How to Unpivot Data Using Dynamic SQL in PostgreSQL for Top 3 Values per Game.

Top 3 Values in the Same Row: A Deep Dive into Unpivoting and Dynamic SQL

Introduction

Unpivoting data is a common task in data analysis and reporting. It involves transforming columnar data into row-based data, making it easier to perform aggregation operations or analyze individual rows. In this article, we’ll explore how to unpivot data using dynamic SQL in PostgreSQL, a popular relational database management system.

Problem Statement

The problem at hand is finding the top 3 values for each game in Steam data, where all tag values are in the same row. The data is stored in a table with 36,000 games and 370 columns (one for each tag), with unique appid values for each game.

Here’s an example of what the table might look like:

+---------+--------+--------+-------+
| appid   | column1| column2| ...    |
+---------+--------+--------+-------+
| 1       | value1 | value2 | ...    |
+---------+--------+--------+-------+
| 2       | value3 | value4 | ...    |
+---------+--------+--------+-------+
...

The goal is to find the top 3 values for each game, regardless of the column name.

Solution Overview

To solve this problem, we’ll use dynamic SQL to unpivot the data. The basic idea is to retrieve the table metadata and assemble a big SQL query that can handle all possible combinations of columns. We’ll use PostgreSQL’s information_schema.columns system view to get the list of available columns for each game.

Step 1: Retrieving Table Metadata

First, we need to retrieve the list of available columns for each game using PostgreSQL’s information_schema.columns system view.

SELECT *
FROM information_schema.columns
WHERE table_name = 'steam_data'
ORDER BY ordinal_position;

This query will return a list of columns with their corresponding data types and default values.

Step 2: Assembling the Dynamic SQL Query

Next, we’ll assemble the dynamic SQL query using PostgreSQL’s string_agg function.

SELECT string_agg(
  'select appid, ' || column_name || ', [' || value || '] from steam_data',
  ' union all '
)
FROM information_schema.columns
WHERE table_name = 'steam_data'
ORDER BY ordinal_position;

This query will generate a dynamic SQL query that includes all possible columns for each game. The string_agg function concatenates the column names and values into a single string, separated by commas.

Step 3: Unpivoting the Data

Now we can unpivot the data using the dynamic SQL query.

WITH unpivoted_data AS (
  SELECT *
  FROM (
    -- execute the dynamic SQL query
    EXECUTE (
      'select * from (' ||
        string_agg(
          'select appid, "'' || column_name || '''", [' || value || '] from steam_data',
          ' union all '
        ) :: text,
        '
      )'
    )
  )
)
SELECT *
FROM unpivoted_data;

This query uses a common table expression (CTE) to execute the dynamic SQL query. The result is a new table with all possible columns for each game.

Step 4: Finding Top 3 Values

Finally, we can find the top 3 values for each game using PostgreSQL’s row_number function.

WITH ranked_data AS (
  SELECT appid,
         column_name,
         value,
         row_number() OVER (PARTITION BY appid ORDER BY value DESC) as rn
  FROM unpivoted_data
)
SELECT *
FROM ranked_data
WHERE rn <= 3;

This query assigns a ranking to each value for each game using the row_number function. The final result is a table with only the top 3 values for each game.

Conclusion

In this article, we’ve explored how to unpivot data using dynamic SQL in PostgreSQL. We’ve used the information_schema.columns system view to retrieve the list of available columns for each game and assembled a dynamic SQL query to handle all possible combinations. Finally, we’ve used the row_number function to find the top 3 values for each game.

Additional Resources

Last modified on 2024-08-30