postgres/contrib/tablefunc/tablefunc.c

Ignoring revisions in .git-blame-ignore-revs. Click here to bypass and see the normal blame view.

1592 lines
44 KiB
C
Raw Normal View History

/*
2010-09-21 00:08:53 +04:00
* contrib/tablefunc/tablefunc.c
*
*
* tablefunc
*
* Sample to demonstrate C functions which return setof scalar
* and setof composite.
* Joe Conway <mail@joeconway.com>
* And contributors:
* Nabil Sayegh <postgresql@e-trolley.de>
*
* Copyright (c) 2002-2023, PostgreSQL Global Development Group
*
* Permission to use, copy, modify, and distribute this software and its
* documentation for any purpose, without fee, and without a written agreement
* is hereby granted, provided that the above copyright notice and this
* paragraph and the following two paragraphs appear in all copies.
*
* IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING
* LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS
* DOCUMENTATION, EVEN IF THE AUTHOR OR DISTRIBUTORS HAVE BEEN ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES,
* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
* AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE AUTHOR AND DISTRIBUTORS HAS NO OBLIGATIONS TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
*/
#include "postgres.h"
#include <math.h>
#include "access/htup_details.h"
#include "catalog/pg_type.h"
#include "common/pg_prng.h"
#include "executor/spi.h"
#include "funcapi.h"
#include "lib/stringinfo.h"
#include "miscadmin.h"
#include "tablefunc.h"
#include "utils/builtins.h"
PG_MODULE_MAGIC;
static HTAB *load_categories_hash(char *cats_sql, MemoryContext per_query_ctx);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
static Tuplestorestate *get_crosstab_tuplestore(char *sql,
HTAB *crosstab_hash,
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
TupleDesc tupdesc,
bool randomAccess);
static void validateConnectbyTupleDesc(TupleDesc td, bool show_branch, bool show_serial);
static bool compatCrosstabTupleDescs(TupleDesc ret_tupdesc, TupleDesc sql_tupdesc);
static void compatConnectbyTupleDescs(TupleDesc ret_tupdesc, TupleDesc sql_tupdesc);
static void get_normal_pair(float8 *x1, float8 *x2);
static Tuplestorestate *connectby(char *relname,
char *key_fld,
char *parent_key_fld,
char *orderby_fld,
char *branch_delim,
char *start_with,
int max_depth,
bool show_branch,
bool show_serial,
MemoryContext per_query_ctx,
bool randomAccess,
AttInMetadata *attinmeta);
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
static void build_tuplestore_recursively(char *key_fld,
char *parent_key_fld,
char *relname,
char *orderby_fld,
char *branch_delim,
char *start_with,
char *branch,
int level,
int *serial,
int max_depth,
bool show_branch,
bool show_serial,
MemoryContext per_query_ctx,
AttInMetadata *attinmeta,
Tuplestorestate *tupstore);
typedef struct
{
float8 mean; /* mean of the distribution */
float8 stddev; /* stddev of the distribution */
float8 carry_val; /* hold second generated value */
bool use_carry; /* use second generated value */
} normal_rand_fctx;
#define xpfree(var_) \
do { \
if (var_ != NULL) \
{ \
pfree(var_); \
var_ = NULL; \
} \
} while (0)
#define xpstrdup(tgtvar_, srcvar_) \
do { \
if (srcvar_) \
tgtvar_ = pstrdup(srcvar_); \
else \
tgtvar_ = NULL; \
} while (0)
#define xstreq(tgtvar_, srcvar_) \
(((tgtvar_ == NULL) && (srcvar_ == NULL)) || \
((tgtvar_ != NULL) && (srcvar_ != NULL) && (strcmp(tgtvar_, srcvar_) == 0)))
/* sign, 10 digits, '\0' */
#define INT32_STRLEN 12
/* stored info for a crosstab category */
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
typedef struct crosstab_cat_desc
{
char *catname; /* full category name */
uint64 attidx; /* zero based */
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
} crosstab_cat_desc;
#define MAX_CATNAME_LEN NAMEDATALEN
#define INIT_CATS 64
#define crosstab_HashTableLookup(HASHTAB, CATNAME, CATDESC) \
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
do { \
crosstab_HashEnt *hentry; char key[MAX_CATNAME_LEN]; \
\
MemSet(key, 0, MAX_CATNAME_LEN); \
snprintf(key, MAX_CATNAME_LEN - 1, "%s", CATNAME); \
hentry = (crosstab_HashEnt*) hash_search(HASHTAB, \
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
key, HASH_FIND, NULL); \
if (hentry) \
CATDESC = hentry->catdesc; \
else \
CATDESC = NULL; \
} while(0)
#define crosstab_HashTableInsert(HASHTAB, CATDESC) \
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
do { \
crosstab_HashEnt *hentry; bool found; char key[MAX_CATNAME_LEN]; \
\
MemSet(key, 0, MAX_CATNAME_LEN); \
snprintf(key, MAX_CATNAME_LEN - 1, "%s", CATDESC->catname); \
hentry = (crosstab_HashEnt*) hash_search(HASHTAB, \
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
key, HASH_ENTER, &found); \
if (found) \
ereport(ERROR, \
(errcode(ERRCODE_DUPLICATE_OBJECT), \
errmsg("duplicate category name"))); \
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
hentry->catdesc = CATDESC; \
} while(0)
/* hash table */
typedef struct crosstab_hashent
{
char internal_catname[MAX_CATNAME_LEN];
crosstab_cat_desc *catdesc;
} crosstab_HashEnt;
/*
* normal_rand - return requested number of random values
* with a Gaussian (Normal) distribution.
*
* inputs are int numvals, float8 mean, and float8 stddev
* returns setof float8
*/
PG_FUNCTION_INFO_V1(normal_rand);
Datum
normal_rand(PG_FUNCTION_ARGS)
{
FuncCallContext *funcctx;
uint64 call_cntr;
uint64 max_calls;
normal_rand_fctx *fctx;
float8 mean;
float8 stddev;
float8 carry_val;
bool use_carry;
MemoryContext oldcontext;
/* stuff done only on the first call of the function */
if (SRF_IS_FIRSTCALL())
{
int32 num_tuples;
/* create a function context for cross-call persistence */
funcctx = SRF_FIRSTCALL_INIT();
/*
* switch to memory context appropriate for multiple function calls
*/
oldcontext = MemoryContextSwitchTo(funcctx->multi_call_memory_ctx);
/* total number of tuples to be returned */
num_tuples = PG_GETARG_INT32(0);
if (num_tuples < 0)
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("number of rows cannot be negative")));
funcctx->max_calls = num_tuples;
/* allocate memory for user context */
fctx = (normal_rand_fctx *) palloc(sizeof(normal_rand_fctx));
/*
* Use fctx to keep track of upper and lower bounds from call to call.
* It will also be used to carry over the spare value we get from the
* Box-Muller algorithm so that we only actually calculate a new value
* every other call.
*/
fctx->mean = PG_GETARG_FLOAT8(1);
fctx->stddev = PG_GETARG_FLOAT8(2);
fctx->carry_val = 0;
fctx->use_carry = false;
funcctx->user_fctx = fctx;
MemoryContextSwitchTo(oldcontext);
}
/* stuff done on every call of the function */
funcctx = SRF_PERCALL_SETUP();
call_cntr = funcctx->call_cntr;
max_calls = funcctx->max_calls;
fctx = funcctx->user_fctx;
mean = fctx->mean;
stddev = fctx->stddev;
carry_val = fctx->carry_val;
use_carry = fctx->use_carry;
2002-09-05 00:31:48 +04:00
if (call_cntr < max_calls) /* do when there is more left to send */
{
float8 result;
if (use_carry)
{
/*
* reset use_carry and use second value obtained on last pass
*/
fctx->use_carry = false;
result = carry_val;
}
else
{
float8 normval_1;
float8 normval_2;
/* Get the next two normal values */
get_normal_pair(&normval_1, &normval_2);
/* use the first */
result = mean + (stddev * normval_1);
/* and save the second */
fctx->carry_val = mean + (stddev * normval_2);
fctx->use_carry = true;
}
/* send the result */
SRF_RETURN_NEXT(funcctx, Float8GetDatum(result));
}
else
/* do when there is no more left */
SRF_RETURN_DONE(funcctx);
}
/*
* get_normal_pair()
* Assigns normally distributed (Gaussian) values to a pair of provided
* parameters, with mean 0, standard deviation 1.
*
* This routine implements Algorithm P (Polar method for normal deviates)
* from Knuth's _The_Art_of_Computer_Programming_, Volume 2, 3rd ed., pages
* 122-126. Knuth cites his source as "The polar method", G. E. P. Box, M. E.
* Muller, and G. Marsaglia, _Annals_Math,_Stat._ 29 (1958), 610-611.
*
*/
static void
get_normal_pair(float8 *x1, float8 *x2)
{
float8 u1,
u2,
v1,
v2,
s;
do
{
u1 = pg_prng_double(&pg_global_prng_state);
u2 = pg_prng_double(&pg_global_prng_state);
v1 = (2.0 * u1) - 1.0;
v2 = (2.0 * u2) - 1.0;
s = v1 * v1 + v2 * v2;
} while (s >= 1.0);
if (s == 0)
{
*x1 = 0;
*x2 = 0;
}
else
{
s = sqrt((-2.0 * log(s)) / s);
*x1 = v1 * s;
*x2 = v2 * s;
}
}
/*
* crosstab - create a crosstab of rowids and values columns from a
* SQL statement returning one rowid column, one category column,
* and one value column.
*
* e.g. given sql which produces:
*
* rowid cat value
* ------+-------+-------
* row1 cat1 val1
* row1 cat2 val2
* row1 cat3 val3
* row1 cat4 val4
* row2 cat1 val5
* row2 cat2 val6
* row2 cat3 val7
* row2 cat4 val8
*
* crosstab returns:
* <===== values columns =====>
* rowid cat1 cat2 cat3 cat4
* ------+-------+-------+-------+-------
* row1 val1 val2 val3 val4
* row2 val5 val6 val7 val8
*
* NOTES:
* 1. SQL result must be ordered by 1,2.
* 2. The number of values columns depends on the tuple description
* of the function's declared return type. The return type's columns
* must match the datatypes of the SQL query's result. The datatype
* of the category column can be anything, however.
* 3. Missing values (i.e. not enough adjacent rows of same rowid to
* fill the number of result values columns) are filled in with nulls.
* 4. Extra values (i.e. too many adjacent rows of same rowid to fill
* the number of result values columns) are skipped.
* 5. Rows with all nulls in the values columns are skipped.
*/
PG_FUNCTION_INFO_V1(crosstab);
Datum
crosstab(PG_FUNCTION_ARGS)
{
char *sql = text_to_cstring(PG_GETARG_TEXT_PP(0));
ReturnSetInfo *rsinfo = (ReturnSetInfo *) fcinfo->resultinfo;
Tuplestorestate *tupstore;
TupleDesc tupdesc;
uint64 call_cntr;
uint64 max_calls;
AttInMetadata *attinmeta;
SPITupleTable *spi_tuptable;
TupleDesc spi_tupdesc;
bool firstpass;
char *lastrowid;
int i;
int num_categories;
MemoryContext per_query_ctx;
MemoryContext oldcontext;
int ret;
uint64 proc;
/* check to see if caller supports us returning a tuplestore */
if (rsinfo == NULL || !IsA(rsinfo, ReturnSetInfo))
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("set-valued function called in context that cannot accept a set")));
if (!(rsinfo->allowedModes & SFRM_Materialize))
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("materialize mode required, but it is not allowed in this context")));
per_query_ctx = rsinfo->econtext->ecxt_per_query_memory;
/* Connect to SPI manager */
if ((ret = SPI_connect()) < 0)
/* internal error */
elog(ERROR, "crosstab: SPI_connect returned %d", ret);
/* Retrieve the desired rows */
ret = SPI_execute(sql, true, 0);
proc = SPI_processed;
/* If no qualifying tuples, fall out early */
if (ret != SPI_OK_SELECT || proc == 0)
{
SPI_finish();
rsinfo->isDone = ExprEndResult;
PG_RETURN_NULL();
}
spi_tuptable = SPI_tuptable;
spi_tupdesc = spi_tuptable->tupdesc;
/*----------
* The provided SQL query must always return three columns.
*
* 1. rowname
* the label or identifier for each row in the final result
* 2. category
* the label or identifier for each column in the final result
* 3. values
* the value for each column in the final result
*----------
*/
if (spi_tupdesc->natts != 3)
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("invalid source data SQL statement"),
errdetail("The provided SQL must return 3 "
"columns: rowid, category, and values.")));
/* get a tuple descriptor for our result type */
switch (get_call_result_type(fcinfo, NULL, &tupdesc))
{
case TYPEFUNC_COMPOSITE:
/* success */
break;
case TYPEFUNC_RECORD:
/* failed to determine actual type of RECORD */
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("function returning record called in context "
"that cannot accept type record")));
break;
default:
/* result type isn't composite */
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("return type must be a row type")));
break;
}
/*
* Check that return tupdesc is compatible with the data we got from SPI,
* at least based on number and type of attributes
*/
if (!compatCrosstabTupleDescs(tupdesc, spi_tupdesc))
ereport(ERROR,
(errcode(ERRCODE_SYNTAX_ERROR),
errmsg("return and sql tuple descriptions are " \
"incompatible")));
/*
* switch to long-lived memory context
*/
oldcontext = MemoryContextSwitchTo(per_query_ctx);
/* make sure we have a persistent copy of the result tupdesc */
tupdesc = CreateTupleDescCopy(tupdesc);
/* initialize our tuplestore in long-lived context */
tupstore =
tuplestore_begin_heap(rsinfo->allowedModes & SFRM_Materialize_Random,
false, work_mem);
MemoryContextSwitchTo(oldcontext);
/*
* Generate attribute metadata needed later to produce tuples from raw C
* strings
*/
attinmeta = TupleDescGetAttInMetadata(tupdesc);
/* total number of tuples to be examined */
max_calls = proc;
/* the return tuple always must have 1 rowid + num_categories columns */
num_categories = tupdesc->natts - 1;
2002-09-05 00:31:48 +04:00
firstpass = true;
lastrowid = NULL;
for (call_cntr = 0; call_cntr < max_calls; call_cntr++)
{
bool skip_tuple = false;
char **values;
/* allocate and zero space */
values = (char **) palloc0((1 + num_categories) * sizeof(char *));
/*
* now loop through the sql results and assign each value in sequence
* to the next category
*/
for (i = 0; i < num_categories; i++)
{
HeapTuple spi_tuple;
char *rowid;
/* see if we've gone too far already */
if (call_cntr >= max_calls)
break;
/* get the next sql result tuple */
spi_tuple = spi_tuptable->vals[call_cntr];
/* get the rowid from the current sql result tuple */
rowid = SPI_getvalue(spi_tuple, spi_tupdesc, 1);
/*
* If this is the first pass through the values for this rowid,
* set the first column to rowid
*/
if (i == 0)
{
xpstrdup(values[0], rowid);
/*
* Check to see if the rowid is the same as that of the last
* tuple sent -- if so, skip this tuple entirely
*/
if (!firstpass && xstreq(lastrowid, rowid))
{
xpfree(rowid);
skip_tuple = true;
break;
}
}
/*
* If rowid hasn't changed on us, continue building the output
* tuple.
*/
if (xstreq(rowid, values[0]))
{
/*
* Get the next category item value, which is always attribute
* number three.
*
* Be careful to assign the value to the array index based on
* which category we are presently processing.
*/
values[1 + i] = SPI_getvalue(spi_tuple, spi_tupdesc, 3);
/*
* increment the counter since we consume a row for each
* category, but not for last pass because the outer loop will
* do that for us
*/
if (i < (num_categories - 1))
call_cntr++;
xpfree(rowid);
}
else
{
/*
* We'll fill in NULLs for the missing values, but we need to
* decrement the counter since this sql result row doesn't
* belong to the current output tuple.
*/
call_cntr--;
xpfree(rowid);
break;
}
}
if (!skip_tuple)
{
HeapTuple tuple;
/* build the tuple and store it */
tuple = BuildTupleFromCStrings(attinmeta, values);
tuplestore_puttuple(tupstore, tuple);
heap_freetuple(tuple);
}
/* Remember current rowid */
xpfree(lastrowid);
xpstrdup(lastrowid, values[0]);
firstpass = false;
/* Clean up */
for (i = 0; i < num_categories + 1; i++)
if (values[i] != NULL)
pfree(values[i]);
pfree(values);
}
/* let the caller know we're sending back a tuplestore */
rsinfo->returnMode = SFRM_Materialize;
rsinfo->setResult = tupstore;
rsinfo->setDesc = tupdesc;
/* release SPI related resources (and return to caller's context) */
SPI_finish();
return (Datum) 0;
}
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/*
* crosstab_hash - reimplement crosstab as materialized function and
* properly deal with missing values (i.e. don't pack remaining
* values to the left)
*
* crosstab - create a crosstab of rowids and values columns from a
* SQL statement returning one rowid column, one category column,
* and one value column.
*
* e.g. given sql which produces:
*
* rowid cat value
* ------+-------+-------
* row1 cat1 val1
* row1 cat2 val2
* row1 cat4 val4
* row2 cat1 val5
* row2 cat2 val6
* row2 cat3 val7
* row2 cat4 val8
*
* crosstab returns:
* <===== values columns =====>
* rowid cat1 cat2 cat3 cat4
* ------+-------+-------+-------+-------
* row1 val1 val2 null val4
* row2 val5 val6 val7 val8
*
* NOTES:
* 1. SQL result must be ordered by 1.
* 2. The number of values columns depends on the tuple description
* of the function's declared return type.
* 3. Missing values (i.e. missing category) are filled in with nulls.
* 4. Extra values (i.e. not in category results) are skipped.
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
*/
PG_FUNCTION_INFO_V1(crosstab_hash);
Datum
crosstab_hash(PG_FUNCTION_ARGS)
{
char *sql = text_to_cstring(PG_GETARG_TEXT_PP(0));
char *cats_sql = text_to_cstring(PG_GETARG_TEXT_PP(1));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
ReturnSetInfo *rsinfo = (ReturnSetInfo *) fcinfo->resultinfo;
TupleDesc tupdesc;
MemoryContext per_query_ctx;
MemoryContext oldcontext;
HTAB *crosstab_hash;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* check to see if caller supports us returning a tuplestore */
if (rsinfo == NULL || !IsA(rsinfo, ReturnSetInfo))
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("set-valued function called in context that cannot accept a set")));
if (!(rsinfo->allowedModes & SFRM_Materialize) ||
rsinfo->expectedDesc == NULL)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("materialize mode required, but it is not allowed in this context")));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
per_query_ctx = rsinfo->econtext->ecxt_per_query_memory;
oldcontext = MemoryContextSwitchTo(per_query_ctx);
/* get the requested return tuple description */
tupdesc = CreateTupleDescCopy(rsinfo->expectedDesc);
/*
* Check to make sure we have a reasonable tuple descriptor
*
* Note we will attempt to coerce the values into whatever the return
* attribute type is and depend on the "in" function to complain if
* needed.
*/
if (tupdesc->natts < 2)
ereport(ERROR,
(errcode(ERRCODE_SYNTAX_ERROR),
errmsg("query-specified return tuple and " \
"crosstab function are not compatible")));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* load up the categories hash table */
crosstab_hash = load_categories_hash(cats_sql, per_query_ctx);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* let the caller know we're sending back a tuplestore */
rsinfo->returnMode = SFRM_Materialize;
/* now go build it */
rsinfo->setResult = get_crosstab_tuplestore(sql,
crosstab_hash,
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
tupdesc,
rsinfo->allowedModes & SFRM_Materialize_Random);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/*
* SFRM_Materialize mode expects us to return a NULL Datum. The actual
* tuples are in our tuplestore and passed back through rsinfo->setResult.
* rsinfo->setDesc is set to the tuple description that we actually used
* to build our tuples with, so the caller can verify we did what it was
* expecting.
*/
rsinfo->setDesc = tupdesc;
MemoryContextSwitchTo(oldcontext);
return (Datum) 0;
}
/*
* load up the categories hash table
*/
static HTAB *
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
load_categories_hash(char *cats_sql, MemoryContext per_query_ctx)
{
HTAB *crosstab_hash;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
HASHCTL ctl;
int ret;
uint64 proc;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
MemoryContext SPIcontext;
/* initialize the category hash table */
ctl.keysize = MAX_CATNAME_LEN;
ctl.entrysize = sizeof(crosstab_HashEnt);
ctl.hcxt = per_query_ctx;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/*
* use INIT_CATS, defined above as a guess of how many hash table entries
* to create, initially
*/
crosstab_hash = hash_create("crosstab hash",
INIT_CATS,
&ctl,
Improve hash_create()'s API for some added robustness. Invent a new flag bit HASH_STRINGS to specify C-string hashing, which was formerly the default; and add assertions insisting that exactly one of the bits HASH_STRINGS, HASH_BLOBS, and HASH_FUNCTION be set. This is in hopes of preventing recurrences of the type of oversight fixed in commit a1b8aa1e4 (i.e., mistakenly omitting HASH_BLOBS). Also, when HASH_STRINGS is specified, insist that the keysize be more than 8 bytes. This is a heuristic, but it should catch accidental use of HASH_STRINGS for integer or pointer keys. (Nearly all existing use-cases set the keysize to NAMEDATALEN or more, so there's little reason to think this restriction should be problematic.) Tweak hash_create() to insist that the HASH_ELEM flag be set, and remove the defaults it had for keysize and entrysize. Since those defaults were undocumented and basically useless, no callers omitted HASH_ELEM anyway. Also, remove memset's zeroing the HASHCTL parameter struct from those callers that had one. This has never been really necessary, and while it wasn't a bad coding convention it was confusing that some callers did it and some did not. We might as well save a few cycles by standardizing on "not". Also improve the documentation for hash_create(). In passing, improve reinit.c's usage of a hash table by storing the key as a binary Oid rather than a string; and, since that's a temporary hash table, allocate it in CurrentMemoryContext for neatness. Discussion: https://postgr.es/m/590625.1607878171@sss.pgh.pa.us
2020-12-15 19:38:53 +03:00
HASH_ELEM | HASH_STRINGS | HASH_CONTEXT);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* Connect to SPI manager */
if ((ret = SPI_connect()) < 0)
/* internal error */
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
elog(ERROR, "load_categories_hash: SPI_connect returned %d", ret);
/* Retrieve the category name rows */
ret = SPI_execute(cats_sql, true, 0);
proc = SPI_processed;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* Check for qualifying tuples */
if ((ret == SPI_OK_SELECT) && (proc > 0))
{
SPITupleTable *spi_tuptable = SPI_tuptable;
TupleDesc spi_tupdesc = spi_tuptable->tupdesc;
uint64 i;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/*
* The provided categories SQL query must always return one column:
* category - the label or identifier for each column
*/
if (spi_tupdesc->natts != 1)
ereport(ERROR,
(errcode(ERRCODE_SYNTAX_ERROR),
errmsg("provided \"categories\" SQL must " \
"return 1 column of at least one row")));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
for (i = 0; i < proc; i++)
{
crosstab_cat_desc *catdesc;
char *catname;
HeapTuple spi_tuple;
/* get the next sql result tuple */
spi_tuple = spi_tuptable->vals[i];
/* get the category from the current sql result tuple */
catname = SPI_getvalue(spi_tuple, spi_tupdesc, 1);
if (catname == NULL)
ereport(ERROR,
(errcode(ERRCODE_SYNTAX_ERROR),
errmsg("provided \"categories\" SQL must " \
"not return NULL values")));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
SPIcontext = MemoryContextSwitchTo(per_query_ctx);
catdesc = (crosstab_cat_desc *) palloc(sizeof(crosstab_cat_desc));
catdesc->catname = catname;
catdesc->attidx = i;
/* Add the proc description block to the hashtable */
crosstab_HashTableInsert(crosstab_hash, catdesc);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
MemoryContextSwitchTo(SPIcontext);
}
}
if (SPI_finish() != SPI_OK_FINISH)
/* internal error */
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
elog(ERROR, "load_categories_hash: SPI_finish() failed");
return crosstab_hash;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
}
/*
* create and populate the crosstab tuplestore using the provided source query
*/
static Tuplestorestate *
get_crosstab_tuplestore(char *sql,
HTAB *crosstab_hash,
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
TupleDesc tupdesc,
bool randomAccess)
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
{
Tuplestorestate *tupstore;
int num_categories = hash_get_num_entries(crosstab_hash);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
AttInMetadata *attinmeta = TupleDescGetAttInMetadata(tupdesc);
char **values;
HeapTuple tuple;
int ret;
uint64 proc;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* initialize our tuplestore (while still in query context!) */
tupstore = tuplestore_begin_heap(randomAccess, false, work_mem);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* Connect to SPI manager */
if ((ret = SPI_connect()) < 0)
/* internal error */
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
elog(ERROR, "get_crosstab_tuplestore: SPI_connect returned %d", ret);
/* Now retrieve the crosstab source rows */
ret = SPI_execute(sql, true, 0);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
proc = SPI_processed;
/* Check for qualifying tuples */
if ((ret == SPI_OK_SELECT) && (proc > 0))
{
SPITupleTable *spi_tuptable = SPI_tuptable;
TupleDesc spi_tupdesc = spi_tuptable->tupdesc;
int ncols = spi_tupdesc->natts;
char *rowid;
char *lastrowid = NULL;
bool firstpass = true;
uint64 i;
int j;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
int result_ncols;
if (num_categories == 0)
{
/* no qualifying category tuples */
ereport(ERROR,
(errcode(ERRCODE_SYNTAX_ERROR),
errmsg("provided \"categories\" SQL must " \
"return 1 column of at least one row")));
}
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/*
* The provided SQL query must always return at least three columns:
*
* 1. rowname the label for each row - column 1 in the final result
* 2. category the label for each value-column in the final result 3.
* value the values used to populate the value-columns
*
* If there are more than three columns, the last two are taken as
* "category" and "values". The first column is taken as "rowname".
* Additional columns (2 thru N-2) are assumed the same for the same
* "rowname", and are copied into the result tuple from the first time
* we encounter a particular rowname.
*/
if (ncols < 3)
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("invalid source data SQL statement"),
errdetail("The provided SQL must return 3 " \
" columns; rowid, category, and values.")));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
result_ncols = (ncols - 2) + num_categories;
/* Recheck to make sure we tuple descriptor still looks reasonable */
if (tupdesc->natts != result_ncols)
ereport(ERROR,
(errcode(ERRCODE_SYNTAX_ERROR),
errmsg("invalid return type"),
errdetail("Query-specified return " \
"tuple has %d columns but crosstab " \
"returns %d.", tupdesc->natts, result_ncols)));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
/* allocate space and make sure it's clear */
values = (char **) palloc0(result_ncols * sizeof(char *));
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
for (i = 0; i < proc; i++)
{
HeapTuple spi_tuple;
crosstab_cat_desc *catdesc;
char *catname;
/* get the next sql result tuple */
spi_tuple = spi_tuptable->vals[i];
/* get the rowid from the current sql result tuple */
rowid = SPI_getvalue(spi_tuple, spi_tupdesc, 1);
/*
* if we're on a new output row, grab the column values up to
* column N-2 now
*/
if (firstpass || !xstreq(lastrowid, rowid))
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
{
/*
* a new row means we need to flush the old one first, unless
* we're on the very first row
*/
if (!firstpass)
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
{
/* rowid changed, flush the previous output row */
tuple = BuildTupleFromCStrings(attinmeta, values);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
tuplestore_puttuple(tupstore, tuple);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
for (j = 0; j < result_ncols; j++)
xpfree(values[j]);
}
values[0] = rowid;
for (j = 1; j < ncols - 2; j++)
values[j] = SPI_getvalue(spi_tuple, spi_tupdesc, j + 1);
/* we're no longer on the first pass */
firstpass = false;
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
}
/* look up the category and fill in the appropriate column */
catname = SPI_getvalue(spi_tuple, spi_tupdesc, ncols - 1);
if (catname != NULL)
{
crosstab_HashTableLookup(crosstab_hash, catname, catdesc);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
if (catdesc)
values[catdesc->attidx + ncols - 2] =
SPI_getvalue(spi_tuple, spi_tupdesc, ncols);
}
xpfree(lastrowid);
xpstrdup(lastrowid, rowid);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
}
/* flush the last output row */
tuple = BuildTupleFromCStrings(attinmeta, values);
tuplestore_puttuple(tupstore, tuple);
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
}
if (SPI_finish() != SPI_OK_FINISH)
/* internal error */
Attached is an update to contrib/tablefunc. It implements a new hashed version of crosstab. This fixes a major deficiency in real-world use of the 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_startdate text, 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_startdate timestamp, 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 the result tuple when it encounters missing data. In order to work around this you have to be make your source sql do all sorts of contortions (cartesian join of distinct rowid with distinct attribute; left join that back to the real source data). The new version avoids this by building a hash table using a second distinct attribute query. The new version also allows for "extra" columns (see the README) and allows the result columns to be coerced into differing datatypes if they are suitable (as shown above). In testing a "real-world" data set (69 distinct rowid's, 27 distinct categories/attributes, multiple missing data points) I saw about a 5-fold improvement in execution time (from about 2200 ms old, to 440 ms new). I left the original version intact because: 1) BC, 2) it is probably slightly faster if you know that you have no missing attributes. README and regression test adjustments included. If there are no objections, please apply. Joe Conway
2003-03-20 09:46:30 +03:00
elog(ERROR, "get_crosstab_tuplestore: SPI_finish() failed");
return tupstore;
}
/*
* connectby_text - produce a result set from a hierarchical (parent/child)
* table.
*
* e.g. given table foo:
*
* keyid parent_keyid pos
* ------+------------+--
* row1 NULL 0
* row2 row1 0
* row3 row1 0
* row4 row2 1
* row5 row2 0
* row6 row4 0
* row7 row3 0
* row8 row6 0
* row9 row5 0
*
*
* connectby(text relname, text keyid_fld, text parent_keyid_fld
* [, text orderby_fld], text start_with, int max_depth
* [, text branch_delim])
* connectby('foo', 'keyid', 'parent_keyid', 'pos', 'row2', 0, '~') returns:
*
* keyid parent_id level branch serial
* ------+-----------+--------+-----------------------
* row2 NULL 0 row2 1
* row5 row2 1 row2~row5 2
* row9 row5 2 row2~row5~row9 3
* row4 row2 1 row2~row4 4
* row6 row4 2 row2~row4~row6 5
* row8 row6 3 row2~row4~row6~row8 6
*
*/
PG_FUNCTION_INFO_V1(connectby_text);
#define CONNECTBY_NCOLS 4
#define CONNECTBY_NCOLS_NOBRANCH 3
Datum
connectby_text(PG_FUNCTION_ARGS)
{
char *relname = text_to_cstring(PG_GETARG_TEXT_PP(0));
char *key_fld = text_to_cstring(PG_GETARG_TEXT_PP(1));
char *parent_key_fld = text_to_cstring(PG_GETARG_TEXT_PP(2));
char *start_with = text_to_cstring(PG_GETARG_TEXT_PP(3));
int max_depth = PG_GETARG_INT32(4);
char *branch_delim = NULL;
bool show_branch = false;
bool show_serial = false;
ReturnSetInfo *rsinfo = (ReturnSetInfo *) fcinfo->resultinfo;
TupleDesc tupdesc;
AttInMetadata *attinmeta;
MemoryContext per_query_ctx;
MemoryContext oldcontext;
/* check to see if caller supports us returning a tuplestore */
if (rsinfo == NULL || !IsA(rsinfo, ReturnSetInfo))
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("set-valued function called in context that cannot accept a set")));
if (!(rsinfo->allowedModes & SFRM_Materialize) ||
rsinfo->expectedDesc == NULL)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("materialize mode required, but it is not allowed in this context")));
if (fcinfo->nargs == 6)
{
branch_delim = text_to_cstring(PG_GETARG_TEXT_PP(5));
show_branch = true;
}
else
/* default is no show, tilde for the delimiter */
branch_delim = pstrdup("~");
per_query_ctx = rsinfo->econtext->ecxt_per_query_memory;
oldcontext = MemoryContextSwitchTo(per_query_ctx);
/* get the requested return tuple description */
tupdesc = CreateTupleDescCopy(rsinfo->expectedDesc);
/* does it meet our needs */
validateConnectbyTupleDesc(tupdesc, show_branch, show_serial);
/* OK, use it then */
attinmeta = TupleDescGetAttInMetadata(tupdesc);
/* OK, go to work */
rsinfo->returnMode = SFRM_Materialize;
rsinfo->setResult = connectby(relname,
key_fld,
parent_key_fld,
NULL,
branch_delim,
start_with,
max_depth,
show_branch,
show_serial,
per_query_ctx,
rsinfo->allowedModes & SFRM_Materialize_Random,
attinmeta);
rsinfo->setDesc = tupdesc;
MemoryContextSwitchTo(oldcontext);
/*
* SFRM_Materialize mode expects us to return a NULL Datum. The actual
* tuples are in our tuplestore and passed back through rsinfo->setResult.
* rsinfo->setDesc is set to the tuple description that we actually used
* to build our tuples with, so the caller can verify we did what it was
* expecting.
*/
return (Datum) 0;
}
PG_FUNCTION_INFO_V1(connectby_text_serial);
Datum
connectby_text_serial(PG_FUNCTION_ARGS)
{
char *relname = text_to_cstring(PG_GETARG_TEXT_PP(0));
char *key_fld = text_to_cstring(PG_GETARG_TEXT_PP(1));
char *parent_key_fld = text_to_cstring(PG_GETARG_TEXT_PP(2));
char *orderby_fld = text_to_cstring(PG_GETARG_TEXT_PP(3));
char *start_with = text_to_cstring(PG_GETARG_TEXT_PP(4));
int max_depth = PG_GETARG_INT32(5);
char *branch_delim = NULL;
bool show_branch = false;
bool show_serial = true;
ReturnSetInfo *rsinfo = (ReturnSetInfo *) fcinfo->resultinfo;
TupleDesc tupdesc;
AttInMetadata *attinmeta;
MemoryContext per_query_ctx;
MemoryContext oldcontext;
/* check to see if caller supports us returning a tuplestore */
if (rsinfo == NULL || !IsA(rsinfo, ReturnSetInfo))
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("set-valued function called in context that cannot accept a set")));
if (!(rsinfo->allowedModes & SFRM_Materialize) ||
rsinfo->expectedDesc == NULL)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("materialize mode required, but it is not allowed in this context")));
if (fcinfo->nargs == 7)
{
branch_delim = text_to_cstring(PG_GETARG_TEXT_PP(6));
show_branch = true;
}
else
/* default is no show, tilde for the delimiter */
branch_delim = pstrdup("~");
per_query_ctx = rsinfo->econtext->ecxt_per_query_memory;
oldcontext = MemoryContextSwitchTo(per_query_ctx);
/* get the requested return tuple description */
tupdesc = CreateTupleDescCopy(rsinfo->expectedDesc);
/* does it meet our needs */
validateConnectbyTupleDesc(tupdesc, show_branch, show_serial);
/* OK, use it then */
attinmeta = TupleDescGetAttInMetadata(tupdesc);
/* OK, go to work */
rsinfo->returnMode = SFRM_Materialize;
rsinfo->setResult = connectby(relname,
key_fld,
parent_key_fld,
orderby_fld,
branch_delim,
start_with,
max_depth,
show_branch,
show_serial,
per_query_ctx,
rsinfo->allowedModes & SFRM_Materialize_Random,
attinmeta);
rsinfo->setDesc = tupdesc;
MemoryContextSwitchTo(oldcontext);
/*
* SFRM_Materialize mode expects us to return a NULL Datum. The actual
* tuples are in our tuplestore and passed back through rsinfo->setResult.
* rsinfo->setDesc is set to the tuple description that we actually used
* to build our tuples with, so the caller can verify we did what it was
* expecting.
*/
return (Datum) 0;
}
/*
* connectby - does the real work for connectby_text()
*/
static Tuplestorestate *
connectby(char *relname,
char *key_fld,
char *parent_key_fld,
char *orderby_fld,
char *branch_delim,
char *start_with,
int max_depth,
bool show_branch,
bool show_serial,
MemoryContext per_query_ctx,
bool randomAccess,
AttInMetadata *attinmeta)
{
Tuplestorestate *tupstore = NULL;
int ret;
MemoryContext oldcontext;
int serial = 1;
/* Connect to SPI manager */
if ((ret = SPI_connect()) < 0)
/* internal error */
elog(ERROR, "connectby: SPI_connect returned %d", ret);
/* switch to longer term context to create the tuple store */
oldcontext = MemoryContextSwitchTo(per_query_ctx);
/* initialize our tuplestore */
tupstore = tuplestore_begin_heap(randomAccess, false, work_mem);
MemoryContextSwitchTo(oldcontext);
/* now go get the whole tree */
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
build_tuplestore_recursively(key_fld,
parent_key_fld,
relname,
orderby_fld,
branch_delim,
start_with,
start_with, /* current_branch */
0, /* initial level is 0 */
&serial, /* initial serial is 1 */
max_depth,
show_branch,
show_serial,
per_query_ctx,
attinmeta,
tupstore);
SPI_finish();
return tupstore;
}
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
static void
build_tuplestore_recursively(char *key_fld,
char *parent_key_fld,
char *relname,
char *orderby_fld,
char *branch_delim,
char *start_with,
char *branch,
int level,
int *serial,
int max_depth,
bool show_branch,
bool show_serial,
MemoryContext per_query_ctx,
AttInMetadata *attinmeta,
Tuplestorestate *tupstore)
{
TupleDesc tupdesc = attinmeta->tupdesc;
int ret;
uint64 proc;
int serial_column;
StringInfoData sql;
char **values;
char *current_key;
char *current_key_parent;
char current_level[INT32_STRLEN];
char serial_str[INT32_STRLEN];
char *current_branch;
HeapTuple tuple;
if (max_depth > 0 && level > max_depth)
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
return;
initStringInfo(&sql);
/* Build initial sql statement */
if (!show_serial)
{
appendStringInfo(&sql, "SELECT %s, %s FROM %s WHERE %s = %s AND %s IS NOT NULL AND %s <> %s",
key_fld,
parent_key_fld,
relname,
parent_key_fld,
quote_literal_cstr(start_with),
key_fld, key_fld, parent_key_fld);
serial_column = 0;
}
else
{
appendStringInfo(&sql, "SELECT %s, %s FROM %s WHERE %s = %s AND %s IS NOT NULL AND %s <> %s ORDER BY %s",
key_fld,
parent_key_fld,
relname,
parent_key_fld,
quote_literal_cstr(start_with),
key_fld, key_fld, parent_key_fld,
orderby_fld);
serial_column = 1;
}
if (show_branch)
values = (char **) palloc((CONNECTBY_NCOLS + serial_column) * sizeof(char *));
else
values = (char **) palloc((CONNECTBY_NCOLS_NOBRANCH + serial_column) * sizeof(char *));
/* First time through, do a little setup */
if (level == 0)
{
/* root value is the one we initially start with */
values[0] = start_with;
/* root value has no parent */
values[1] = NULL;
/* root level is 0 */
sprintf(current_level, "%d", level);
values[2] = current_level;
/* root branch is just starting root value */
if (show_branch)
values[3] = start_with;
/* root starts the serial with 1 */
if (show_serial)
{
sprintf(serial_str, "%d", (*serial)++);
if (show_branch)
values[4] = serial_str;
else
values[3] = serial_str;
}
/* construct the tuple */
tuple = BuildTupleFromCStrings(attinmeta, values);
/* now store it */
tuplestore_puttuple(tupstore, tuple);
/* increment level */
level++;
}
/* Retrieve the desired rows */
ret = SPI_execute(sql.data, true, 0);
proc = SPI_processed;
/* Check for qualifying tuples */
if ((ret == SPI_OK_SELECT) && (proc > 0))
{
HeapTuple spi_tuple;
SPITupleTable *tuptable = SPI_tuptable;
TupleDesc spi_tupdesc = tuptable->tupdesc;
uint64 i;
StringInfoData branchstr;
StringInfoData chk_branchstr;
StringInfoData chk_current_key;
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
/*
* Check that return tupdesc is compatible with the one we got from
* the query.
*/
compatConnectbyTupleDescs(tupdesc, spi_tupdesc);
initStringInfo(&branchstr);
initStringInfo(&chk_branchstr);
initStringInfo(&chk_current_key);
for (i = 0; i < proc; i++)
{
> Now I'm testing connectby() in the /contrib/tablefunc in 7.3b1, which would > be a useful function for many users. However, I found the fact that > if connectby_tree has the following data, connectby() tries to search the end > of roots without knowing that the relations are infinite(-5-9-10-11-9-10-11-) . > I hope connectby() supports a check routine to find infinite relations. > > > CREATE TABLE connectby_tree(keyid int, parent_keyid int); > INSERT INTO connectby_tree VALUES(1,NULL); > INSERT INTO connectby_tree VALUES(2,1); > INSERT INTO connectby_tree VALUES(3,1); > INSERT INTO connectby_tree VALUES(4,2); > INSERT INTO connectby_tree VALUES(5,2); > INSERT INTO connectby_tree VALUES(6,4); > INSERT INTO connectby_tree VALUES(7,3); > INSERT INTO connectby_tree VALUES(8,6); > INSERT INTO connectby_tree VALUES(9,5); > > INSERT INTO connectby_tree VALUES(10,9); > INSERT INTO connectby_tree VALUES(11,10); > INSERT INTO connectby_tree VALUES(9,11); <-- infinite > The attached patch fixes the infinite recursion bug in contrib/tablefunc/tablefunc.c:connectby found by Masaru Sugawara. test=# SELECT * FROM connectby('connectby_tree', 'keyid', 'parent_keyid', '2', 4, '~') AS t(keyid int, parent_keyid int, level int, branch text); keyid | parent_keyid | level | branch -------+--------------+-------+------------- 2 | | 0 | 2 4 | 2 | 1 | 2~4 6 | 4 | 2 | 2~4~6 8 | 6 | 3 | 2~4~6~8 5 | 2 | 1 | 2~5 9 | 5 | 2 | 2~5~9 10 | 9 | 3 | 2~5~9~10 11 | 10 | 4 | 2~5~9~10~11 (8 rows) test=# SELECT * FROM connectby('connectby_tree', 'keyid', 'parent_keyid', '2', 5, '~') AS t(keyid int, parent_keyid int, level int, branch text); ERROR: infinite recursion detected I implemented it by checking the branch string for repeated keys (whether or not the branch is returned). The performance hit was pretty minimal -- about 1% for a moderately complex test case (220000 record table, 9 level tree with 3800 members). Joe Conway
2002-09-12 04:19:44 +04:00
/* initialize branch for this pass */
appendStringInfoString(&branchstr, branch);
appendStringInfo(&chk_branchstr, "%s%s%s", branch_delim, branch, branch_delim);
/* get the next sql result tuple */
spi_tuple = tuptable->vals[i];
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
/* get the current key (might be NULL) */
current_key = SPI_getvalue(spi_tuple, spi_tupdesc, 1);
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
/* get the parent key (might be NULL) */
current_key_parent = SPI_getvalue(spi_tuple, spi_tupdesc, 2);
/* get the current level */
sprintf(current_level, "%d", level);
/* check to see if this key is also an ancestor */
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
if (current_key)
{
appendStringInfo(&chk_current_key, "%s%s%s",
branch_delim, current_key, branch_delim);
if (strstr(chk_branchstr.data, chk_current_key.data))
ereport(ERROR,
(errcode(ERRCODE_INVALID_RECURSION),
errmsg("infinite recursion detected")));
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
}
/* OK, extend the branch */
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
if (current_key)
appendStringInfo(&branchstr, "%s%s", branch_delim, current_key);
current_branch = branchstr.data;
/* build a tuple */
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
values[0] = current_key;
values[1] = current_key_parent;
values[2] = current_level;
if (show_branch)
values[3] = current_branch;
if (show_serial)
{
sprintf(serial_str, "%d", (*serial)++);
if (show_branch)
values[4] = serial_str;
else
values[3] = serial_str;
}
tuple = BuildTupleFromCStrings(attinmeta, values);
/* store the tuple for later use */
tuplestore_puttuple(tupstore, tuple);
heap_freetuple(tuple);
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
/* recurse using current_key as the new start_with */
if (current_key)
build_tuplestore_recursively(key_fld,
parent_key_fld,
relname,
orderby_fld,
branch_delim,
current_key,
current_branch,
level + 1,
serial,
max_depth,
show_branch,
show_serial,
per_query_ctx,
attinmeta,
tupstore);
xpfree(current_key);
xpfree(current_key_parent);
> Now I'm testing connectby() in the /contrib/tablefunc in 7.3b1, which would > be a useful function for many users. However, I found the fact that > if connectby_tree has the following data, connectby() tries to search the end > of roots without knowing that the relations are infinite(-5-9-10-11-9-10-11-) . > I hope connectby() supports a check routine to find infinite relations. > > > CREATE TABLE connectby_tree(keyid int, parent_keyid int); > INSERT INTO connectby_tree VALUES(1,NULL); > INSERT INTO connectby_tree VALUES(2,1); > INSERT INTO connectby_tree VALUES(3,1); > INSERT INTO connectby_tree VALUES(4,2); > INSERT INTO connectby_tree VALUES(5,2); > INSERT INTO connectby_tree VALUES(6,4); > INSERT INTO connectby_tree VALUES(7,3); > INSERT INTO connectby_tree VALUES(8,6); > INSERT INTO connectby_tree VALUES(9,5); > > INSERT INTO connectby_tree VALUES(10,9); > INSERT INTO connectby_tree VALUES(11,10); > INSERT INTO connectby_tree VALUES(9,11); <-- infinite > The attached patch fixes the infinite recursion bug in contrib/tablefunc/tablefunc.c:connectby found by Masaru Sugawara. test=# SELECT * FROM connectby('connectby_tree', 'keyid', 'parent_keyid', '2', 4, '~') AS t(keyid int, parent_keyid int, level int, branch text); keyid | parent_keyid | level | branch -------+--------------+-------+------------- 2 | | 0 | 2 4 | 2 | 1 | 2~4 6 | 4 | 2 | 2~4~6 8 | 6 | 3 | 2~4~6~8 5 | 2 | 1 | 2~5 9 | 5 | 2 | 2~5~9 10 | 9 | 3 | 2~5~9~10 11 | 10 | 4 | 2~5~9~10~11 (8 rows) test=# SELECT * FROM connectby('connectby_tree', 'keyid', 'parent_keyid', '2', 5, '~') AS t(keyid int, parent_keyid int, level int, branch text); ERROR: infinite recursion detected I implemented it by checking the branch string for repeated keys (whether or not the branch is returned). The performance hit was pretty minimal -- about 1% for a moderately complex test case (220000 record table, 9 level tree with 3800 members). Joe Conway
2002-09-12 04:19:44 +04:00
/* reset branch for next pass */
resetStringInfo(&branchstr);
resetStringInfo(&chk_branchstr);
resetStringInfo(&chk_current_key);
}
xpfree(branchstr.data);
xpfree(chk_branchstr.data);
xpfree(chk_current_key.data);
}
}
/*
* Check expected (query runtime) tupdesc suitable for Connectby
*/
static void
validateConnectbyTupleDesc(TupleDesc td, bool show_branch, bool show_serial)
{
int serial_column = 0;
if (show_serial)
serial_column = 1;
2003-08-04 04:43:34 +04:00
/* are there the correct number of columns */
if (show_branch)
{
if (td->natts != (CONNECTBY_NCOLS + serial_column))
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
errdetail("Query-specified return tuple has " \
"wrong number of columns.")));
}
else
{
if (td->natts != CONNECTBY_NCOLS_NOBRANCH + serial_column)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
errdetail("Query-specified return tuple has " \
"wrong number of columns.")));
}
/* check that the types of the first two columns match */
if (TupleDescAttr(td, 0)->atttypid != TupleDescAttr(td, 1)->atttypid)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
errdetail("First two columns must be the same type.")));
/* check that the type of the third column is INT4 */
if (TupleDescAttr(td, 2)->atttypid != INT4OID)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
errdetail("Third column must be type %s.",
format_type_be(INT4OID))));
/* check that the type of the fourth column is TEXT if applicable */
if (show_branch && TupleDescAttr(td, 3)->atttypid != TEXTOID)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
errdetail("Fourth column must be type %s.",
format_type_be(TEXTOID))));
/* check that the type of the fifth column is INT4 */
if (show_branch && show_serial &&
TupleDescAttr(td, 4)->atttypid != INT4OID)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("query-specified return tuple not valid for Connectby: "
"fifth column must be type %s",
format_type_be(INT4OID))));
/* check that the type of the fourth column is INT4 */
if (!show_branch && show_serial &&
TupleDescAttr(td, 3)->atttypid != INT4OID)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("query-specified return tuple not valid for Connectby: "
"fourth column must be type %s",
format_type_be(INT4OID))));
2003-08-04 04:43:34 +04:00
/* OK, the tupdesc is valid for our purposes */
}
/*
* Check if spi sql tupdesc and return tupdesc are compatible
*/
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
static void
compatConnectbyTupleDescs(TupleDesc ret_tupdesc, TupleDesc sql_tupdesc)
{
Oid ret_atttypid;
Oid sql_atttypid;
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
int32 ret_atttypmod;
int32 sql_atttypmod;
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
/*
* Result must have at least 2 columns.
*/
if (sql_tupdesc->natts < 2)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
errmsg("invalid return type"),
errdetail("Query must return at least two columns.")));
/*
* These columns must match the result type indicated by the calling
* query.
*/
ret_atttypid = TupleDescAttr(ret_tupdesc, 0)->atttypid;
sql_atttypid = TupleDescAttr(sql_tupdesc, 0)->atttypid;
ret_atttypmod = TupleDescAttr(ret_tupdesc, 0)->atttypmod;
sql_atttypmod = TupleDescAttr(sql_tupdesc, 0)->atttypmod;
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
if (ret_atttypid != sql_atttypid ||
(ret_atttypmod >= 0 && ret_atttypmod != sql_atttypmod))
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
errdetail("SQL key field type %s does " \
"not match return key field type %s.",
format_type_with_typemod(ret_atttypid, ret_atttypmod),
format_type_with_typemod(sql_atttypid, sql_atttypmod))));
ret_atttypid = TupleDescAttr(ret_tupdesc, 1)->atttypid;
sql_atttypid = TupleDescAttr(sql_tupdesc, 1)->atttypid;
ret_atttypmod = TupleDescAttr(ret_tupdesc, 1)->atttypmod;
sql_atttypmod = TupleDescAttr(sql_tupdesc, 1)->atttypmod;
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
if (ret_atttypid != sql_atttypid ||
(ret_atttypmod >= 0 && ret_atttypmod != sql_atttypmod))
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
Handle unexpected query results, especially NULLs, safely in connectby(). connectby() didn't adequately check that the constructed SQL query returns what it's expected to; in fact, since commit 08c33c426bfebb32 it wasn't checking that at all. This could result in a null-pointer-dereference crash if the constructed query returns only one column instead of the expected two. Less excitingly, it could also result in surprising data conversion failures if the constructed query returned values that were not I/O-conversion-compatible with the types specified by the query calling connectby(). In all branches, insist that the query return at least two columns; this seems like a minimal sanity check that can't break any reasonable use-cases. In HEAD, insist that the constructed query return the types specified by the outer query, including checking for typmod incompatibility, which the code never did even before it got broken. This is to hide the fact that the implementation does a conversion to text and back; someday we might want to improve that. In back branches, leave that alone, since adding a type check in a minor release is more likely to break things than make people happy. Type inconsistencies will continue to work so long as the actual type and declared type are I/O representation compatible, and otherwise will fail the same way they used to. Also, in all branches, be on guard for NULL results from the constructed query, which formerly would cause null-pointer dereference crashes. We now print the row with the NULL but don't recurse down from it. In passing, get rid of the rather pointless idea that build_tuplestore_recursively() should return the same tuplestore that's passed to it. Michael Paquier, adjusted somewhat by me
2015-01-30 04:18:33 +03:00
errdetail("SQL parent key field type %s does " \
"not match return parent key field type %s.",
format_type_with_typemod(ret_atttypid, ret_atttypmod),
format_type_with_typemod(sql_atttypid, sql_atttypmod))));
/* OK, the two tupdescs are compatible for our purposes */
}
/*
* Check if two tupdescs match in type of attributes
*/
static bool
compatCrosstabTupleDescs(TupleDesc ret_tupdesc, TupleDesc sql_tupdesc)
{
int i;
Form_pg_attribute ret_attr;
Oid ret_atttypid;
Form_pg_attribute sql_attr;
Oid sql_atttypid;
if (ret_tupdesc->natts < 2 ||
sql_tupdesc->natts < 3)
return false;
/* check the rowid types match */
ret_atttypid = TupleDescAttr(ret_tupdesc, 0)->atttypid;
sql_atttypid = TupleDescAttr(sql_tupdesc, 0)->atttypid;
if (ret_atttypid != sql_atttypid)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("invalid return type"),
errdetail("SQL rowid datatype does not match " \
"return rowid datatype.")));
/*
* - attribute [1] of the sql tuple is the category; no need to check it -
* attribute [2] of the sql tuple should match attributes [1] to [natts]
* of the return tuple
*/
sql_attr = TupleDescAttr(sql_tupdesc, 2);
for (i = 1; i < ret_tupdesc->natts; i++)
{
ret_attr = TupleDescAttr(ret_tupdesc, i);
if (ret_attr->atttypid != sql_attr->atttypid)
return false;
}
/* OK, the two tupdescs are compatible for our purposes */
return true;
}