How Can Tablefunc Handle Multiple-Variable Pivoting to Avoid Data Loss?

Use tablefunc for multi-column pivoting

Question:

How to use tablefunc to pivot on multiple variables instead of just row names?

Background:

Datasets containing billions of rows need to be pivoted into a wide format in order to efficiently compare multiple measurements taken on numerous entities. These measurements vary widely, requiring frequent pivoting of the data into a wide format.

Question:

The standard tablefunc approach assumes that attribute columns (aka "extra" columns) are consistent for every row name. If multiple values ​​exist for an attribute column within a row name, only the first value is reported, resulting in incomplete data in the pivot output.

Solution:

To overcome this limitation, you need to reorder the query columns and place the attribute column before the row name column. This ensures that the attribute values ​​are populated from the first row of each rowname partition, thus capturing all the different attribute values ​​for that rowname.

Code:

SELECT *
FROM crosstab(
   'SELECT entity, timeof, status, ct
    FROM   t4
    ORDER  BY entity'
 , 'VALUES (1), (0)'
   ) AS ct (
      "Attribute" character
    , "Section" timestamp
    , "status_1" int
    , "status_0" int
      );

Summary:

By reversing the order of the first two columns (attribute columns before row name columns), tablefunc can effectively pivot on multiple variables, providing a complete pivot output. This approach works well when the data set contains a different number of attribute values ​​per row name.

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