How to pivot or crosstab in postgresql without writing a function? - postgresql

Question

I have a dataset that looks something like this:
I'd like to aggregate all co values on one row, so the final result looks something like:
Seems pretty easy, right? Just write a query using crosstab, as suggested in this answer. Problem is that requires that I CREATE EXTENSION tablefunc; and I don't have write access to my DB.
Can anyone recommend an alternative?

Answer 1

Conditional aggregation:
SELECT co,
MIN(CASE WHEN ontology_type = 'industry' THEN tags END) AS industry,
MIN(CASE WHEN ontology_type = 'customer_type' THEN tags END) AS customer_type,
-- ...
FROM tab_name
GROUP BY co

Answer 2

You can use DO to generate and PREPARE your own SQL with crosstab columns, then EXECUTE it.
-- replace tab_name to yours table name
DO $$
DECLARE
_query text;
_name text;
BEGIN
_name := 'prepared_query';
_query := '
SELECT co
'||(SELECT ', '||string_agg(DISTINCT
' string_agg(DISTINCT
CASE ontology_type WHEN '||quote_literal(ontology_type)||' THEN tags
ELSE NULL
END, '',''
) AS '||quote_ident(ontology_type),',')
FROM tab_name)||'
FROM tab_name
GROUP BY co
';
BEGIN
EXECUTE 'DEALLOCATE '||_name;
EXCEPTION
WHEN invalid_sql_statement_name THEN
END;
EXECUTE 'PREPARE '||_name||' AS '||_query;
END
$$;
EXECUTE prepared_query;

Answer 3

Since Ver. 9.4 there's json_object_agg(), which lets us do part of the necessary magic dynamically.
However to be totally dynamic, a temp type (a temp table) has to be FIRST built by running a SQL-EXEC inside an anonymous procedure.
DB FIDDLE (UK):
https://dbfiddle.uk/Sn7iO4zL
DISCLAIMER: Typically the ability to create TEMP TABLES are granted to end-users, but YMMV. Another concern is whether anon. procedures can be exec'd as in-line code by regular users.
-- /**
-- begin test data
-- begin test data
-- begin test data
-- */
DROP TABLE IF EXISTS tmpSales ;
CREATE TEMP TABLE tmpSales AS
SELECT
sale_id
,TRUNC(RANDOM()*12)+1 AS book_id
,TRUNC(RANDOM()*100)+1 AS customer_id
,(date '2010-01-01' + random() * (timestamp '2016-12-31' - timestamp '2010-01-01')) AS sale_date
FROM generate_series(1,10000) AS sale_id;
DROP TABLE IF EXISTS tmp_month_total ;
CREATE TEMP TABLE tmp_month_total AS
SELECT
date_part( 'year' , sale_date ) AS year
,date_part( 'month', sale_date ) AS mn
,to_char(sale_date, 'mon') AS month
,COUNT(*) AS total
FROM tmpSales
GROUP BY date_part('year', sale_date), to_char(sale_date, 'mon') ,date_part( 'month', sale_date )
;
DATA:
+----+--+-----+-----+
|year|mn|month|total|
+----+--+-----+-----+
|2010|1 |jan |127 |
|2010|2 |feb |117 |
|2010|3 |mar |121 |
|2010|4 |apr |131 |
|2010|5 |may |106 |
|2010|6 |jun |121 |
|2010|7 |jul |129 |
|2010|8 |aug |114 |
|2010|9 |sep |115 |
|2010|10|oct |110 |
|2010|11|nov |133 |
|2010|12|dec |108 |
+----+--+-----+-----+
-- /**
-- END test data
-- END test data
-- END test data
-- */
-- /**
-- dyn. build a temporary row-type based on existing data, not hard-coded
-- dyn. build a temporary row-type based on existing data, not hard-coded
-- dyn. build a temporary row-type based on existing data, not hard-coded
-- **/
DROP TABLE IF EXISTS tmpTblTyp CASCADE ;
DO LANGUAGE plpgsql $$ DECLARE v_sqlstring VARCHAR = ''; BEGIN
v_sqlstring := CONCAT( 'CREATE TEMP TABLE tmpTblTyp AS SELECT '
,(SELECT STRING_AGG( CONCAT('NULL::int AS ' , month )::TEXT , ' ,'
ORDER BY mn
)::TEXT
FROM
(SELECT DISTINCT month, mn FROM tmp_month_total )a )
,' LIMIT 0 '
) ; -- RAISE NOTICE '%', v_sqlstring ;
EXECUTE( v_sqlstring ) ; END $$;
DROP TABLE IF EXISTS tmpMoToJson ;
CREATE TEMP TABLE tmpMoToJson AS
SELECT
year AS year
,(json_build_array( months )) AS js_months_arr
,json_populate_recordset ( NULL::tmpTblTyp /** use temp table as a record type!! **/
, json_build_array( months )
) jprs /** builds row-type column that can be expanded with (jprs).*
**/
FROM ( SELECT year
-- accum data into JSON array
,json_object_agg(month,total) AS months
FROM tmp_month_total
GROUP BY year
ORDER BY year
) a
;
SELECT
year
,(ROW((jprs).*)::tmpTblTyp).* -- explode the composite type row
FROM tmpMoToJson ;
+----+---+---+---+---+---+---+---+---+---+---+---+---+
|year|jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec|
+----+---+---+---+---+---+---+---+---+---+---+---+---+
|2010|127|117|121|131|106|121|129|114|115|110|133|108|
|2011|117|112|117|115|139|116|119|152|117|112|115|103|
|2012|129|111|98 |140|109|131|114|110|112|115|100|121|
|2013|128|112|141|127|141|102|113|109|111|110|123|116|
|2014|129|114|117|118|111|123|106|111|127|121|124|145|
|2015|118|113|131|122|120|121|140|114|118|108|114|131|
|2016|117|110|139|100|110|116|112|109|131|117|122|132|
+----+---+---+---+---+---+---+---+---+---+---+---+---+

Answer 4

By using pivot also we can achieve your required out put
SELECT co
,industry
,customer_type
,product_type
,sales_model
,stage
FROM dataSet
PIVOT(max(tags) FOR ontologyType IN (
industry
,customer_type
,product_type
,sales_model
,stage
)) AS PVT