全文共 字，预计学习时间 分钟或更长
人与世界万物的互动会产生大量的时空数据。那么，当我们需要随时调用过去的数据时，改怎么办？尤其是面对各种海量、多维度的数据库，如果没有高效的搜索方法，我们只能望洋兴叹、束手无策。
别担心，本文将使用详细的代码，携手传授高效的搜索技巧！
对象数据可分为静态数据(相对静态，如建筑)和动态数据(如人类活动和物联网传感器活动)两种类型。
搜索时空快照
有些对象以相对较低的频率生成数据。例如，建筑物、道路等惰性物体在几年内可能不会改变。如果将为这些对象生成的数据写入数据库，并根据时间范围查询数据（如2017-07-01-2017-07-02），则可能找不到与这些对象相关的数据。原因很简单，数据库在此期间根本没有相关的数据输入。
搜索时空行为数据
时空行为数据是指从人类活动等动态对象中获取数据。
例如，分析特定地区特定时间段内某一人群的特征，或分析大学周围人群在工作日和周末的差异。
本文不讨论时空快照。现在，让我们来看看如何搜索时空行为数据。
时空行为数据包括时间、空间和对象三个属性。
非结构化索引：
create table test( id int8, crt_time timestamp, -- Time pos geometry, -- Location obj jsonb -- Object description );除了应用于JSON，对象描述也可以使用结构化数据。
create table test( id int8, crt_time timestamp, -- Time pos geometry, -- Location c1 int, -- Some property examples c2 int, c3 text, c4 float8, c5 int, c6 date, c7 text, c8 int, c9 int, c10 int );时空行为数据SQL查询实例
select * from test where pos ? < ? and crt_time between ? and ? and ( (c1 = ? and c2 between ? and ?) or c10=?) ... ;考虑使用以下知识：
时间序列BRIN索引
crt_time字段是时间序列字段，表示生成数据的时间。PostgreSQL存储与字段值具有很强的线性相关性。
因此，BRIN索引非常合适。
使用BRIN索引取代分区表TPC-H测试。大规模搜索的性能甚至比使用分区表时更好。
create index idx_test_1 on test using brin(crt_time);空间索引
显然，空间检索需要空间索引。PostgreSQL空间检索可以用三种方法来实现。
create index idx_test_2 on test using gist(pos);该索引支持空间KNN搜索和确定空间位置等功能。
create index idx_test_2 on test using spgist(pos);该索引支持空间KNN搜索和确定空间位置等功能。
(将经度和纬度转换为Geohash并为hash值创建B-tree索引)。只需使用表达式索引。
create index idx_test_3 on test using btree( ST_GeoHash(pos,15) );该索引支持前缀搜索（它可以实现编码地理信息网格中包含的关系）。它是一个需要二次过滤的损坏索引。
GiST和SPGiST空间索引能找到准确的地理位置信息，优于GEOHASH索引。但是，在查询信息时要特别注意。
GIN 索引
该索引类型的目标是对象属性字段JSONB或多个结构化对象属性字段。只需使用GIN索引。
例如：
create extension btree_gin;非结构化索引：
create index idx_test_4 on test using gin( obj );结构索引：
create index idx_test_4 on test using gin( c1,c2,c3,c4,c5,c6,c7,c8,c9 );BitmapAnd和BitmapOr
在上一节中，可以根据数据类型和查询要求选择不同的查询维度。
但是，这些索引能同时使用吗？ PostgreSQL提供多个索引bitmapAnd及bitmapOr接口。它们可以结合多个索引来减少需要扫描的数据库数量。
Heap, one square = one page: --------------------------------------------- |c____u_____X___u___X_________u___cXcc______u_| --------------------------------------------- Rows marked c match customers pkey condition. Rows marked u match username condition. Rows marked X match both conditions. Bitmap scan from customers_pkey: --------------------------------------------- |100000000001000000010000000000000111100000000| bitmap 1 --------------------------------------------- One bit per heap page, in the same order as the heap Bts 1 when condition matches, 0 if not Bitmap scan from ix_cust_username: +---------------------------------------------+ |000001000001000100010000000001000010000000010| bitmap 2 +---------------------------------------------+ Once the bitmaps are created a bitwise AND is performed on them: +---------------------------------------------+ |100000000001000000010000000000000111100000000| bitmap 1 |000001000001000100010000000001000010000000010| bitmap 2 &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& |000000000001000000010000000000000010000000000| Combined bitmap +-----------+-------+--------------+----------+ | | | v v v Used to scan the heap only for matching pages: +---------------------------------------------+ |___________X_______X______________X__________| +---------------------------------------------+ The bitmap heap scan then seeks to the start of each page and reads the page: +---------------------------------------------+ |___________X_______X______________X__________| +---------------------------------------------+ seek------->^seek-->^seek--------->^ | | | ------------------------ only these pages read例如：
select * from test where c1 ... and crt_time between ? and ? and test->> c1 in (?, ? ...);根据统计数据自动使用适当的索引。如果需要，bitmapAnd和bitmapOr将在多个索引上自动执行合并扫描。跳过不需要扫描的页面，重新检查命中的页面。
堆表存储分级和分区
存储可以分为一级分区或多级分区：
例如，按时间划分。
create table test( id int8, crt_time timestamp, -- Time pos geometry, -- Location obj jsonb -- Object description ) PARTITION BY range (crt_time) ; create table test_201701 PARTITION OF test for values FROM ( 2017-01-01 ) TO ( 2017-02-01 ); ......例如，先按时间分区，然后按Geohash划分。
create table test_201701 PARTITION OF test for values FROM ( 2017-01-01 ) TO ( 2017-02-01 ) partition by range(st_geohash(pos,15)); ... create table test_201701_prefix1 PARTITION OF test for values FROM ( xxxx1 ) TO ( xxxx2 ); -- Generate BOX (GRID) on a map, find corresponding boundaries and use -- boundaries as partitioning conditions使用分区时，如果查询条件包括分区键(如时间和空间范围)，相应的分区将自动定位，这即为需要扫描的数据量。
创建面向对象属性的GIN索引，以实现高效查询。
索引分级与分区
与数据一样，索引在不使用分区表的情况下也支持分区逻辑。
空间索引+时间分区
create index idx_20170101 on tbl using gist (pos) where crt_time between 2017-01-01 and 2017-01-02 ; ... create index idx_20170102 on tbl using gist (pos) where crt_time between 2017-01-02 and 2017-01-03 ; ...通过使用前述分区索引，可以在输入时间范围后快速定位目标数据，执行空间搜索。
select * from tbl where crt_time between 2017-01-01 and 2017-01-02 -- Time and (pos ?) < ? -- Distance to a point to be searched for and ? -- Other conditions order by pos ? -- Sort by distance limit ?; -- Number of results to be returned可以使用更多的索引分区，比如用作搜索条件和商店类型的维度(对象属性)(假设它是可枚举的或在范围相对较小的情况下)。
create index idx_20170101_mod0 on tbl using gist (pos) where crt_time between 2017-01-01 and 2017-01-02 and dtype=0; ... create index idx_20170101_mod1 on tbl using gist (pos) where crt_time between 2017-01-01 and 2017-01-02 and dtype=1; ...通过使用前面的分区索引，在输入时间范围或特定条件以执行空间搜索后，可以快速定位目标数据。
select * from tbl where crt_time between 2017-01-01 and 2017-01-02 -- Time and (pos ?) < ? -- Distance to a point to be searched for and dtype=0 -- Object condition and ? -- Other conditions order by pos ? -- Sort by distance limit ?; -- Number of results to be returned请注意，前面的SQL查询可以实现最佳性能优化。
索引组织形式(或索引结构)可以由逻辑分区重新构造，可以用上述类似的索引创建方法覆盖所有条件。
CTID相交阵列连接扫描
如前所述，BitmapAnd和BitmapOr合并扫描是在多个索引或GIN索引中自动执行的。事实上，这种扫描也可以在SQL中显式执行。
每个条件渗透对应的CTID。
使用Intersect或Union生成满足总体需求的CTID。(Intersect对应于“and”条件；union对应于“or”条件。)
生成一个ctid数组。
1. 创建对象提要数据表
postgres=# create table tbl (id int, info text, crt_time timestamp, pos point, c1 int , c2 int, c3 int ); CREATE TABLE2. 将5000万条测试数据写入表中
postgres=# insert into tbl select generate_series(1,50000000), md5(random()::text), clock_timestamp(), point(180-random()*180, 90-random()*90), random()*10000, random()*5000, random()*1000; INSERT 0 500000003. 创建对象索引
postgres=# create index idx_tbl_1 on tbl using gin (info, c1, c2, c3); CREATE INDEX4. 创建时间索引
postgres=# create index idx_tbl_2 on tbl using btree (crt_time); CREATE INDEX5. 创建空间索引
postgres=# create index idx_tbl_3 on tbl using gist (pos); CREATE INDEX6. 生成数据布局以方便后续查询
postgres=# select min(crt_time),max(crt_time),count(*) from tbl; min | max | count ----------------------------+----------------------------+---------- 2017-07-22 17:59:34.136497 | 2017-07-22 18:01:27.233688 | 50000000 (1 row)7. 创建一个极限KNN查询函数
create or replace function ff(point, float8, int) returns setof tid as $declare v_rec record; v_limit int := $3; begin set local enable_seqscan=off; -- Force index that exits when scanned rows reach a specific number for v_rec in select *, (pos $1) as dist, ctid from tbl order by pos $1 loop if v_limit <=0 then -- raise notice "Sufficient data obtained" return; end if; if v_rec.dist > $2 then -- raise notice "All matching points returned" return; else return next v_rec.ctid; end if; v_limit := v_limit -1; end loop; end; $ language plpgsql strict volatile; postgres=# select * from ff(point (100,100) ,100,100) ; ff ------------- (407383,11) (640740,9) (26073,51) (642750,34) ... (100 rows) Time: 1.061 ms8. CTID合并检索
显示符合以下条件的记录
( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ) and pos point (0,0) < 5 and crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 ;首先，分别查看每个条件，找匹配一个条件的记录数量，以及在索引扫描上所花时长。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where c1 in (1,2,3,4,100,200,99,88,77,66,55); QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on postgres.tbl (cost=820.07..65393.94 rows=54151 width=73) (actual time=23.842..91.911 rows=54907 loops=1) Output: id, info, crt_time, pos, c1, c2, c3 Recheck Cond: (tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) Heap Blocks: exact=52778 Buffers: shared hit=52866 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..806.54 rows=54151 width=0) (actual time=14.264..14.264 rows=54907 loops=1) Index Cond: (tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) Buffers: shared hit=88 Planning time: 0.105 ms Execution time: 94.606 ms (10 rows)postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where c2<10; QUERY PLAN --------------------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on postgres.tbl (cost=835.73..112379.10 rows=99785 width=73) (actual time=69.243..179.388 rows=95147 loops=1) Output: id, info, crt_time, pos, c1, c2, c3 Recheck Cond: (tbl.c2 < 10) Heap Blocks: exact=88681 Buffers: shared hit=88734 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..810.79 rows=99785 width=0) (actual time=53.612..53.612 rows=95147 loops=1) Index Cond: (tbl.c2 < 10) Buffers: shared hit=53 Planning time: 0.094 ms Execution time: 186.201 ms (10 rows)(为快速获得结果，PostgreSQL使用位图进行合并扫描)
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 <10; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------ Bitmap Heap Scan on postgres.tbl (cost=1694.23..166303.58 rows=153828 width=73) (actual time=98.988..266.852 rows=149930 loops=1) Output: id, info, crt_time, pos, c1, c2, c3 Recheck Cond: ((tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) OR (tbl.c2 < 10)) Heap Blocks: exact=134424 Buffers: shared hit=134565 -> BitmapOr (cost=1694.23..1694.23 rows=153936 width=0) (actual time=73.763..73.763 rows=0 loops=1) Buffers: shared hit=141 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..806.54 rows=54151 width=0) (actual time=16.733..16.733 rows=54907 loops=1) Index Cond: (tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) Buffers: shared hit=88 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..810.79 rows=99785 width=0) (actual time=57.029..57.029 rows=95147 loops=1) Index Cond: (tbl.c2 < 10) Buffers: shared hit=53 Planning time: 0.149 ms Execution time: 274.548 ms (15 rows)(即使运用出色的KNN性能优化，仍然需要耗费195毫秒)。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from ff(point (0,0) ,5,1000000); QUERY PLAN ---------------------------------------------------------------------------------------------------------------------- Function Scan on postgres.ff (cost=0.25..10.25 rows=1000 width=6) (actual time=188.563..192.114 rows=60687 loops=1) Output: ff Function Call: ff( (0,0) ::point, 5 ::double precision, 1000000) Buffers: shared hit=61296 Planning time: 0.029 ms Execution time: 195.097 ms (6 rows)让我们看看不使用KNN优化需要多长时间。
结果非常令人惊讶——极限优化性能提高了一个数量级。
使用所有索引逐个扫描数据条件，得到ctid并执行ctid扫描。
现在，让我们来分解这个过程：
首先，让我们看看时间和对象属性的合并查询，成果非常惊人。使用位图BitmapOr时，查询可以跳过大多数数据块，并且扫描时间比单索引扫描要短。
注意，在此步骤中记录的数量减少到7,847条。
postgres=# explain (analyze,verbose,timing,costs,buffers) select ctid from tbl where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ); QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Bitmap Heap Scan on postgres.tbl (cost=35025.85..44822.94 rows=7576 width=6) (actual time=205.577..214.821 rows=7847 loops=1) Output: ctid Recheck Cond: (((tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) OR (tbl.c2 < 10)) AND (tbl.crt_time >= 2017-07-22 17:59:34 ::timestamp without time zone) AND (tbl.crt_time <= 2017-07-22 17:59:40 ::timestamp without time zone)) Heap Blocks: exact=6983 Buffers: shared hit=14343 -> BitmapAnd (cost=35025.85..35025.85 rows=7581 width=0) (actual time=204.048..204.048 rows=0 loops=1) Buffers: shared hit=7360 -> BitmapOr (cost=1621.11..1621.11 rows=153936 width=0) (actual time=70.279..70.279 rows=0 loops=1) Buffers: shared hit=141 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..806.54 rows=54151 width=0) (actual time=15.860..15.860 rows=54907 loops=1) Index Cond: (tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) Buffers: shared hit=88 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..810.79 rows=99785 width=0) (actual time=54.418..54.418 rows=95147 loops=1) Index Cond: (tbl.c2 < 10) Buffers: shared hit=53 -> Bitmap Index Scan on idx_tbl_2 (cost=0.00..33402.60 rows=2462443 width=0) (actual time=127.101..127.101 rows=2640751 loops=1) Index Cond: ((tbl.crt_time >= 2017-07-22 17:59:34 ::timestamp without time zone) AND (tbl.crt_time <= 2017-07-22 17:59:40 ::timestamp without time zone)) Buffers: shared hit=7219 Planning time: 0.203 ms Execution time: 216.697 ms (20 rows)然后，看KNN的扫描时间：
注意，60,687条记录满足KNN距离条件，所以接下来将解释CTID合并扫描与原始扫描之间的性能比较。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from ff(point (0,0) ,5,1000000); QUERY PLAN ---------------------------------------------------------------------------------------------------------------------- Function Scan on postgres.ff (cost=0.25..10.25 rows=1000 width=6) (actual time=188.563..192.114 rows=60687 loops=1) Output: ff Function Call: ff( (0,0) ::point, 5 ::double precision, 1000000) Buffers: shared hit=61296 Planning time: 0.029 ms Execution time: 195.097 ms (6 rows)最后，将这些片段合并到ctid中。
select * from ff(point (0,0) ,5,1000000) intersect select ctid from tbl where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ); ff ------------ (1394,8) (3892,50) (6124,45) (7235,8) (7607,45) (11540,8) (13397,31) (14266,36) (18149,7) (19256,44) (24671,62) (26525,64) (30235,48) (13 rows) Time: 463.012 ms取得最终纪录。
select * from tbl where ctid = any ( array( -- array start select * from ff(point (0,0) ,5,1000000) intersect select ctid from tbl where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ) ) -- array end ); id | info | crt_time | pos | c1 | c2 | c3 ---------+----------------------------------+----------------------------+----------------------------------------+------+------+----- 104558 | c4699c933d4e2d2a10d828c4ff0b3362 | 2017-07-22 17:59:34.362508 | (4.20534582808614,2.43749532848597) | 99 | 4858 | 543 291950 | 1c2901689ab1eb7653d8ad972f7aa376 | 2017-07-22 17:59:34.776808 | (2.5384977646172,1.09820357523859) | 3 | 2131 | 360 459345 | 9e46548f29d914019ce53a589be8ebac | 2017-07-22 17:59:35.148699 | (0.715781506150961,3.1486327573657) | 1 | 1276 | 8 542633 | c422d6137f9111d5c2dc723b40c7023f | 2017-07-22 17:59:35.334278 | (0.0631888210773468,2.2334903664887) | 4968 | 3 | 245 570570 | fc57bfc6b7781d89b17c90417bd306f7 | 2017-07-22 17:59:35.39653 | (3.14926156774163,1.04107855819166) | 88 | 2560 | 561 865508 | 34509c7f7640afaf288a5e1d38199701 | 2017-07-22 17:59:36.052573 | (3.12869547866285,2.34822122845799) | 2 | 65 | 875 1004806 | afe9f88cbebf615a7ae5f41180c4b33f | 2017-07-22 17:59:36.362027 | (1.13972157239914,3.28763140831143) | 3 | 1639 | 208 1069986 | 6b9f27bfde993fb0bae3336ac010af7a | 2017-07-22 17:59:36.507775 | (4.51995821669698,2.08761331625283) | 2 | 200 | 355 1361182 | 7c4c1c208c2b2b21f00772c43955d238 | 2017-07-22 17:59:37.155127 | (1.7334086727351,2.18367457855493) | 9742 | 0 | 232 1444244 | 41bf6f8e4b89458c13fb408a7db05284 | 2017-07-22 17:59:37.339594 | (0.52773853763938,2.16670122463256) | 1 | 2470 | 820 1850387 | 6e0011c6db76075edd2aa7f81ec94129 | 2017-07-22 17:59:38.243091 | (0.0168232340365648,0.420973123982549) | 100 | 4395 | 321 1989439 | 6211907ac254a4a3ca54f90822a2095e | 2017-07-22 17:59:38.551637 | (0.0274275150150061,0.490507003851235) | 1850 | 5 | 74 2267673 | 898fdd54dcc5b14c27cf1c8b9afe2471 | 2017-07-22 17:59:39.170035 | (0.394239127635956,2.86229319870472) | 2892 | 6 | 917 (13 rows) Time: 462.715 ms过程花费462毫秒。
9. 测试原始SQL查询的性能: PostgreSQL Multi-Index BitmapAnd and BitmapOr跳过扫描
直接编写SQL查询，而不是使用多CTID扫描。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ) and pos point (0,0) < 5; Bitmap Heap Scan on postgres.tbl (cost=35022.06..44857.06 rows=2525 width=73) (actual time=205.542..214.547 rows=13 loops=1) Output: id, info, crt_time, pos, c1, c2, c3 Recheck Cond: (((tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) OR (tbl.c2 < 10)) AND (tbl.crt_time >= 2017-07-22 17:59:34 ::timestamp without time zone) AND (tbl.crt_time <= 2017-07-22 17:59:40 ::timestamp without time zone)) Filter: ((tbl.pos (0,0) ::point) < 5 ::double precision) Rows Removed by Filter: 7834 Heap Blocks: exact=6983 Buffers: shared hit=14343 -> BitmapAnd (cost=35022.06..35022.06 rows=7581 width=0) (actual time=203.620..203.620 rows=0 loops=1) Buffers: shared hit=7360 -> BitmapOr (cost=1618.58..1618.58 rows=153936 width=0) (actual time=71.660..71.660 rows=0 loops=1) Buffers: shared hit=141 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..806.54 rows=54151 width=0) (actual time=14.861..14.861 rows=54907 loops=1) Index Cond: (tbl.c1 = ANY ( {1,2,3,4,100,200,99,88,77,66,55} ::integer[])) Buffers: shared hit=88 -> Bitmap Index Scan on idx_tbl_1 (cost=0.00..810.79 rows=99785 width=0) (actual time=56.797..56.797 rows=95147 loops=1) Index Cond: (tbl.c2 < 10) Buffers: shared hit=53 -> Bitmap Index Scan on idx_tbl_2 (cost=0.00..33402.60 rows=2462443 width=0) (actual time=125.255..125.255 rows=2640751 loops=1) Index Cond: ((tbl.crt_time >= 2017-07-22 17:59:34 ::timestamp without time zone) AND (tbl.crt_time <= 2017-07-22 17:59:40 ::timestamp without time zone)) Buffers: shared hit=7219 Planning time: 0.160 ms Execution time: 216.797 ms (22 rows)性能如预期的那样好，之前解释过原因。KNN条件以外的条件已经将结果收敛到7,000条记录，因此没有必要使用包含KNN条件的索引。(即使使用KNN索引也需要195毫秒，因为有60,687条记录满足KNN条件。)
校验结果：
select * from tbl where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ) and pos point (0,0) < 5; id | info | crt_time | pos | c1 | c2 | c3 ---------+----------------------------------+----------------------------+----------------------------------------+------+------+----- 104558 | c4699c933d4e2d2a10d828c4ff0b3362 | 2017-07-22 17:59:34.362508 | (4.20534582808614,2.43749532848597) | 99 | 4858 | 543 291950 | 1c2901689ab1eb7653d8ad972f7aa376 | 2017-07-22 17:59:34.776808 | (2.5384977646172,1.09820357523859) | 3 | 2131 | 360 459345 | 9e46548f29d914019ce53a589be8ebac | 2017-07-22 17:59:35.148699 | (0.715781506150961,3.1486327573657) | 1 | 1276 | 8 542633 | c422d6137f9111d5c2dc723b40c7023f | 2017-07-22 17:59:35.334278 | (0.0631888210773468,2.2334903664887) | 4968 | 3 | 245 570570 | fc57bfc6b7781d89b17c90417bd306f7 | 2017-07-22 17:59:35.39653 | (3.14926156774163,1.04107855819166) | 88 | 2560 | 561 865508 | 34509c7f7640afaf288a5e1d38199701 | 2017-07-22 17:59:36.052573 | (3.12869547866285,2.34822122845799) | 2 | 65 | 875 1004806 | afe9f88cbebf615a7ae5f41180c4b33f | 2017-07-22 17:59:36.362027 | (1.13972157239914,3.28763140831143) | 3 | 1639 | 208 1069986 | 6b9f27bfde993fb0bae3336ac010af7a | 2017-07-22 17:59:36.507775 | (4.51995821669698,2.08761331625283) | 2 | 200 | 355 1361182 | 7c4c1c208c2b2b21f00772c43955d238 | 2017-07-22 17:59:37.155127 | (1.7334086727351,2.18367457855493) | 9742 | 0 | 232 1444244 | 41bf6f8e4b89458c13fb408a7db05284 | 2017-07-22 17:59:37.339594 | (0.52773853763938,2.16670122463256) | 1 | 2470 | 820 1850387 | 6e0011c6db76075edd2aa7f81ec94129 | 2017-07-22 17:59:38.243091 | (0.0168232340365648,0.420973123982549) | 100 | 4395 | 321 1989439 | 6211907ac254a4a3ca54f90822a2095e | 2017-07-22 17:59:38.551637 | (0.0274275150150061,0.490507003851235) | 1850 | 5 | 74 2267673 | 898fdd54dcc5b14c27cf1c8b9afe2471 | 2017-07-22 17:59:39.170035 | (0.394239127635956,2.86229319870472) | 2892 | 6 | 917 (13 rows)假设前面的查询条件保持不变，使用分区索引来测试性能。
这是为了演示分区索引的极端效果。在实际场景中，集合级别可能没有那么高(例如按天集合或按ID散列集合)。只要集合是可能的，就可以展现出色的性能。
postgres=# create index idx_tbl_4 on tbl using gist (pos) where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ) ; CREATE INDEX Time: 8359.330 ms (00:08.359)重构极值KNN优化函数
create or replace function ff(point, float8, int) returns setof record as $declare v_rec record; v_limit int := $3; begin set local enable_seqscan=off; -- Force index that exits when scanned rows reach a specific number for v_rec in select *, (pos $1) as dist from tbl where crt_time between 2017-07-22 17:59:34 and 2017-07-22 17:59:40 and ( c1 in (1,2,3,4,100,200,99,88,77,66,55) or c2 < 10 ) order by pos $1 loop if v_limit <=0 then -- raise notice "Sufficient data obtained" return; end if; if v_rec.dist > $2 then -- raise notice "All matching points returned" return; else return next v_rec; end if; v_limit := v_limit -1; end loop; end; $ language plpgsql strict volatile;查询性能：
postgres=# select * from ff(point (0,0) , 5, 10000000) as t(id int, info text, crt_time timestamp, pos point, c1 int, c2 int, c3 int, dist float8); id | info | crt_time | pos | c1 | c2 | c3 | dist ---------+----------------------------------+----------------------------+----------------------------------------+------+------+-----+------------------- 1850387 | 6e0011c6db76075edd2aa7f81ec94129 | 2017-07-22 17:59:38.243091 | (0.0168232340365648,0.420973123982549) | 100 | 4395 | 321 | 0.421309141034319 1989439 | 6211907ac254a4a3ca54f90822a2095e | 2017-07-22 17:59:38.551637 | (0.0274275150150061,0.490507003851235) | 1850 | 5 | 74 | 0.49127323294376 1444244 | 41bf6f8e4b89458c13fb408a7db05284 | 2017-07-22 17:59:37.339594 | (0.52773853763938,2.16670122463256) | 1 | 2470 | 820 | 2.23004532710301 542633 | c422d6137f9111d5c2dc723b40c7023f | 2017-07-22 17:59:35.334278 | (0.0631888210773468,2.2334903664887) | 4968 | 3 | 245 | 2.23438404136508 291950 | 1c2901689ab1eb7653d8ad972f7aa376 | 2017-07-22 17:59:34.776808 | (2.5384977646172,1.09820357523859) | 3 | 2131 | 360 | 2.76586731309247 1361182 | 7c4c1c208c2b2b21f00772c43955d238 | 2017-07-22 17:59:37.155127 | (1.7334086727351,2.18367457855493) | 9742 | 0 | 232 | 2.78803520274409 2267673 | 898fdd54dcc5b14c27cf1c8b9afe2471 | 2017-07-22 17:59:39.170035 | (0.394239127635956,2.86229319870472) | 2892 | 6 | 917 | 2.88931598221975 459345 | 9e46548f29d914019ce53a589be8ebac | 2017-07-22 17:59:35.148699 | (0.715781506150961,3.1486327573657) | 1 | 1276 | 8 | 3.22896754478952 570570 | fc57bfc6b7781d89b17c90417bd306f7 | 2017-07-22 17:59:35.39653 | (3.14926156774163,1.04107855819166) | 88 | 2560 | 561 | 3.31688000783581 1004806 | afe9f88cbebf615a7ae5f41180c4b33f | 2017-07-22 17:59:36.362027 | (1.13972157239914,3.28763140831143) | 3 | 1639 | 208 | 3.47958123047986 865508 | 34509c7f7640afaf288a5e1d38199701 | 2017-07-22 17:59:36.052573 | (3.12869547866285,2.34822122845799) | 2 | 65 | 875 | 3.91188935630676 104558 | c4699c933d4e2d2a10d828c4ff0b3362 | 2017-07-22 17:59:34.362508 | (4.20534582808614,2.43749532848597) | 99 | 4858 | 543 | 4.86069100130757 1069986 | 6b9f27bfde993fb0bae3336ac010af7a | 2017-07-22 17:59:36.507775 | (4.51995821669698,2.08761331625283) | 2 | 200 | 355 | 4.97877009299311 (13 rows) Time: 0.592 ms太棒了！查询时间从200毫秒减少到1毫秒以内。
优化方法回顾：
1. 为不同的数据类型构建不同的索引。
例如，对空间使用GiST或SP-GiST索引，对时间使用B树或BRIN索引，对多个对象属性使用GIN索引。索引的目的是缩小数据扫描的范围。
2. 方法五提到数据分区。
数据分区的目的是有意地组织数据，这意味着有意地组织数据以满足搜索需求。例如，如果时间是必需的查询条件或公共查询条件，那么可以按时间(分区)分割数据，以减少需要扫描的数据量。
3. 方法六描述了索引分区。
目的类似于方法五。方法五和方法六的区别在于分区在索引级别使用，因此当执行索引扫描时，数据命中率会直接提高。
4.方法七中的ctid合并扫描类似于PostgreSQL中的多索引bitmapAnd或bitmapOr扫描。
bitmapAnd/bitmapOr跳过不需要扫描的块，方法七中的ctid合并扫描跳过不需要扫描的行。
合并从多个索引扫描获得的ctid。跳过不需要扫描的行数。
如果当其他条件为“AND”时，过滤条件可以显著减少ctid(记录)，则没有必要使用ctid合并扫描。相反，使用FILTER作为另一个条件。(这将略微增加CPU开销。)
5. 最好的功夫总是以最大的灵活性、自由和对每一个动作的无限想象为特征。
PostgreSQL实现多索引BitmapAnd或BitmapOr扫描，显著提高了多种条件(索引)下的数据命中率。
此外，PostgreSQL具有出色的CBO估计机制，它允许PostgreSQL不总是使用位图合并扫描的所有索引。这也是为什么在“测试原始SQL查询的性能——PostgreSQL多索引BitmapAnd位图或跳过扫描”一节中描述的性能更好。
6. 如何实现极端优化
采用方法五或六，并使用可修复的条件作为分区键来分区数据或索引。
对于其他条件，可以使用PostgreSQL中的多索引BitmapAnd或BitmapOr扫描来提高多条件(索引)的数据命中率。
我们可以看到，按照时间、空间和对象属性从5,000万数据块中进行多维检索所需的时间减少到了0.592毫秒。
7. 对于空间数据，除了使用GiST索引，我们还可以使用BRIN索引，这降低了成本。有条理地组织数据后，会使滤波性能良好。
编译组：李美尼、余书敏
相关链接：
https://dzone.com/articles/efficient-search-on-massive-data-with-multidimensi
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