假设我们要为自定义的数据类型 point 创建一个空间索引,可以按照以下步骤进行:
首先,创建一个用户定义的数据类型 point:
CREATE TYPE point AS (
x DOUBLE PRECISION,
y DOUBLE PRECISION
);
然后,创建一个表,其中包含 point 类型的列:
CREATE TABLE spatial_table (
id SERIAL PRIMARY KEY,
location point
);
接下来,我们将定义用户定义的索引接口扩展。在这个例子中,我们将创建一个 R 树(R-tree)索引,用于支持 point 类型的空间查询。
#include "postgres.h"
#include "fmgr.h"
#include "access/spgist.h"
PG_MODULE_MAGIC;
PG_FUNCTION_INFO_V1(spgist_point_config);
Datum
spgist_point_config(PG_FUNCTION_ARGS)
{
SpGistOpClass *opclass = (SpGistOpClass *) PG_GETARG_POINTER(0);
spgConfigOut *cfg = (spgConfigOut *) PG_GETARG_POINTER(1);
/* Set config parameters for the index */
cfg->prefixType = FLOAT8OID; /* Type of bounding box prefix */
cfg->labelType = VOIDOID; /* Type of node label */
cfg->leafType = pointType; /* Type of leaf values */
cfg->canReturnData = true; /* Index can return stored data */
PG_RETURN_VOID();
}
PG_FUNCTION_INFO_V1(spgist_point_choose);
Datum
spgist_point_choose(PG_FUNCTION_ARGS)
{
spgChooseIn *in = (spgChooseIn *) PG_GETARG_POINTER(0);
spgChooseOut *out = (spgChooseOut *) PG_GETARG_POINTER(1);
/* Choose the index function to call based on the strategy */
switch (in->strategy)
{
case SPGIST_SEARCH_DISTANCE:
out->resultType = spgMatchNode; /* Use distance match function */
out->result.matchNode.nodeN = 1; /* Use the only one node */
out->result.matchNode.levelAdd = 0;
PG_RETURN_VOID();
default:
elog(ERROR, "unsupported strategy number: %d", in->strategy);
}
PG_RETURN_VOID();
}
PG_FUNCTION_INFO_V1(spgist_point_picksplit);
Datum
spgist_point_picksplit(PG_FUNCTION_ARGS)
{
spgPickSplitIn *in = (spgPickSplitIn *) PG_GETARG_POINTER(0);
spgPickSplitOut *out = (spgPickSplitOut *) PG_GETARG_POINTER(1);
/* Split the bounding box in half */
out->hasPrefix = false;
out->nNodes = 2;
out->nodeLabels = (Datum *) palloc(sizeof(Datum) * 2);
out->nodeLabels[0] = PointerGetDatum(in->datums[0]);
out->nodeLabels[1] = PointerGetDatum(in->datums[1]);
PG_RETURN_VOID();
}
PG_FUNCTION_INFO_V1(spgist_point_inner_consistent);
Datum
spgist_point_inner_consistent(PG_FUNCTION_ARGS)
{
spgInnerConsistentIn *in = (spgInnerConsistentIn *) PG_GETARG_POINTER(0);
spgInnerConsistentOut *out = (spgInnerConsistentOut *) PG_GETARG_POINTER(1);
/* Check consistency between query and bounding box */
if (DatumGetBool(DirectFunctionCall2(point_inside_circle,
in->query,
in->prefix)))
out->nodeNumbers = lcons_oid(0, NIL);
else
out->nodeNumbers = lcons_oid(1, NIL);
PG_RETURN_VOID();
}
在上述 C 代码中,我们定义了四个函数,分别用于配置索引、选择节点、分裂节点以及判断内部节点的一致性。这些函数将被 PostgreSQL 用于创建和维护 R 树索引。
最后,我们将使用 SQL 语句将用户定义的索引方法与表关联起来:
-- 创建用户定义的索引方法
CREATE FUNCTION point_in_circle(point, point)
RETURNS boolean
AS 'MODULE_PATHNAME'
LANGUAGE C IMMUTABLE STRICT;
-- 创建用户定义的索引
CREATE INDEX spatial_index
ON spatial_table USING spgist (location point_in_circle);
在上述 SQL 语句中,我们创建了一个名为 point_in_circle 的用户定义函数,用于检查一个点是否在另一个点的半径范围内。然后,我们使用 CREATE INDEX 语句创建了一个使用用户定义索引方法 spgist 的索引。
需要注意的是,用户定义索引方法是一个高级功能,需要深入理解 PostgreSQL 的内部工作原理和 C 编程。这个示例只是演示了如何创建一个简单的用户定义索引方法,并且实际应用中可能涉及到更复杂的逻辑和数据结构。
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