Pivot on Multiple Columns using Tablefunc

Has anyone used tablefunc to pivot on multiple variables as opposed to only using row name?

I need to do this for billions of rows and I'm testing out storing this data in long and wide formats and seeing if I can use tablefunc to go from long to wide format more efficiently than with regular aggregate functions. I'll have about 100 measurements made every minute for around 300 entities. Often, we will need to compare the different measurements made for a given second for a given entity, so we will need to go to wide format very often. Also, the measurements made on a particular entity are highly variable.

As sample data, I've edited the data used in a response to this question:

  timeof   timestamp
 ,entity    character
 ,status    integer
 ,ct        integer);

  ('2012-01-01', 'a', 1, 1)
 ,('2012-01-01', 'a', 0, 2)
 ,('2012-01-02', 'b', 1, 3)
 ,('2012-01-02', 'c', 0, 4);

 SELECT * FROM crosstab(
     'SELECT timeof, entity, status, ct
      FROM   t4
      ORDER  BY 1,2,3'
     ,$$VALUES (1::text), (0::text)$$)
 AS ct ("Section" timestamp, "Attribute" character, "1" int, "0" int);


Section Attribute 1 0
2012-01-01 00:00:00 a 1 2
2012-01-02 00:00:00 b 3 4

So as the documentation states, the extra column aka 'Attribute' is assumed to be the same for each row name aka 'Section'. Thus, it reports b for the second row even though 'entity' also has a 'c' value for that 'timeof' value.

Desired Output:

Section Attribute 1 0
2012-01-01 00:00:00 a 1 2
2012-01-02 00:00:00 b 3
2012-01-02 00:00:00 c 4

Some resources that I used: 1, 2

How do I do it?


  • The problem with your query is that b and c share the same timestamp 2012-01-02 00:00:00, and you have the timestamp column timeof first in your query, so - even though you added bold emphasis - b and c are just extra columns that fall in the same group 2012-01-02 00:00:00. Only the first (b) is returned since (quoting the manual):

    The row_name column must be first. The category and value columns must be the last two columns, in that order. Any columns between row_name and category are treated as "extra". The "extra" columns are expected to be the same for all rows with the same row_name value.

    Bold emphasis mine.
    Just revert the order of the first two columns to make entity the row name and it works as desired:

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

    entity must be unique, of course.


    • row_name first
    • (optional) extra columns next
    • category (as defined by the second parameter) and value last.

    Extra columns are filled from the first row from each row_name partition. Values from other rows are ignored, there is only one column per row_name to fill. Typically those would be the same for every row of one row_name, but that's up to you.


    For the different setup in your answer:

    SELECT localt, entity
         , msrmnt01, msrmnt02, msrmnt03, msrmnt04, msrmnt05  -- , more?
    FROM   crosstab(
      'SELECT dense_rank() OVER (ORDER BY localt, entity)::int AS row_name
            , localt, entity -- additional columns
            , msrmnt, val
       FROM   test
    -- WHERE  ???   -- instead of LIMIT at the end
       ORDER  BY localt, entity, msrmnt
    -- LIMIT ???'   -- instead of LIMIT at the end
    , 'SELECT generate_series(1,5)'  -- more?
       ) AS ct (row_name int, localt timestamp, entity int
              , msrmnt01 float8, msrmnt02 float8, msrmnt03 float8, msrmnt04 float8, msrmnt05 float8 -- , more?
    LIMIT 1000  -- ?!

    No wonder the queries in your test perform terribly. Your test setup has 14M rows and you process all of them before throwing most away with LIMIT 1000. For a reduced result set add WHERE conditions or a LIMIT to the source query!

    Plus, the array you work with is needlessly expensive on top of it. I generated a surrogate row name with dense_rank() instead.

    db<>fiddle here - with simpler test setup and fewer rows.