Example code using JOINs and temporary tables (for arrays):

Here are some resources to explore further:

Important notes:

• These methods might not be as straightforward as a dedicated function.
• Consider the complexity of your JSON data (objects vs arrays) when choosing an approach.

Example code using JOINs and temporary tables (for arrays):

-- Sample data
CREATE TABLE mytable (
  id INT PRIMARY KEY,
  data1 JSON,
  data2 JSON
);

INSERT INTO mytable (id, data1, data2)
VALUES (1, '["a", "b", "c"]', '["b", "c", "d"]');

-- Find intersection
SELECT DISTINCT t1.value
FROM (
  SELECT JSON_TABLE(data1, '$[*]' COLUMNS (value INT PATH '$')) AS t1
) AS t1
INNER JOIN (
  SELECT JSON_TABLE(data2, '$[*]' COLUMNS (value INT PATH '$')) AS t2
) AS t2 ON t1.value = t2.value;

Explanation:

1. We define a table mytable with sample JSON data in data1 and data2 columns.
2. The inner query uses JSON_TABLE to convert each JSON array into a temporary table with a single column named value containing each element.
3. We then join these temporary tables on the value column, effectively finding the values present in both arrays.
4. SELECT DISTINCT t1.value ensures we only get unique values in the final result.

1. Using JSON_CONTAINS (limited):

This method has limitations but can be useful for specific scenarios. JSON_CONTAINS checks if a JSON object contains another object. You can potentially use it to check if one array is completely contained within another. However, it won't work for finding common elements between two separate arrays.

1. Regular Expressions (complex):

For simple cases, you might explore using regular expressions to extract and compare values within JSON strings. This approach can be complex and error-prone, especially for nested structures.

1. External tools (pre-processing):

If performance is crucial, consider pre-processing your data before storing it in JSON format. You could write scripts in Python or other languages to parse the JSON, find intersections, and then store the results in a separate table.

1. Consider alternative storage:

If finding intersections is a frequent operation, storing your data in a format more suitable for such queries might be beneficial. Depending on your use case, relational tables with normalized data structures could be a better fit.

Remember:

• The choice of method depends on the complexity of your JSON data and the frequency of intersection operations.
• JOINs and temporary tables offer a good balance for simple to moderately complex data.
• UDFs provide more flexibility but require programming expertise.
• External tools and alternative storage might be suitable for specific scenarios with high performance demands.