postgresql/src/backend/utils/adt/array_userfuncs.c

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/*-------------------------------------------------------------------------
*
* array_userfuncs.c
* Misc user-visible array support functions
*
* Copyright (c) 2003-2015, PostgreSQL Global Development Group
*
* IDENTIFICATION
2010-09-20 22:08:53 +02:00
* src/backend/utils/adt/array_userfuncs.c
*
*-------------------------------------------------------------------------
*/
#include "postgres.h"
#include "catalog/pg_type.h"
#include "utils/array.h"
#include "utils/builtins.h"
#include "utils/lsyscache.h"
#include "utils/typcache.h"
static Datum array_position_common(FunctionCallInfo fcinfo);
/*
* fetch_array_arg_replace_nulls
*
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
* Fetch an array-valued argument in expanded form; if it's null, construct an
* empty array value of the proper data type. Also cache basic element type
* information in fn_extra.
*
* Caution: if the input is a read/write pointer, this returns the input
* argument; so callers must be sure that their changes are "safe", that is
* they cannot leave the array in a corrupt state.
*/
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
static ExpandedArrayHeader *
fetch_array_arg_replace_nulls(FunctionCallInfo fcinfo, int argno)
{
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
ExpandedArrayHeader *eah;
Oid element_type;
ArrayMetaState *my_extra;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
/* If first time through, create datatype cache struct */
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
if (my_extra == NULL)
{
my_extra = (ArrayMetaState *)
MemoryContextAlloc(fcinfo->flinfo->fn_mcxt,
sizeof(ArrayMetaState));
my_extra->element_type = InvalidOid;
fcinfo->flinfo->fn_extra = my_extra;
}
/* Now collect the array value */
if (!PG_ARGISNULL(argno))
{
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
eah = PG_GETARG_EXPANDED_ARRAYX(argno, my_extra);
}
else
{
/* We have to look up the array type and element type */
Oid arr_typeid = get_fn_expr_argtype(fcinfo->flinfo, argno);
if (!OidIsValid(arr_typeid))
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("could not determine input data type")));
element_type = get_element_type(arr_typeid);
if (!OidIsValid(element_type))
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("input data type is not an array")));
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
eah = construct_empty_expanded_array(element_type,
CurrentMemoryContext,
my_extra);
}
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
return eah;
}
/*-----------------------------------------------------------------------------
* array_append :
* push an element onto the end of a one-dimensional array
*----------------------------------------------------------------------------
*/
Datum
array_append(PG_FUNCTION_ARGS)
{
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
ExpandedArrayHeader *eah;
Datum newelem;
bool isNull;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
Datum result;
int *dimv,
*lb;
int indx;
ArrayMetaState *my_extra;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
eah = fetch_array_arg_replace_nulls(fcinfo, 0);
isNull = PG_ARGISNULL(1);
if (isNull)
newelem = (Datum) 0;
else
newelem = PG_GETARG_DATUM(1);
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
if (eah->ndims == 1)
{
/* append newelem */
int ub;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
lb = eah->lbound;
dimv = eah->dims;
ub = dimv[0] + lb[0] - 1;
indx = ub + 1;
/* overflow? */
if (indx < ub)
ereport(ERROR,
(errcode(ERRCODE_NUMERIC_VALUE_OUT_OF_RANGE),
errmsg("integer out of range")));
}
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
else if (eah->ndims == 0)
indx = 1;
else
ereport(ERROR,
(errcode(ERRCODE_DATA_EXCEPTION),
errmsg("argument must be empty or one-dimensional array")));
/* Perform element insertion */
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
result = array_set_element(EOHPGetRWDatum(&eah->hdr),
1, &indx, newelem, isNull,
-1, my_extra->typlen, my_extra->typbyval, my_extra->typalign);
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
PG_RETURN_DATUM(result);
}
/*-----------------------------------------------------------------------------
* array_prepend :
* push an element onto the front of a one-dimensional array
*----------------------------------------------------------------------------
*/
Datum
array_prepend(PG_FUNCTION_ARGS)
{
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
ExpandedArrayHeader *eah;
Datum newelem;
bool isNull;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
Datum result;
2015-02-20 06:47:28 +01:00
int *lb;
int indx;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
int lb0;
ArrayMetaState *my_extra;
isNull = PG_ARGISNULL(0);
if (isNull)
newelem = (Datum) 0;
else
newelem = PG_GETARG_DATUM(0);
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
eah = fetch_array_arg_replace_nulls(fcinfo, 1);
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
if (eah->ndims == 1)
2003-06-25 01:14:49 +02:00
{
/* prepend newelem */
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
lb = eah->lbound;
indx = lb[0] - 1;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
lb0 = lb[0];
/* overflow? */
if (indx > lb[0])
ereport(ERROR,
(errcode(ERRCODE_NUMERIC_VALUE_OUT_OF_RANGE),
errmsg("integer out of range")));
2003-06-25 01:14:49 +02:00
}
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
else if (eah->ndims == 0)
{
indx = 1;
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
lb0 = 1;
}
2003-06-25 01:14:49 +02:00
else
ereport(ERROR,
(errcode(ERRCODE_DATA_EXCEPTION),
2005-10-15 04:49:52 +02:00
errmsg("argument must be empty or one-dimensional array")));
/* Perform element insertion */
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
2003-06-25 23:30:34 +02:00
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
result = array_set_element(EOHPGetRWDatum(&eah->hdr),
1, &indx, newelem, isNull,
-1, my_extra->typlen, my_extra->typbyval, my_extra->typalign);
/* Readjust result's LB to match the input's, as expected for prepend */
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
Assert(result == EOHPGetRWDatum(&eah->hdr));
if (eah->ndims == 1)
{
/* This is ok whether we've deconstructed or not */
eah->lbound[0] = lb0;
}
Support "expanded" objects, particularly arrays, for better performance. This patch introduces the ability for complex datatypes to have an in-memory representation that is different from their on-disk format. On-disk formats are typically optimized for minimal size, and in any case they can't contain pointers, so they are often not well-suited for computation. Now a datatype can invent an "expanded" in-memory format that is better suited for its operations, and then pass that around among the C functions that operate on the datatype. There are also provisions (rudimentary as yet) to allow an expanded object to be modified in-place under suitable conditions, so that operations like assignment to an element of an array need not involve copying the entire array. The initial application for this feature is arrays, but it is not hard to foresee using it for other container types like JSON, XML and hstore. I have hopes that it will be useful to PostGIS as well. In this initial implementation, a few heuristics have been hard-wired into plpgsql to improve performance for arrays that are stored in plpgsql variables. We would like to generalize those hacks so that other datatypes can obtain similar improvements, but figuring out some appropriate APIs is left as a task for future work. (The heuristics themselves are probably not optimal yet, either, as they sometimes force expansion of arrays that would be better left alone.) Preliminary performance testing shows impressive speed gains for plpgsql functions that do element-by-element access or update of large arrays. There are other cases that get a little slower, as a result of added array format conversions; but we can hope to improve anything that's annoyingly bad. In any case most applications should see a net win. Tom Lane, reviewed by Andres Freund
2015-05-14 18:08:40 +02:00
PG_RETURN_DATUM(result);
}
/*-----------------------------------------------------------------------------
* array_cat :
* concatenate two nD arrays to form an nD array, or
* push an (n-1)D array onto the end of an nD array
*----------------------------------------------------------------------------
*/
Datum
array_cat(PG_FUNCTION_ARGS)
{
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ArrayType *v1,
*v2;
ArrayType *result;
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int *dims,
*lbs,
ndims,
nitems,
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ndatabytes,
nbytes;
int *dims1,
*lbs1,
ndims1,
nitems1,
2003-08-04 02:43:34 +02:00
ndatabytes1;
int *dims2,
*lbs2,
ndims2,
nitems2,
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ndatabytes2;
int i;
2003-08-04 02:43:34 +02:00
char *dat1,
*dat2;
bits8 *bitmap1,
*bitmap2;
Oid element_type;
Oid element_type1;
Oid element_type2;
int32 dataoffset;
/* Concatenating a null array is a no-op, just return the other input */
if (PG_ARGISNULL(0))
{
if (PG_ARGISNULL(1))
PG_RETURN_NULL();
result = PG_GETARG_ARRAYTYPE_P(1);
PG_RETURN_ARRAYTYPE_P(result);
}
if (PG_ARGISNULL(1))
{
result = PG_GETARG_ARRAYTYPE_P(0);
PG_RETURN_ARRAYTYPE_P(result);
}
v1 = PG_GETARG_ARRAYTYPE_P(0);
v2 = PG_GETARG_ARRAYTYPE_P(1);
element_type1 = ARR_ELEMTYPE(v1);
element_type2 = ARR_ELEMTYPE(v2);
/* Check we have matching element types */
if (element_type1 != element_type2)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("cannot concatenate incompatible arrays"),
errdetail("Arrays with element types %s and %s are not "
"compatible for concatenation.",
format_type_be(element_type1),
format_type_be(element_type2))));
/* OK, use it */
element_type = element_type1;
/*----------
* We must have one of the following combinations of inputs:
* 1) one empty array, and one non-empty array
* 2) both arrays empty
* 3) two arrays with ndims1 == ndims2
* 4) ndims1 == ndims2 - 1
* 5) ndims1 == ndims2 + 1
*----------
*/
ndims1 = ARR_NDIM(v1);
ndims2 = ARR_NDIM(v2);
/*
2005-10-15 04:49:52 +02:00
* short circuit - if one input array is empty, and the other is not, we
* return the non-empty one as the result
*
* if both are empty, return the first one
*/
if (ndims1 == 0 && ndims2 > 0)
PG_RETURN_ARRAYTYPE_P(v2);
if (ndims2 == 0)
PG_RETURN_ARRAYTYPE_P(v1);
/* the rest fall under rule 3, 4, or 5 */
if (ndims1 != ndims2 &&
ndims1 != ndims2 - 1 &&
ndims1 != ndims2 + 1)
ereport(ERROR,
(errcode(ERRCODE_ARRAY_SUBSCRIPT_ERROR),
errmsg("cannot concatenate incompatible arrays"),
errdetail("Arrays of %d and %d dimensions are not "
2003-08-04 02:43:34 +02:00
"compatible for concatenation.",
ndims1, ndims2)));
/* get argument array details */
lbs1 = ARR_LBOUND(v1);
lbs2 = ARR_LBOUND(v2);
dims1 = ARR_DIMS(v1);
dims2 = ARR_DIMS(v2);
dat1 = ARR_DATA_PTR(v1);
dat2 = ARR_DATA_PTR(v2);
bitmap1 = ARR_NULLBITMAP(v1);
bitmap2 = ARR_NULLBITMAP(v2);
nitems1 = ArrayGetNItems(ndims1, dims1);
nitems2 = ArrayGetNItems(ndims2, dims2);
ndatabytes1 = ARR_SIZE(v1) - ARR_DATA_OFFSET(v1);
ndatabytes2 = ARR_SIZE(v2) - ARR_DATA_OFFSET(v2);
if (ndims1 == ndims2)
{
/*
* resulting array is made up of the elements (possibly arrays
* themselves) of the input argument arrays
*/
ndims = ndims1;
dims = (int *) palloc(ndims * sizeof(int));
lbs = (int *) palloc(ndims * sizeof(int));
dims[0] = dims1[0] + dims2[0];
lbs[0] = lbs1[0];
for (i = 1; i < ndims; i++)
{
if (dims1[i] != dims2[i] || lbs1[i] != lbs2[i])
ereport(ERROR,
(errcode(ERRCODE_ARRAY_SUBSCRIPT_ERROR),
errmsg("cannot concatenate incompatible arrays"),
2005-10-15 04:49:52 +02:00
errdetail("Arrays with differing element dimensions are "
"not compatible for concatenation.")));
dims[i] = dims1[i];
lbs[i] = lbs1[i];
}
}
else if (ndims1 == ndims2 - 1)
{
/*
2005-10-15 04:49:52 +02:00
* resulting array has the second argument as the outer array, with
* the first argument inserted at the front of the outer dimension
*/
ndims = ndims2;
dims = (int *) palloc(ndims * sizeof(int));
lbs = (int *) palloc(ndims * sizeof(int));
memcpy(dims, dims2, ndims * sizeof(int));
memcpy(lbs, lbs2, ndims * sizeof(int));
/* increment number of elements in outer array */
dims[0] += 1;
/* make sure the added element matches our existing elements */
for (i = 0; i < ndims1; i++)
{
if (dims1[i] != dims[i + 1] || lbs1[i] != lbs[i + 1])
ereport(ERROR,
(errcode(ERRCODE_ARRAY_SUBSCRIPT_ERROR),
errmsg("cannot concatenate incompatible arrays"),
2005-10-15 04:49:52 +02:00
errdetail("Arrays with differing dimensions are not "
"compatible for concatenation.")));
}
}
2003-08-04 02:43:34 +02:00
else
{
/*
* (ndims1 == ndims2 + 1)
*
2005-10-15 04:49:52 +02:00
* resulting array has the first argument as the outer array, with the
* second argument appended to the end of the outer dimension
*/
ndims = ndims1;
dims = (int *) palloc(ndims * sizeof(int));
lbs = (int *) palloc(ndims * sizeof(int));
memcpy(dims, dims1, ndims * sizeof(int));
memcpy(lbs, lbs1, ndims * sizeof(int));
/* increment number of elements in outer array */
dims[0] += 1;
/* make sure the added element matches our existing elements */
for (i = 0; i < ndims2; i++)
{
if (dims2[i] != dims[i + 1] || lbs2[i] != lbs[i + 1])
ereport(ERROR,
(errcode(ERRCODE_ARRAY_SUBSCRIPT_ERROR),
errmsg("cannot concatenate incompatible arrays"),
2005-10-15 04:49:52 +02:00
errdetail("Arrays with differing dimensions are not "
"compatible for concatenation.")));
}
}
/* Do this mainly for overflow checking */
nitems = ArrayGetNItems(ndims, dims);
/* build the result array */
ndatabytes = ndatabytes1 + ndatabytes2;
if (ARR_HASNULL(v1) || ARR_HASNULL(v2))
{
dataoffset = ARR_OVERHEAD_WITHNULLS(ndims, nitems);
nbytes = ndatabytes + dataoffset;
}
else
{
dataoffset = 0; /* marker for no null bitmap */
nbytes = ndatabytes + ARR_OVERHEAD_NONULLS(ndims);
}
result = (ArrayType *) palloc0(nbytes);
SET_VARSIZE(result, nbytes);
result->ndim = ndims;
result->dataoffset = dataoffset;
result->elemtype = element_type;
memcpy(ARR_DIMS(result), dims, ndims * sizeof(int));
memcpy(ARR_LBOUND(result), lbs, ndims * sizeof(int));
/* data area is arg1 then arg2 */
memcpy(ARR_DATA_PTR(result), dat1, ndatabytes1);
memcpy(ARR_DATA_PTR(result) + ndatabytes1, dat2, ndatabytes2);
/* handle the null bitmap if needed */
if (ARR_HASNULL(result))
{
array_bitmap_copy(ARR_NULLBITMAP(result), 0,
bitmap1, 0,
nitems1);
array_bitmap_copy(ARR_NULLBITMAP(result), nitems1,
bitmap2, 0,
nitems2);
}
PG_RETURN_ARRAYTYPE_P(result);
}
/*
* used by text_to_array() in varlena.c
*/
ArrayType *
create_singleton_array(FunctionCallInfo fcinfo,
Oid element_type,
Datum element,
bool isNull,
int ndims)
{
2003-08-04 02:43:34 +02:00
Datum dvalues[1];
bool nulls[1];
2003-08-04 02:43:34 +02:00
int16 typlen;
bool typbyval;
char typalign;
int dims[MAXDIM];
int lbs[MAXDIM];
int i;
ArrayMetaState *my_extra;
if (ndims < 1)
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("invalid number of dimensions: %d", ndims)));
if (ndims > MAXDIM)
ereport(ERROR,
(errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
errmsg("number of array dimensions (%d) exceeds the maximum allowed (%d)",
ndims, MAXDIM)));
dvalues[0] = element;
nulls[0] = isNull;
for (i = 0; i < ndims; i++)
{
dims[i] = 1;
lbs[i] = 1;
}
/*
2005-10-15 04:49:52 +02:00
* We arrange to look up info about element type only once per series of
* calls, assuming the element type doesn't change underneath us.
*/
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
if (my_extra == NULL)
{
fcinfo->flinfo->fn_extra = MemoryContextAlloc(fcinfo->flinfo->fn_mcxt,
2005-10-15 04:49:52 +02:00
sizeof(ArrayMetaState));
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
my_extra->element_type = ~element_type;
}
if (my_extra->element_type != element_type)
{
/* Get info about element type */
get_typlenbyvalalign(element_type,
&my_extra->typlen,
&my_extra->typbyval,
&my_extra->typalign);
my_extra->element_type = element_type;
}
typlen = my_extra->typlen;
typbyval = my_extra->typbyval;
typalign = my_extra->typalign;
return construct_md_array(dvalues, nulls, ndims, dims, lbs, element_type,
typlen, typbyval, typalign);
}
/*
* ARRAY_AGG(anynonarray) aggregate function
*/
Datum
array_agg_transfn(PG_FUNCTION_ARGS)
{
Oid arg1_typeid = get_fn_expr_argtype(fcinfo->flinfo, 1);
MemoryContext aggcontext;
ArrayBuildState *state;
Datum elem;
if (arg1_typeid == InvalidOid)
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("could not determine input data type")));
/*
* Note: we do not need a run-time check about whether arg1_typeid is a
* valid array element type, because the parser would have verified that
* while resolving the input/result types of this polymorphic aggregate.
*/
if (!AggCheckCallContext(fcinfo, &aggcontext))
{
/* cannot be called directly because of internal-type argument */
elog(ERROR, "array_agg_transfn called in non-aggregate context");
}
if (PG_ARGISNULL(0))
state = initArrayResult(arg1_typeid, aggcontext, false);
else
state = (ArrayBuildState *) PG_GETARG_POINTER(0);
elem = PG_ARGISNULL(1) ? (Datum) 0 : PG_GETARG_DATUM(1);
state = accumArrayResult(state,
elem,
PG_ARGISNULL(1),
arg1_typeid,
aggcontext);
/*
* The transition type for array_agg() is declared to be "internal", which
* is a pass-by-value type the same size as a pointer. So we can safely
* pass the ArrayBuildState pointer through nodeAgg.c's machinations.
*/
PG_RETURN_POINTER(state);
}
Datum
array_agg_finalfn(PG_FUNCTION_ARGS)
{
Datum result;
ArrayBuildState *state;
int dims[1];
int lbs[1];
/* cannot be called directly because of internal-type argument */
Assert(AggCheckCallContext(fcinfo, NULL));
state = PG_ARGISNULL(0) ? NULL : (ArrayBuildState *) PG_GETARG_POINTER(0);
if (state == NULL)
PG_RETURN_NULL(); /* returns null iff no input values */
dims[0] = state->nelems;
lbs[0] = 1;
/*
* Make the result. We cannot release the ArrayBuildState because
2010-02-26 03:01:40 +01:00
* sometimes aggregate final functions are re-executed. Rather, it is
* nodeAgg.c's responsibility to reset the aggcontext when it's safe to do
* so.
*/
result = makeMdArrayResult(state, 1, dims, lbs,
CurrentMemoryContext,
false);
PG_RETURN_DATUM(result);
}
/*
* ARRAY_AGG(anyarray) aggregate function
*/
Datum
array_agg_array_transfn(PG_FUNCTION_ARGS)
{
Oid arg1_typeid = get_fn_expr_argtype(fcinfo->flinfo, 1);
MemoryContext aggcontext;
ArrayBuildStateArr *state;
if (arg1_typeid == InvalidOid)
ereport(ERROR,
(errcode(ERRCODE_INVALID_PARAMETER_VALUE),
errmsg("could not determine input data type")));
/*
* Note: we do not need a run-time check about whether arg1_typeid is a
* valid array type, because the parser would have verified that while
* resolving the input/result types of this polymorphic aggregate.
*/
if (!AggCheckCallContext(fcinfo, &aggcontext))
{
/* cannot be called directly because of internal-type argument */
elog(ERROR, "array_agg_array_transfn called in non-aggregate context");
}
if (PG_ARGISNULL(0))
state = initArrayResultArr(arg1_typeid, InvalidOid, aggcontext, false);
else
state = (ArrayBuildStateArr *) PG_GETARG_POINTER(0);
state = accumArrayResultArr(state,
PG_GETARG_DATUM(1),
PG_ARGISNULL(1),
arg1_typeid,
aggcontext);
/*
* The transition type for array_agg() is declared to be "internal", which
* is a pass-by-value type the same size as a pointer. So we can safely
* pass the ArrayBuildStateArr pointer through nodeAgg.c's machinations.
*/
PG_RETURN_POINTER(state);
}
Datum
array_agg_array_finalfn(PG_FUNCTION_ARGS)
{
Datum result;
ArrayBuildStateArr *state;
/* cannot be called directly because of internal-type argument */
Assert(AggCheckCallContext(fcinfo, NULL));
state = PG_ARGISNULL(0) ? NULL : (ArrayBuildStateArr *) PG_GETARG_POINTER(0);
if (state == NULL)
PG_RETURN_NULL(); /* returns null iff no input values */
/*
* Make the result. We cannot release the ArrayBuildStateArr because
* sometimes aggregate final functions are re-executed. Rather, it is
* nodeAgg.c's responsibility to reset the aggcontext when it's safe to do
* so.
*/
result = makeArrayResultArr(state, CurrentMemoryContext, false);
PG_RETURN_DATUM(result);
}
/*-----------------------------------------------------------------------------
* array_position, array_position_start :
* return the offset of a value in an array.
*
* IS NOT DISTINCT FROM semantics are used for comparisons. Return NULL when
* the value is not found.
*-----------------------------------------------------------------------------
*/
Datum
array_position(PG_FUNCTION_ARGS)
{
return array_position_common(fcinfo);
}
Datum
array_position_start(PG_FUNCTION_ARGS)
{
return array_position_common(fcinfo);
}
/*
* array_position_common
2015-05-24 03:35:49 +02:00
* Common code for array_position and array_position_start
*
* These are separate wrappers for the sake of opr_sanity regression test.
* They are not strict so we have to test for null inputs explicitly.
*/
static Datum
array_position_common(FunctionCallInfo fcinfo)
{
ArrayType *array;
Oid collation = PG_GET_COLLATION();
Oid element_type;
Datum searched_element,
value;
bool isnull;
int position,
position_min;
bool found = false;
TypeCacheEntry *typentry;
ArrayMetaState *my_extra;
bool null_search;
ArrayIterator array_iterator;
if (PG_ARGISNULL(0))
PG_RETURN_NULL();
array = PG_GETARG_ARRAYTYPE_P(0);
element_type = ARR_ELEMTYPE(array);
/*
* We refuse to search for elements in multi-dimensional arrays, since we
* have no good way to report the element's location in the array.
*/
if (ARR_NDIM(array) > 1)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("searching for elements in multidimensional arrays is not supported")));
if (PG_ARGISNULL(1))
{
/* fast return when the array doesn't have nulls */
if (!array_contains_nulls(array))
PG_RETURN_NULL();
searched_element = (Datum) 0;
null_search = true;
}
else
{
searched_element = PG_GETARG_DATUM(1);
null_search = false;
}
position = (ARR_LBOUND(array))[0] - 1;
/* figure out where to start */
if (PG_NARGS() == 3)
{
if (PG_ARGISNULL(2))
ereport(ERROR,
(errcode(ERRCODE_NULL_VALUE_NOT_ALLOWED),
errmsg("initial position should not be NULL")));
position_min = PG_GETARG_INT32(2);
}
else
position_min = (ARR_LBOUND(array))[0];
/*
* We arrange to look up type info for array_create_iterator only once per
2015-05-24 03:35:49 +02:00
* series of calls, assuming the element type doesn't change underneath
* us.
*/
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
if (my_extra == NULL)
{
fcinfo->flinfo->fn_extra = MemoryContextAlloc(fcinfo->flinfo->fn_mcxt,
sizeof(ArrayMetaState));
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
my_extra->element_type = ~element_type;
}
if (my_extra->element_type != element_type)
{
get_typlenbyvalalign(element_type,
&my_extra->typlen,
&my_extra->typbyval,
&my_extra->typalign);
typentry = lookup_type_cache(element_type, TYPECACHE_EQ_OPR_FINFO);
if (!OidIsValid(typentry->eq_opr_finfo.fn_oid))
ereport(ERROR,
(errcode(ERRCODE_UNDEFINED_FUNCTION),
2015-05-24 03:35:49 +02:00
errmsg("could not identify an equality operator for type %s",
format_type_be(element_type))));
my_extra->element_type = element_type;
fmgr_info(typentry->eq_opr_finfo.fn_oid, &my_extra->proc);
}
/* Examine each array element until we find a match. */
array_iterator = array_create_iterator(array, 0, my_extra);
while (array_iterate(array_iterator, &value, &isnull))
{
position++;
/* skip initial elements if caller requested so */
if (position < position_min)
continue;
/*
* Can't look at the array element's value if it's null; but if we
* search for null, we have a hit and are done.
*/
if (isnull || null_search)
{
if (isnull && null_search)
{
found = true;
break;
}
else
continue;
}
/* not nulls, so run the operator */
if (DatumGetBool(FunctionCall2Coll(&my_extra->proc, collation,
searched_element, value)))
{
found = true;
break;
}
}
array_free_iterator(array_iterator);
/* Avoid leaking memory when handed toasted input */
PG_FREE_IF_COPY(array, 0);
if (!found)
PG_RETURN_NULL();
PG_RETURN_INT32(position);
}
/*-----------------------------------------------------------------------------
* array_positions :
* return an array of positions of a value in an array.
*
* IS NOT DISTINCT FROM semantics are used for comparisons. Returns NULL when
* the input array is NULL. When the value is not found in the array, returns
* an empty array.
*
* This is not strict so we have to test for null inputs explicitly.
*-----------------------------------------------------------------------------
*/
Datum
array_positions(PG_FUNCTION_ARGS)
{
ArrayType *array;
Oid collation = PG_GET_COLLATION();
Oid element_type;
Datum searched_element,
value;
bool isnull;
int position;
TypeCacheEntry *typentry;
ArrayMetaState *my_extra;
bool null_search;
ArrayIterator array_iterator;
ArrayBuildState *astate = NULL;
if (PG_ARGISNULL(0))
PG_RETURN_NULL();
array = PG_GETARG_ARRAYTYPE_P(0);
element_type = ARR_ELEMTYPE(array);
position = (ARR_LBOUND(array))[0] - 1;
/*
* We refuse to search for elements in multi-dimensional arrays, since we
* have no good way to report the element's location in the array.
*/
if (ARR_NDIM(array) > 1)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("searching for elements in multidimensional arrays is not supported")));
astate = initArrayResult(INT4OID, CurrentMemoryContext, false);
if (PG_ARGISNULL(1))
{
/* fast return when the array doesn't have nulls */
if (!array_contains_nulls(array))
PG_RETURN_DATUM(makeArrayResult(astate, CurrentMemoryContext));
searched_element = (Datum) 0;
null_search = true;
}
else
{
searched_element = PG_GETARG_DATUM(1);
null_search = false;
}
/*
* We arrange to look up type info for array_create_iterator only once per
2015-05-24 03:35:49 +02:00
* series of calls, assuming the element type doesn't change underneath
* us.
*/
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
if (my_extra == NULL)
{
fcinfo->flinfo->fn_extra = MemoryContextAlloc(fcinfo->flinfo->fn_mcxt,
sizeof(ArrayMetaState));
my_extra = (ArrayMetaState *) fcinfo->flinfo->fn_extra;
my_extra->element_type = ~element_type;
}
if (my_extra->element_type != element_type)
{
get_typlenbyvalalign(element_type,
&my_extra->typlen,
&my_extra->typbyval,
&my_extra->typalign);
typentry = lookup_type_cache(element_type, TYPECACHE_EQ_OPR_FINFO);
if (!OidIsValid(typentry->eq_opr_finfo.fn_oid))
ereport(ERROR,
(errcode(ERRCODE_UNDEFINED_FUNCTION),
2015-05-24 03:35:49 +02:00
errmsg("could not identify an equality operator for type %s",
format_type_be(element_type))));
my_extra->element_type = element_type;
fmgr_info(typentry->eq_opr_finfo.fn_oid, &my_extra->proc);
}
/*
2015-05-24 03:35:49 +02:00
* Accumulate each array position iff the element matches the given
* element.
*/
array_iterator = array_create_iterator(array, 0, my_extra);
while (array_iterate(array_iterator, &value, &isnull))
{
position += 1;
/*
* Can't look at the array element's value if it's null; but if we
* search for null, we have a hit.
*/
if (isnull || null_search)
{
if (isnull && null_search)
astate =
accumArrayResult(astate, Int32GetDatum(position), false,
INT4OID, CurrentMemoryContext);
continue;
}
/* not nulls, so run the operator */
if (DatumGetBool(FunctionCall2Coll(&my_extra->proc, collation,
searched_element, value)))
astate =
accumArrayResult(astate, Int32GetDatum(position), false,
INT4OID, CurrentMemoryContext);
}
array_free_iterator(array_iterator);
/* Avoid leaking memory when handed toasted input */
PG_FREE_IF_COPY(array, 0);
PG_RETURN_DATUM(makeArrayResult(astate, CurrentMemoryContext));
}