Extracting Value from JSON Fields and Splitting into Rows in MariaDB

Here's a basic example:

SELECT *
FROM your_table,
JSON_TABLE(your_json_field, '$'  -- Path to all elements
             COLUMNS (key VARCHAR(255) PATH  '$.key',  -- Extract key as a column
                      value VARCHAR(255) PATH '$.value')  -- Extract value as a column
             ) AS extracted_data;

This query assumes your table has a column named your_json_field containing JSON data, and you want to extract both the key and value as separate columns named key and value.

Important points:

• MariaDB also supports wildcards (*) in paths for more flexibility.
• You can adjust the path to target specific elements within the JSON structure based on your data format.
• The $ symbol in the path refers to the root of the JSON document.

For more complex scenarios, you might need to combine JSON_TABLE with other functions like JSON_EXTRACT or conditional logic to manipulate the extracted data further.

This example assumes you have a table named products with a column named data containing a JSON array of products. Each product has a name and price.

SELECT p.id, extracted_data.name, extracted_data.price
FROM products p,
JSON_TABLE(p.data, '$[*]'  -- Path to each element in the array
             COLUMNS (name VARCHAR(255) PATH '$.name',
                      price DECIMAL(5,2) PATH '$.price')  -- Extract name and price
             ) AS extracted_data;

This query uses $[*] in the path to target each element within the JSON array. It then extracts the name and price properties for each product and creates separate rows.

Example 2: Splitting a JSON Object with Arrays

This example assumes you have a table named orders with a column named order_details containing a JSON object with customer information and an array of ordered items. Each item has a product_id and quantity.

SELECT o.id, 
       extracted_data.customer_name, 
       oi.product_id, 
       oi.quantity
FROM orders o,
JSON_TABLE(o.order_details, '$.customer_name'  -- Path to customer name
             COLUMNS (customer_name VARCHAR(255) PATH '$')) AS customer_data,
JSON_TABLE(o.order_details.items, '$[*]'  -- Path to each item in the array
             COLUMNS (product_id INT PATH '$.product_id',
                      quantity INT PATH '$.quantity')) AS order_items;

This query uses two JSON_TABLE functions. The first one extracts the customer_name from the root of the JSON object. The second one targets the items array and extracts product_id and quantity for each item.

1. Conditional Logic with JSON_EXTRACT:

This approach involves using the JSON_EXTRACT function to extract specific values from the JSON field and then using conditional logic (e.g., IF, CASE) to create separate rows based on the extracted data.

SELECT your_table.id,
       (CASE WHEN JSON_EXTRACT(your_json_field, '$.key1') IS NOT NULL THEN JSON_EXTRACT(your_json_field, '$.key1') ELSE NULL END) AS key1_value,
       (CASE WHEN JSON_EXTRACT(your_json_field, '$.key2') IS NOT NULL THEN JSON_EXTRACT(your_json_field, '$.key2') ELSE NULL END) AS key2_value
FROM your_table;

This example uses JSON_EXTRACT to get values for specific keys (key1 and key2) and then uses a CASE statement to create separate columns with the extracted values or NULL if the key doesn't exist.

1. Stored Procedures:

For complex scenarios involving heavy manipulation of JSON data, you can create stored procedures. These procedures can handle the logic of parsing the JSON field, extracting data, and inserting it into separate rows.

1. Application-Level Processing:

If your workflow allows, you can consider processing the JSON data within your application before storing it in MariaDB. This might involve parsing the JSON and creating separate objects or data structures before inserting them into the database. This approach can offload some processing from the database server.

Choosing the Right Method:

The best method for you depends on:

• Developer familiarity: Choose the method your developers are most comfortable with in terms of implementation and maintenance.
• Performance needs: For simple extractions, JSON_TABLE can be performant. If you have complex logic or heavy data manipulation, stored procedures or application-level processing might be more efficient.
• Complexity of your JSON data: JSON_TABLE is efficient for structured JSON arrays. For more complex or dynamic structures, conditional logic or stored procedures might be more suitable.