改进连接查询 postgresql/postgis
Improving join query postgresql/postgis
我相信 postgresql 可以更快地处理我的查询,但每次尝试修改它都会使它变慢!
我有 2 个 table:
- 统计数据(id,field1,[...],field10)
- 几何(id, geom)
我在 :
上创建了索引
- statistics.id
- geometry.id
- 几何 (st_x(st_centroid(st_transform(geom, 2154))), st_y(st_centroid(st_transform( geom, 2154))))
这里是查询
EXPLAIN ANALYZE SELECT
statistics.*,
st_x(st_centroid(st_transform(geometry.geom, 2154))) AS x,
st_y(st_centroid(st_transform(geometry.geom, 2154))) AS y
FROM statistics
JOIN geometry ON statistics.id = geometry.id
WHERE statistics.id not like '97%';
这是结果
Hash Join (cost=1294.66..5158.10 rows=36593 width=342) (actual time=20.788..1085.257 rows=36552 loops=1)
Hash Cond: (geometry.id = (statistics.id)::text)
-> Seq Scan on geometry (cost=0.00..2445.46 rows=36593 width=279) (actual time=0.010..25.271 rows=36597 loops=1)
Filter: (id !~~ '97%'::text)
-> Hash (cost=835.96..835.96 rows=36696 width=69) (actual time=19.892..19.892 rows=36696 loops=1)
Buckets: 4096 Batches: 1 Memory Usage: 3780kB
-> Seq Scan on statistics (cost=0.00..835.96 rows=36696 width=69) (actual time=0.005..6.871 rows=36696 loops=1)
Planning time: 0.401 ms
Execution time: 1088.612 ms
最昂贵的操作是哈希连接。您将如何重组那里的查询以获得更好的结果?
下面是 tables
的架构
CREATE TABLE "statistics" (
"REG" integer,
"DEP" character varying(10),
"COM" character varying(50),
"D03" integer,
"D04" integer,
"D05" integer,
"D06" integer,
"D07" integer,
"D08" integer,
"D09" integer,
"D10" integer,
"D11" integer,
"D12" integer,
"D13" integer,
"id" text
);
CREATE TABLE geometry (
id text NOT NULL,
id_geo numeric(10,0),
cm_code character varying(3),
name character varying(50),
status character varying(20),
lat integer,
long integer,
lat_centroid integer,
long_centroid integer,
z_ smallint,
area numeric(10,0),
population double precision,
code_ct character varying(2),
code_r character varying(1),
code_dp character varying(2),
name_dp character varying(30),
code_rg character varying(2),
geom geometry(MultiPolygon,4326),
x real,
y real
);
每个 table
中大约有 40 000 行
索引已创建如下
CREATE INDEX statistics_id_idx ON public.statistics USING btree (id COLLATE pg_catalog."default");
CREATE INDEX geometry_geom_idx ON public.geometry USING gist (geom);
CREATE INDEX geometry_id_gin2 ON public.geometry USING gin (id COLLATE pg_catalog."default" gin_trgm_ops);
为了获得信息,我在 geometry_id 和 statistics_id 上尝试了不同的索引(btree 和 gin)。
我没有发现您的查询有任何问题。
要检查的事情
(geometry.id = (statistics.id)::text)
两个字段的数据类型相同吗?
WHERE statistics.id not like '97%';
。 LIKE '%me'
永远不会使用索引,但 LIKE 'me%'
可能会使用索引。 Why doesnt use index?
st_x(st_centroid(st_transform(geometry.geom, 2154))) AS x,
是一个函数,需要时间。需要转换坐标,然后提取一个值。如果计算该值并将其存储在一个字段中,你会更好。
- 您的几何索引对此查询没有任何影响,因为您正在计算一个值而不是搜索某些内容。
- 如果您想要执行索引不正确的地理搜索。不过我们可以稍后再谈
值得尝试的事情
首先是where like
.
SELECT *
FROM statistics
WHERE statistics.id not like '97%';
然后 join
SELECT statistics.*,
geometry.geom
FROM statistics
JOIN geometry ON statistics.id = geometry.id
然后加入 + st_x
SELECT statistics.*,
st_x(st_centroid(st_transform(geometry.geom, 2154))) AS x,
st_y(st_centroid(st_transform(geometry.geom, 2154))) AS y
FROM statistics
JOIN geometry ON statistics.id = geometry.id
然后在 geometry
table
中创建预计算 x, y
列
SELECT statistics.*,
geometry.x,
geometry.y,
FROM statistics
JOIN geometry ON statistics.id = geometry.id
然后加入+st_x+where like
然后加入+geometry.xy+where like
比较每个步骤之间的时间以检查哪个步骤花费的时间最多。
我相信 postgresql 可以更快地处理我的查询,但每次尝试修改它都会使它变慢!
我有 2 个 table:
- 统计数据(id,field1,[...],field10)
- 几何(id, geom)
我在 :
上创建了索引- statistics.id
- geometry.id
- 几何 (st_x(st_centroid(st_transform(geom, 2154))), st_y(st_centroid(st_transform( geom, 2154))))
这里是查询
EXPLAIN ANALYZE SELECT
statistics.*,
st_x(st_centroid(st_transform(geometry.geom, 2154))) AS x,
st_y(st_centroid(st_transform(geometry.geom, 2154))) AS y
FROM statistics
JOIN geometry ON statistics.id = geometry.id
WHERE statistics.id not like '97%';
这是结果
Hash Join (cost=1294.66..5158.10 rows=36593 width=342) (actual time=20.788..1085.257 rows=36552 loops=1)
Hash Cond: (geometry.id = (statistics.id)::text)
-> Seq Scan on geometry (cost=0.00..2445.46 rows=36593 width=279) (actual time=0.010..25.271 rows=36597 loops=1)
Filter: (id !~~ '97%'::text)
-> Hash (cost=835.96..835.96 rows=36696 width=69) (actual time=19.892..19.892 rows=36696 loops=1)
Buckets: 4096 Batches: 1 Memory Usage: 3780kB
-> Seq Scan on statistics (cost=0.00..835.96 rows=36696 width=69) (actual time=0.005..6.871 rows=36696 loops=1)
Planning time: 0.401 ms
Execution time: 1088.612 ms
最昂贵的操作是哈希连接。您将如何重组那里的查询以获得更好的结果?
下面是 tables
的架构CREATE TABLE "statistics" (
"REG" integer,
"DEP" character varying(10),
"COM" character varying(50),
"D03" integer,
"D04" integer,
"D05" integer,
"D06" integer,
"D07" integer,
"D08" integer,
"D09" integer,
"D10" integer,
"D11" integer,
"D12" integer,
"D13" integer,
"id" text
);
CREATE TABLE geometry (
id text NOT NULL,
id_geo numeric(10,0),
cm_code character varying(3),
name character varying(50),
status character varying(20),
lat integer,
long integer,
lat_centroid integer,
long_centroid integer,
z_ smallint,
area numeric(10,0),
population double precision,
code_ct character varying(2),
code_r character varying(1),
code_dp character varying(2),
name_dp character varying(30),
code_rg character varying(2),
geom geometry(MultiPolygon,4326),
x real,
y real
);
每个 table
中大约有 40 000 行索引已创建如下
CREATE INDEX statistics_id_idx ON public.statistics USING btree (id COLLATE pg_catalog."default");
CREATE INDEX geometry_geom_idx ON public.geometry USING gist (geom);
CREATE INDEX geometry_id_gin2 ON public.geometry USING gin (id COLLATE pg_catalog."default" gin_trgm_ops);
为了获得信息,我在 geometry_id 和 statistics_id 上尝试了不同的索引(btree 和 gin)。
我没有发现您的查询有任何问题。
要检查的事情
(geometry.id = (statistics.id)::text)
两个字段的数据类型相同吗?WHERE statistics.id not like '97%';
。LIKE '%me'
永远不会使用索引,但LIKE 'me%'
可能会使用索引。 Why doesnt use index?st_x(st_centroid(st_transform(geometry.geom, 2154))) AS x,
是一个函数,需要时间。需要转换坐标,然后提取一个值。如果计算该值并将其存储在一个字段中,你会更好。- 您的几何索引对此查询没有任何影响,因为您正在计算一个值而不是搜索某些内容。
- 如果您想要执行索引不正确的地理搜索。不过我们可以稍后再谈
值得尝试的事情
首先是where like
.
SELECT *
FROM statistics
WHERE statistics.id not like '97%';
然后 join
SELECT statistics.*,
geometry.geom
FROM statistics
JOIN geometry ON statistics.id = geometry.id
然后加入 + st_x
SELECT statistics.*,
st_x(st_centroid(st_transform(geometry.geom, 2154))) AS x,
st_y(st_centroid(st_transform(geometry.geom, 2154))) AS y
FROM statistics
JOIN geometry ON statistics.id = geometry.id
然后在 geometry
table
x, y
列
SELECT statistics.*,
geometry.x,
geometry.y,
FROM statistics
JOIN geometry ON statistics.id = geometry.id
然后加入+st_x+where like
然后加入+geometry.xy+where like
比较每个步骤之间的时间以检查哪个步骤花费的时间最多。