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Attached is an update to contrib/tablefunc. It implements a new hashed
version of crosstab. This fixes a major deficiency in real-world use ofthe original version. Easiest to undestand with an illustration:Data:-------------------------------------------------------------------select * from cth; id | rowid | rowdt | attribute | val----+-------+---------------------+----------------+--------------- 1 | test1 | 2003-03-01 00:00:00 | temperature | 42 2 | test1 | 2003-03-01 00:00:00 | test_result | PASS 3 | test1 | 2003-03-01 00:00:00 | volts | 2.6987 4 | test2 | 2003-03-02 00:00:00 | temperature | 53 5 | test2 | 2003-03-02 00:00:00 | test_result | FAIL 6 | test2 | 2003-03-02 00:00:00 | test_startdate | 01 March 2003 7 | test2 | 2003-03-02 00:00:00 | volts | 3.1234(7 rows)Original crosstab:-------------------------------------------------------------------SELECT * FROM crosstab( 'SELECT rowid, attribute, val FROM cth ORDER BY 1,2',4)AS c(rowid text, temperature text, test_result text, test_startdatetext, volts text); rowid | temperature | test_result | test_startdate | volts-------+-------------+-------------+----------------+-------- test1 | 42 | PASS | 2.6987 | test2 | 53 | FAIL | 01 March 2003 | 3.1234(2 rows)Hashed crosstab:-------------------------------------------------------------------SELECT * FROM crosstab( 'SELECT rowid, attribute, val FROM cth ORDER BY 1', 'SELECT DISTINCT attribute FROM cth ORDER BY 1')AS c(rowid text, temperature int4, test_result text, test_startdatetimestamp, volts float8); rowid | temperature | test_result | test_startdate | volts-------+-------------+-------------+---------------------+-------- test1 | 42 | PASS | | 2.6987 test2 | 53 | FAIL | 2003-03-01 00:00:00 | 3.1234(2 rows)Notice that the original crosstab slides data over to the left in theresult tuple when it encounters missing data. In order to work aroundthis you have to be make your source sql do all sorts of contortions(cartesian join of distinct rowid with distinct attribute; left jointhat back to the real source data). The new version avoids this bybuilding a hash table using a second distinct attribute query.The new version also allows for "extra" columns (see the README) andallows the result columns to be coerced into differing datatypes if theyare suitable (as shown above).In testing a "real-world" data set (69 distinct rowid's, 27 distinctcategories/attributes, multiple missing data points) I saw about a5-fold improvement in execution time (from about 2200 ms old, to 440 msnew).I left the original version intact because: 1) BC, 2) it is probablyslightly faster if you know that you have no missing attributes.README and regression test adjustments included. If there are noobjections, please apply.Joe Conway1 parentadd932e commit64d0b8b
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