Postgres JSON_TABLE() function

The JSON_TABLE function transforms JSON data into relational views, allowing you to query JSON data using standard SQL operations. Added in PostgreSQL 17, this feature helps you work with complex JSON data by presenting it as a virtual table which you can access with regular SQL queries.

Use JSON_TABLE when you need to:

Extract specific fields from complex JSON structures
Convert JSON arrays into rows
Join JSON data with regular tables
Apply SQL operations like filtering and aggregation to JSON data

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Function signature

JSON_TABLE uses the following syntax:

JSON_TABLE(
    json_doc,           -- JSON/JSONB input
    path_expression     -- SQL/JSON path expression
    COLUMNS (
        column_definition [, ...]
    )
) AS alias

Parameters:

json_doc: JSON or JSONB data to process
path_expression: SQL/JSON path expression that identifies rows to generate
COLUMNS: Defines the schema of the virtual table
column_definition: Specifies how to extract values for each column
alias: Name for the resulting virtual table

Example usage

Let's explore JSON_TABLE using a library management system example. We'll store book information including reviews, borrowing history, and metadata in JSON format.

Create a test database

-- Test database table for a library management system
CREATE TABLE library_books (
    book_id SERIAL PRIMARY KEY,
    title VARCHAR(255) NOT NULL,
    data JSONB NOT NULL
);

-- Insert sample data
INSERT INTO library_books (title, data) VALUES
(
    'The Art of Programming',
    '{
        "isbn": "978-0123456789",
        "author": {
            "name": "Jane Smith",
            "email": "jane.smith@example.com"
        },
        "publication": {
            "year": 2023,
            "publisher": "Tech Books Inc"
        },
        "metadata": {
            "genres": ["Programming", "Computer Science"],
            "tags": ["algorithms", "python", "best practices"],
            "edition": "2nd"
        },
        "reviews": [
            {
                "user": "john_doe",
                "rating": 5,
                "comment": "Excellent book for beginners!",
                "date": "2024-01-15"
            },
            {
                "user": "mary_jane",
                "rating": 4,
                "comment": "Good examples, could use more exercises",
                "date": "2024-02-20"
            }
        ],
        "borrowing_history": [
            {
                "user_id": "U123",
                "checkout_date": "2024-01-01",
                "return_date": "2024-01-15",
                "condition": "good"
            },
            {
                "user_id": "U456",
                "checkout_date": "2024-02-01",
                "return_date": "2024-02-15",
                "condition": "fair"
            }
        ]
    }'::jsonb
),
(
    'Database Design Fundamentals',
    '{
        "isbn": "978-0987654321",
        "author": {
            "name": "Robert Johnson",
            "email": "robert.j@example.com"
        },
        "publication": {
            "year": 2024,
            "publisher": "Database Press"
        },
        "metadata": {
            "genres": ["Database", "Computer Science"],
            "tags": ["SQL", "design patterns", "normalization"],
            "edition": "1st"
        },
        "reviews": [
            {
                "user": "alice_wonder",
                "rating": 5,
                "comment": "Comprehensive coverage of database concepts",
                "date": "2024-03-01"
            }
        ],
        "borrowing_history": [
            {
                "user_id": "U789",
                "checkout_date": "2024-03-01",
                "return_date": null,
                "condition": "excellent"
            }
        ]
    }'::jsonb
);

Query examples

Extract basic book information

This query extracts core book details from the JSON structure into a relational format.

SELECT b.book_id, b.title, jt.*
FROM library_books b,
JSON_TABLE(
    data,
    '$'
    COLUMNS (
        isbn text PATH '$.isbn',
        author_name text PATH '$.author.name',
        publisher text PATH '$.publication.publisher',
        pub_year int PATH '$.publication.year'
    )
) AS jt;

Result:

book_id	title	isbn	author_name	publisher	pub_year
1	The Art of Programming	978-0123456789	Jane Smith	Tech Books Inc	2023
2	Database Design Fundamentals	978-0987654321	Robert Johnson	Database Press	2024

Analyze book reviews

This query flattens the reviews array into rows, making it easy to analyze reader feedback.

SELECT
    b.title,
    jt.*
FROM library_books b,
JSON_TABLE(
    data,
    '$.reviews[*]'
    COLUMNS (
        reviewer text PATH '$.user',
        rating int PATH '$.rating',
        review_date date PATH '$.date',
        comment text PATH '$.comment'
    )
) AS jt
ORDER BY review_date DESC;

Result:

title	reviewer	rating	review_date	comment
Database Design Fundamentals	alice_wonder	5	2024-03-01	Comprehensive coverage of database concepts
The Art of Programming	mary_jane	4	2024-02-20	Good examples, could use more exercises
The Art of Programming	john_doe	5	2024-01-15	Excellent book for beginners!

Track borrowing history

This query helps track book loans and current borrowing status.

WITH book_loans AS (
    SELECT
        b.title,
        jt.*
    FROM library_books b,
    JSON_TABLE(
        data,
        '$.borrowing_history[*]'
        COLUMNS (
            user_id text PATH '$.user_id',
            checkout_date date PATH '$.checkout_date',
            return_date date PATH '$.return_date',
            condition text PATH '$.condition'
        )
    ) AS jt
)
SELECT
    title,
    user_id,
    checkout_date,
    COALESCE(return_date::text, 'Still borrowed') as return_status,
    condition
FROM book_loans
ORDER BY checkout_date DESC;

Result:

title	user_id	checkout_date	return_status	condition
Database Design Fundamentals	U789	2024-03-01	Still borrowed	excellent
The Art of Programming	U456	2024-02-01	2024-02-15	fair
The Art of Programming	U123	2024-01-01	2024-01-15	good

Advanced usage

Aggregate review data

Use this query to calculate review statistics for each book.

WITH book_ratings AS (
    SELECT
        b.title,
        jt.rating
    FROM library_books b,
    JSON_TABLE(
        data,
        '$.reviews[*]'
        COLUMNS (
            rating int PATH '$.rating'
        )
    ) AS jt
)
SELECT
    title,
    COUNT(*) as num_reviews,
    ROUND(AVG(rating), 2) as avg_rating,
    MIN(rating) as min_rating,
    MAX(rating) as max_rating
FROM book_ratings
GROUP BY title;

Result

title	num_reviews	avg_rating	min_rating	max_rating
Database Design Fundamentals	1	5.00	5	5
The Art of Programming	2	4.50	4	5

Process arrays and metadata

This query extracts array fields and metadata into queryable columns.

SELECT
    b.title,
    jt.*
FROM library_books b,
JSON_TABLE(
    data,
    '$'
    COLUMNS (
        genres json FORMAT JSON PATH '$.metadata.genres',
        tags json FORMAT JSON PATH '$.metadata.tags',
        edition text PATH '$.metadata.edition'
    )
) AS jt;

Result:

title	genres	tags	edition
The Art of Programming	["Programming", "Computer Science"]	["algorithms", "python", "best practices"]	2nd
Database Design Fundamentals	["Database", "Computer Science"]	["SQL", "design patterns", "normalization"]	1st

Error handling

JSON_TABLE returns NULL for missing values by default. You can modify this behavior with error handling clauses:

SELECT title, jt.*
FROM library_books,
JSON_TABLE(
    data,
    '$'
    COLUMNS (
        author_name text PATH '$.author.name',
        metadata TEXT PATH '$.metadata' DEFAULT '{}' ON ERROR,
        edition text PATH '$.metadata.edition' DEFAULT 'Unknown' ON EMPTY DEFAULT 'Unknown' ON ERROR
    )
) AS jt;

This example shows how to handle errors when extracting JSON data. There is an error here because the metadata field is not of type TEXT.

title	author_name	metadata	edition
The Art of Programming	Jane Smith	{}	2nd
Database Design Fundamentals	Robert Johnson	{}	1st

Performance tips

Create GIN indexes on JSONB columns:

CREATE INDEX idx_library_books_data ON library_books USING GIN (data);

Consider these optimizations:
- Place filters on regular columns before JSON operations
- Use JSON operators (->, ->>, @>) when possible
- Materialize frequently accessed JSON paths into regular columns
- Break large JSON documents into smaller pieces to manage memory usage

Postgres JSON_TABLE() function

Try it on Neon!

Function signature

Example usage

Create a test database

Query examples

Extract basic book information

Analyze book reviews

Track borrowing history

Advanced usage

Aggregate review data

Process arrays and metadata

Error handling

Performance tips

Learn more

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