How to Dynamically Pivot Object Keys in SQL?

In this article, we will explore how to dynamically transform a table in SQL where object keys become column names and their values populate corresponding columns. This scenario is common for database tasks involving JSON-like structures, particularly in systems like Snowflake. By utilizing advanced SQL techniques, you can simplify data manipulation and achieve a transformed view of your data without explicitly naming each key. Understanding the Problem The need to pivot object keys arises when you have a table with a column storing semi-structured data types like objects. In our case, we have a table objects_to_pivot where the attributes column contains various object keys that might not be consistent across rows. The challenge is to convert these dynamic object keys into fixed column names dynamically, allowing efficient data retrieval. Sample Data Structure First, let's look at our sample table structure: CREATE OR REPLACE TABLE objects_to_pivot ( row_id INT, attributes OBJECT ); -- Insert sample data INSERT INTO objects_to_pivot (row_id, attributes) SELECT 1, OBJECT_CONSTRUCT('color', 'red', 'size', 'L', 'material', 'cotton'); INSERT INTO objects_to_pivot (row_id, attributes) SELECT 2, OBJECT_CONSTRUCT('color', 'blue', 'material', 'nylon', 'price', 25.50); -- Different keys/types INSERT INTO objects_to_pivot (row_id, attributes) SELECT 3, OBJECT_CONSTRUCT('size', 'M', 'brand', 'XYZ'); INSERT INTO objects_to_pivot (row_id, attributes) SELECT 4, OBJECT_CONSTRUCT('color', 'green', 'size', 'L'); SELECT * FROM objects_to_pivot; As shown, the attributes column can contain different sets of keys for each row. To dynamically pivot these values into separate columns, we can use a combination of SQL functions, like FLATTEN, and an aggregation method. Dynamically Pivoting the Object Keys To achieve this without directly referencing each key, we can follow the steps outlined below: Step 1: Flatten the Object We can utilize the FLATTEN function to convert the object keys into rows. This way, we can reach each key-value pair and process them. SELECT row_id, key, value FROM objects_to_pivot, LATERAL FLATTEN(INPUT => attributes); This query will give you a result set where each row_id now will have individual rows for each key-value pair from the attributes object. The output would look something like this: | row_id | key | value | |--------|-----------|--------| | 1 | color | red | | 1 | size | L | | 1 | material | cotton | | 2 | color | blue | | 2 | material | nylon | | 2 | price | 25.50 | | 3 | size | M | | 3 | brand | XYZ | | 4 | color | green | | 4 | size | L | Step 2: Create Dynamic Columns Now that we have flattened the data, we can dynamically create SQL that constructs a pivot table. In databases like Snowflake, we can use the PIVOT function or a conditional aggregation approach. Assuming you extract distinct keys from your data, you would use them in a query like: SELECT row_id, MAX(CASE WHEN key = 'color' THEN value END) AS color, MAX(CASE WHEN key = 'size' THEN value END) AS size, MAX(CASE WHEN key = 'material' THEN value END) AS material, MAX(CASE WHEN key = 'price' THEN value END) AS price, MAX(CASE WHEN key = 'brand' THEN value END) AS brand FROM ( SELECT row_id, key, value FROM objects_to_pivot, LATERAL FLATTEN(INPUT => attributes) ) GROUP BY row_id; Final Output The final output would give you a structured table where object keys are converted into column names with their respective values: | row_id | color | size | material | price | brand | |--------|-------|------|----------|-------|-------| | 1 | red | L | cotton | NULL | NULL | | 2 | blue | NULL | nylon | 25.50 | NULL | | 3 | NULL | M | NULL | NULL | XYZ | | 4 | green | L | NULL | NULL | NULL | Frequently Asked Questions Can I do this with any SQL database? The method described is particularly effective in databases that support JSON-like structures, particularly Snowflake. Some variation may be necessary in other SQL databases to suit their syntax. What if the keys keep changing? The use of dynamic SQL might be necessary if the keys change frequently. You may need to write a script that generates your pivot SQL based on the unique keys present in the attributes column. In conclusion, transforming object keys to column names in SQL not only streamlines data retrieval but also makes data readability easier, reducing manual effort in data preparation. By leveraging SQL’s powerful capabilities, you can automate this process effectively and keep your reporting accurate and insightful.

May 6, 2025 - 19:27

How to Dynamically Pivot Object Keys in SQL?

In this article, we will explore how to dynamically transform a table in SQL where object keys become column names and their values populate corresponding columns. This scenario is common for database tasks involving JSON-like structures, particularly in systems like Snowflake. By utilizing advanced SQL techniques, you can simplify data manipulation and achieve a transformed view of your data without explicitly naming each key.

Understanding the Problem

The need to pivot object keys arises when you have a table with a column storing semi-structured data types like objects. In our case, we have a table objects_to_pivot where the attributes column contains various object keys that might not be consistent across rows. The challenge is to convert these dynamic object keys into fixed column names dynamically, allowing efficient data retrieval.

Sample Data Structure

First, let's look at our sample table structure:

CREATE OR REPLACE TABLE objects_to_pivot (
    row_id INT,
    attributes OBJECT
);

-- Insert sample data
INSERT INTO objects_to_pivot (row_id, attributes)
SELECT 1, OBJECT_CONSTRUCT('color', 'red', 'size', 'L', 'material', 'cotton');

INSERT INTO objects_to_pivot (row_id, attributes)
SELECT 2, OBJECT_CONSTRUCT('color', 'blue', 'material', 'nylon', 'price', 25.50); -- Different keys/types

INSERT INTO objects_to_pivot (row_id, attributes)
SELECT 3, OBJECT_CONSTRUCT('size', 'M', 'brand', 'XYZ');

INSERT INTO objects_to_pivot (row_id, attributes)
SELECT 4, OBJECT_CONSTRUCT('color', 'green', 'size', 'L');

SELECT * FROM objects_to_pivot;

As shown, the attributes column can contain different sets of keys for each row. To dynamically pivot these values into separate columns, we can use a combination of SQL functions, like FLATTEN, and an aggregation method.

Dynamically Pivoting the Object Keys

To achieve this without directly referencing each key, we can follow the steps outlined below:

Step 1: Flatten the Object

We can utilize the FLATTEN function to convert the object keys into rows. This way, we can reach each key-value pair and process them.

SELECT row_id, key, value
FROM objects_to_pivot,
LATERAL FLATTEN(INPUT => attributes);

This query will give you a result set where each row_id now will have individual rows for each key-value pair from the attributes object. The output would look something like this:

| row_id | key | value | |--------|-----------|--------| | 1 | color | red | | 1 | size | L | | 1 | material | cotton | | 2 | color | blue | | 2 | material | nylon | | 2 | price | 25.50 | | 3 | size | M | | 3 | brand | XYZ | | 4 | color | green | | 4 | size | L |

Step 2: Create Dynamic Columns

Now that we have flattened the data, we can dynamically create SQL that constructs a pivot table. In databases like Snowflake, we can use the PIVOT function or a conditional aggregation approach.

Assuming you extract distinct keys from your data, you would use them in a query like:

SELECT row_id,
       MAX(CASE WHEN key = 'color' THEN value END) AS color,
       MAX(CASE WHEN key = 'size' THEN value END) AS size,
       MAX(CASE WHEN key = 'material' THEN value END) AS material,
       MAX(CASE WHEN key = 'price' THEN value END) AS price,
       MAX(CASE WHEN key = 'brand' THEN value END) AS brand
FROM (
    SELECT row_id, key, value
    FROM objects_to_pivot,
    LATERAL FLATTEN(INPUT => attributes)
)
GROUP BY row_id;

Final Output

The final output would give you a structured table where object keys are converted into column names with their respective values:

| row_id | color | size | material | price | brand | |--------|-------|------|----------|-------|-------| | 1 | red | L | cotton | NULL | NULL | | 2 | blue | NULL | nylon | 25.50 | NULL | | 3 | NULL | M | NULL | NULL | XYZ | | 4 | green | L | NULL | NULL | NULL |

Frequently Asked Questions

Can I do this with any SQL database?

The method described is particularly effective in databases that support JSON-like structures, particularly Snowflake. Some variation may be necessary in other SQL databases to suit their syntax.

What if the keys keep changing?

The use of dynamic SQL might be necessary if the keys change frequently. You may need to write a script that generates your pivot SQL based on the unique keys present in the attributes column.

In conclusion, transforming object keys to column names in SQL not only streamlines data retrieval but also makes data readability easier, reducing manual effort in data preparation. By leveraging SQL’s powerful capabilities, you can automate this process effectively and keep your reporting accurate and insightful.