
FROM student_grades) z
ORDER BY id, subjects;
SELECT * FROM (
SELECT id,
NAME ,
(Unnest(subject_grades_t)::subject_grades_tbl).subjects ,
(unnest(subject_grades_t)::subject_grades_tbl).grades
FROM student_grades) z
WHERE subjects = 'ENGLISH'
ORDER BY id, subjects;
Overall, data nesting in Postgres using the JSONB data type, XML, and Composite data types can be a powerful way of storing and querying complex data structures, and can be particularly useful in situations where data needs to be stored and queried efficiently, or where there are complex relationships between different pieces of data exist.
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