Unpivoting a Row with Multiple Status Change Date Columns in SQL

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In this article, we will explore how to unpivot a row with multiple status change date columns into multiple rows. This process is also known as “denormalization” or “unpivoting” the data. We’ll dive deep into the SQL query that achieves this and provide explanations for each step.

Background

The given problem involves an input table with two rows, where each row has multiple columns representing different statuses (Groomed, Defined, In Progress, and Completed) along with their corresponding timestamps. The goal is to transform these rows into multiple rows, each with a single status column and a timestamp column.

SQL Query

The provided solution uses a combination of the UNION ALL operator and the CASE statement to achieve this. Here’s the detailed explanation:

select story_id, sched_state, last_up_ts
from (
    select t.story_id,
      c.sched_state,
      case c.sched_state
        when 'Groomed' then story_being_groomed_ts
        when 'defined' then story_def_ts
        when 'inprogress' then story_in_prgrs_ts
        when 'completed' then story_cmpl_ts
      end as last_up_ts
    from mytable t
    cross join
    (
      select 'Groomed' as sched_state
      union all select 'defined'
      union all select 'inprogress'
      union all select 'completed'
    ) c
) sub
where last_up_ts is not null 
ORDER BY story_id, sched_state, last_up_ts

This query works as follows:

1. The subquery uses a CROSS JOIN to combine the input table (mytable) with a derived table containing all possible status values.
2. The CASE statement is used to determine which timestamp column to select based on the current status value.
3. The UNION ALL operator is used to combine the results of this process for each row in the input table, effectively unpivoting the data.

How it Works

To understand how this query works, let’s take a closer look at an example:

Suppose we have the following data in our input table:

The UNION ALL operator will create a derived table with the following rows:

Now, the CASE statement is used to determine which timestamp column to select based on the current status value. For example, for the row with sched_state = 'Groomed', the query will select story_being_groomed_ts.

Benefits and Limitations

The benefits of unpivoting a row with multiple status change date columns include:

• Simplified data analysis: By converting rows into separate rows, you can easily analyze individual dates and statuses.
• Improved performance: Unpivoted tables are often faster to query than their original row-based forms.

However, there are also some limitations to consider:

• Data duplication: The process of unpivoting creates duplicate data, which may lead to data inconsistencies or redundancy.
• Loss of aggregated data: By splitting a single row into multiple rows, you lose the ability to calculate aggregates (e.g., averages) across those rows.

Conclusion

Unpivoting a row with multiple status change date columns is a common requirement in various data analysis and reporting scenarios. The provided SQL query demonstrates an effective approach using UNION ALL and CASE statements. By understanding how this process works, you can make informed decisions about when to unpivot your data and how to implement it efficiently.

Additional Considerations

In some cases, you may need to consider additional factors such as:

• Handling missing or null values.
• Maintaining data integrity by avoiding duplicate rows or inconsistent data.
• Optimizing query performance for large datasets.

Last modified on 2023-12-03