Unpivoting data in PostgreSQL   

Installing Hstore

If you don't have hstore installed and are running PostgreSQL 9.1+, you can use the handy

CREATE EXTENSION hstore;

For lower versions, look for the hstore.sql file in share/contrib and run in your database.

Load Our data

For this exercise, I'm going to grab data the Gazetteer zipcode tabulation areas http://www.census.gov/geo/maps-data/data/gazetteer2010.html

First create table to hold the data:

CREATE TABLE zcta5 (zip char(5) primary key, pop10 integer, hu10 integer
 , aland numeric(16,2), awater numeric(16,2)
 , aland_sqmi numeric(16,2), awater_sqmi numeric(16,2)
 , intptlong numeric(8,5), intptlat numeric(8,5));
 

Load the data with psql:

 --in psql
 \cd /Gaz_zcta_national
 \copy zcta5 FROM Gaz_zcta_national.txt DELIMITER E'\t' CSV HEADER

The Hstore unpivot view

We create a view that renders our table unpivoted:

CREATE VIEW vw_zcta_unpivot_hs
AS 
SELECT zip, (h).key, (h).value As val
 FROM (SELECT zip, each(hstore(foo) - 'zip'::text) As h
 FROM zcta5 as foo  ) As unpiv ;

Take it for a test drive by outputting just one zip

 --10 ms uses index too
-- returns 8 rows

SELECT * FROM vw_zcta_unpivot_hs WHERE zip = '02109';

  zip  |     key     |    val
-------+-------------+-----------
 02109 | hu10        | 2462
 02109 | aland       | 449654.00
 02109 | pop10       | 3771
 02109 | awater      | 292691.00
 02109 | intptlat    | -71.05063
 02109 | intptlong   | 42.36722
 02109 | aland_sqmi  | 0.17
 02109 | awater_sqmi | 0.11

 --output the whole thing  3,360ms (264960 records) --
SELECT * from vw_zcta_unpivot_hs;

 -- no network effects-- 
-- 290ms
SELECT count(*) from vw_zcta_unpivot_hs;

The thing that is kind of cool about this approach is that if I add another column to my table, I don't need to redo the view for it to output that new column as a row. To demonstrate

ALTER TABLE zcta5 ADD COLUMN  hu_income10 numeric(12,2);
SELECT * FROM vw_zcta_unpivot_hs WHERE zip = '02109';
  zip  |     key     |    val
-------+-------------+-----------
 02109 | hu10        | 2462
 02109 | aland       | 449654.00
 02109 | pop10       | 3771
 02109 | awater      | 292691.00
 02109 | intptlat    | -71.05063
 02109 | intptlong   | 42.36722
 02109 | aland_sqmi  | 0.17
 02109 | awater_sqmi | 0.11
 02109 | hu_income10 |

The array unpivot view

The second approach is to use array and unnest. As mentioned, the thing I didn't like about this approach is that it's not dynamic - you have to itemize the columns. It also on the downside requires you to cast all the column values before hand if they are not all the same type. It does have a couple of benefits:

• It uses built-in types so no need to install an extension though it requires PostgreSQL 8.4+ because of unnest
• You can grab just a subset very efficiently and if you only care about numeric columns would probably be best.
• It's generally faster.

To test performance I ran this:

Create the view

 CREATE OR REPLACE VIEW vw_zcta_unpivot_ary
 AS 
SELECT zip
 , unnest(
    array['hu10', 'aland', 'pop10'
     , 'awater', 'intptlat', 'intptlong', 'aland_sqmi', 'awater_sqmi'] 
    ) AS key
 , unnest(
    array[hu10::text, aland::text, pop10::text
      , awater::text, intptlat::text, intptlong::text
      , aland_sqmi::text, awater_sqmi::text] 
    ) AS val
 FROM zcta5;

Test some queries

The single record one takes same time and uses index but shorter plan:

--where filter  10 ms --
SELECT * 
FROM vw_zcta_unpivot_ary
WHERE zip = '02109';

Output the whole view

 -- network effects --
-- 3120 ms  (264960 records)
SELECT * from vw_zcta_unpivot_ary;

 -- no network effects --
-- 200 ms 
SELECT count(*) FROM vw_zcta_unpivot_ary;

So there you have it, two very different approaches for accomplishing the same task.