數(shù)據(jù)庫(kù)中如何搜索時(shí)空行為數(shù)據(jù)
這篇文章主要講解了“數(shù)據(jù)庫(kù)中如何搜索時(shí)空行為數(shù)據(jù)”，文中的講解內(nèi)容簡(jiǎn)單清晰，易于學(xué)習(xí)與理解，下面請(qǐng)大家跟著小編的思路慢慢深入，一起來(lái)研究和學(xué)習(xí)“數(shù)據(jù)庫(kù)中如何搜索時(shí)空行為數(shù)據(jù)”吧！
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數(shù)據(jù)結(jié)構(gòu)
時(shí)空行為數(shù)據(jù)包含三個(gè)屬性：時(shí)間、空間和對(duì)象。
非結(jié)構(gòu)化索引：
create table test( id int8, crt_time timestamp, -- Time pos geometry, -- Location obj jsonb -- Object description );
除了應(yīng)用于JSON，結(jié)構(gòu)化數(shù)據(jù)還可以用于對(duì)象描述。例如：
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 );
時(shí)空行為數(shù)據(jù)的SQL查詢實(shí)例
select * from test where pos <-> ? < ? and crt_time between ? and ? and ( (c1 = ? and c2 between ? and ?) or c10=?) ... ;
優(yōu)化方法
考慮運(yùn)用以下知識(shí)：
時(shí)間序列BRIN索引
crt_time字段是一個(gè)時(shí)間序列字段，表示生成數(shù)據(jù)的時(shí)間。在PostgreSQL堆存儲(chǔ)中，存儲(chǔ)和該字段的值具有很強(qiáng)的線性相關(guān)性。
因此，BRIN索引很合適。
使用BRIN索引來(lái)代替分區(qū)表進(jìn)行TPC-H測(cè)試。大范圍搜索的性能甚至優(yōu)于使用分區(qū)表時(shí)的功能。
create index idx_test_1 on test using brin(crt_time);
空間索引
顯然，空間檢索需要空間索引。PostgreSQL中可以使用三種方法實(shí)現(xiàn)空間檢索。
1. 幾何類(lèi)型的GIST索引
create index idx_test_2 on test using gist(pos);
該索引支持空間KNN搜索和空間位置確定等功能。
2. 幾何類(lèi)型的主索引
create index idx_test_2 on test using spgist(pos);
該索引支持空間KNN搜索和空間位置確定等功能。
3. Geohash和B-tree索引(將經(jīng)度和緯度轉(zhuǎn)換為Geohash并為hash值創(chuàng)建B-tree索引)。只需使用表達(dá)式索引。
create index idx_test_3 on test using btree( ST_GeoHash(pos,15) );
此索引支持前綴搜索(其能落實(shí)編碼地理信息網(wǎng)格中包含的關(guān)系)。它屬于有損索引，需要二次過(guò)濾。
GiST和SPGiST空間索引能夠找到準(zhǔn)確的地理位置信息，優(yōu)于GEOHASH索引。但是，查詢信息時(shí)需要特別注意。
GIN 索引
此索引類(lèi)型的目標(biāo)是對(duì)象屬性字段JSONB或多個(gè)結(jié)構(gòu)化對(duì)象屬性字段。只需使用GIN索引。
例如：
create extension btree_gin;
非結(jié)構(gòu)化索引：
create index idx_test_4 on test using gin( obj );
結(jié)構(gòu)化索引：
create index idx_test_4 on test using gin( c1,c2,c3,c4,c5,c6,c7,c8,c9 );
BitmapAnd和BitmapOr
但是，可以同時(shí)使用這些索引嗎? PostgreSQL為多個(gè)索引提供bitmapAnd及bitmapOr接口。它們可以組合多個(gè)索引，減少需要掃描的數(shù)據(jù)庫(kù)數(shù)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 Bits 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 (?, ? ...);
根據(jù)統(tǒng)計(jì)數(shù)據(jù)自動(dòng)使用適當(dāng)?shù)乃饕?。如果需要，bitmapAnd和bitmapOr將在多個(gè)索引上自動(dòng)執(zhí)行合并掃描。跳過(guò)不需要掃描的頁(yè)面，重新檢查命中的頁(yè)面。
堆表存儲(chǔ)分級(jí)和分區(qū)
存儲(chǔ)可以分為一級(jí)分區(qū)或多級(jí)分區(qū)：
1. 單一分區(qū)
例如，按時(shí)間劃分。
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 ); ......
2. 多層分區(qū)
例如，先按時(shí)間分區(qū)，然后按Geohash劃分。
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
使用分區(qū)時(shí)，如果查詢條件包括分區(qū)鍵(如時(shí)間和空間范圍)，相應(yīng)的分區(qū)將自動(dòng)定位，這即為需要掃描的數(shù)據(jù)量。
創(chuàng)建面向?qū)ο髮傩缘腉IN索引，以實(shí)現(xiàn)高效查詢。
索引分級(jí)與分區(qū)
與數(shù)據(jù)一樣，索引在不使用分區(qū)表的情況下也支持分區(qū)邏輯。
空間索引+時(shí)間分區(qū)
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 ; ...
通過(guò)使用前述分區(qū)索引，可以在輸入時(shí)間范圍后快速定位目標(biāo)數(shù)據(jù)，執(zhí)行空間搜索。
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區(qū)，比如用作搜索條件和商店類(lèi)型的維度(對(duì)象屬性)(假設(shè)它是可枚舉的或在范圍相對(duì)較小的情況下)。
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; ...
通過(guò)使用前面的分區(qū)索引，在輸入時(shí)間范圍或特定條件以執(zhí)行空間搜索后，可以快速定位目標(biāo)數(shù)據(jù)。
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
請(qǐng)注意，前面的SQL查詢可以實(shí)現(xiàn)最佳性能優(yōu)化。
索引組織形式(或索引結(jié)構(gòu))可以由邏輯分區(qū)重新構(gòu)造，可以用上述類(lèi)似的索引創(chuàng)建方法覆蓋所有條件。
CTID相交陣列連接掃描
如前所述，BitmapAnd和BitmapOr合并掃描是在多個(gè)索引或GIN索引中自動(dòng)執(zhí)行的。事實(shí)上，這種掃描也可以在SQL中顯式執(zhí)行。
每個(gè)條件滲透對(duì)應(yīng)的CTID。
使用Intersect或Union生成滿足總體需求的CTID。(Intersect對(duì)應(yīng)于“and”條件;union對(duì)應(yīng)于“or”條件。)
生成一個(gè)ctid數(shù)組。
示例
1. 創(chuàng)建對(duì)象提要數(shù)據(jù)表
postgres=# create table tbl (id int, info text, crt_time timestamp, pos point, c1 int , c2 int, c3 int ); CREATE TABLE
2. 將5000萬(wàn)條測(cè)試數(shù)據(jù)寫(xiě)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 50000000
3. 創(chuàng)建對(duì)象索引
postgres=# create index idx_tbl_1 on tbl using gin (info, c1, c2, c3); CREATE INDEX
4. 創(chuàng)建時(shí)間索引
postgres=# create index idx_tbl_2 on tbl using btree (crt_time); CREATE INDEX
5. 創(chuàng)建空間索引
postgres=# create index idx_tbl_3 on tbl using gist (pos); CREATE INDEX
6. 生成數(shù)據(jù)布局以方便后續(xù)查詢
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. 創(chuàng)建一個(gè)極限KNN查詢函數(shù)
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 ms
8. CTID合并檢索
顯示符合以下條件的記錄
( 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，分別查看每個(gè)條件，找匹配一個(gè)條件的記錄數(shù)量，以及在索引掃描上所花時(shí)長(zhǎng)。
1. 54,907條記錄
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)2. 95,147條記錄
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)
3. 149930條記錄(為快速獲得結(jié)果，PostgreSQL使用位圖進(jìn)行合并掃描)
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)4. 60,687條記錄(即使運(yùn)用出色的KNN性能優(yōu)化，仍然需要耗費(fèi)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)
讓我們看看不使用KNN優(yōu)化需要多長(zhǎng)時(shí)間。
結(jié)果非常令人驚訝——極限優(yōu)化性能提高了一個(gè)數(shù)量級(jí)。
5. 2,640,751條記錄
使用所有索引逐個(gè)掃描數(shù)據(jù)條件，得到ctid并執(zhí)行ctid掃描。
現(xiàn)在，讓我們來(lái)分解這個(gè)過(guò)程：
首先，讓我們看看時(shí)間和對(duì)象屬性的合并查詢，成果非常驚人。使用位圖BitmapOr時(shí)，查詢可以跳過(guò)大多數(shù)數(shù)據(jù)塊，并且掃描時(shí)間比單索引掃描要短。
注意，在此步驟中記錄的數(shù)量減少到7,847條。
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的掃描時(shí)間：
注意，60,687條記錄滿足KNN距離條件，所以接下來(lái)將解釋CTID合并掃描與原始掃描之間的性能比較。
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，將這些片段合并到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
取得最終紀(jì)錄。
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
過(guò)程花費(fèi)462毫秒。
9. 測(cè)試原始SQL查詢的性能: PostgreSQL Multi-Index BitmapAnd and BitmapOr跳過(guò)掃描
直接編寫(xiě)SQL查詢，而不是使用多CTID掃描。
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)性能如預(yù)期的那樣好，之前解釋過(guò)原因。KNN條件以外的條件已經(jīng)將結(jié)果收斂到7,000條記錄，因此沒(méi)有必要使用包含KNN條件的索引。(即使使用KNN索引也需要195毫秒，因?yàn)橛?0,687條記錄滿足KNN條件。)
校驗(yàn)結(jié)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)
分區(qū)索引示例
假設(shè)前面的查詢條件保持不變，使用分區(qū)索引來(lái)測(cè)試性能。
這是為了演示分區(qū)索引的極端效果。在實(shí)際場(chǎng)景中，集合級(jí)別可能沒(méi)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，就可以展現(xiàn)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)
重構(gòu)極值KNN優(yōu)化函數(shù)
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;
查詢性能：
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!查詢時(shí)間從200毫秒減少到1毫秒以內(nèi)。
優(yōu)化方法綜述
優(yōu)化方法回顧：
1. 為不同的數(shù)據(jù)類(lèi)型構(gòu)建不同的索引。
例如，對(duì)空間使用GiST或SP-GiST索引，對(duì)時(shí)間使用B樹(shù)或BRIN索引，對(duì)多個(gè)對(duì)象屬性使用GIN索引。索引的目的是縮小數(shù)據(jù)掃描的范圍。
2. 方法五提到數(shù)據(jù)分區(qū)。
數(shù)據(jù)分區(qū)的目的是有意地組織數(shù)據(jù)，這意味著有意地組織數(shù)據(jù)以滿足搜索需求。例如，如果時(shí)間是必需的查詢條件或公共查詢條件，那么可以按時(shí)間(分區(qū))分割數(shù)據(jù)，以減少需要掃描的數(shù)據(jù)量。
3. 方法六描述了索引分區(qū)。
目的類(lèi)似于方法五。方法五和方法六的區(qū)別在于分區(qū)在索引級(jí)別使用，因此當(dāng)執(zhí)行索引掃描時(shí)，數(shù)據(jù)命中率會(huì)直接提高。
4.方法七中的ctid合并掃描類(lèi)似于PostgreSQL中的多索引bitmapAnd或bitmapOr掃描。
bitmapAnd/bitmapOr跳過(guò)不需要掃描的塊，方法七中的ctid合并掃描跳過(guò)不需要掃描的行。
合并從多個(gè)索引掃描獲得的ctid。跳過(guò)不需要掃描的行數(shù)。
如果當(dāng)其他條件為“AND”時(shí)，過(guò)濾條件可以顯著減少ctid(記錄)，則沒(méi)有必要使用ctid合并掃描。相反，使用FILTER作為另一個(gè)條件。(這將略微增加CPU開(kāi)銷(xiāo)。)
5. 最好的功夫總是以最大的靈活性、自由和對(duì)每一個(gè)動(dòng)作的無(wú)限想象為特征。
PostgreSQL實(shí)現(xiàn)多索引BitmapAnd或BitmapOr掃描，顯著提高了多種條件(索引)下的數(shù)據(jù)命中率。
此外，PostgreSQL具有出色的CBO估計(jì)機(jī)制，它允許PostgreSQL不總是使用位圖合并掃描的所有索引。這也是為什么在“測(cè)試原始SQL查詢的性能——PostgreSQL多索引BitmapAnd位圖或跳過(guò)掃描”一節(jié)中描述的性能更好。
6. 如何實(shí)現(xiàn)極端優(yōu)化
采用方法五或六，并使用可修復(fù)的條件作為分區(qū)鍵來(lái)分區(qū)數(shù)據(jù)或索引。
對(duì)于其他條件，可以使用PostgreSQL中的多索引BitmapAnd或BitmapOr掃描來(lái)提高多條件(索引)的數(shù)據(jù)命中率。
我們可以看到，按照時(shí)間、空間和對(duì)象屬性從5,000萬(wàn)數(shù)據(jù)塊中進(jìn)行多維檢索所需的時(shí)間減少到了0.592毫秒。
7. 對(duì)于空間數(shù)據(jù)，除了使用GiST索引，我們還可以使用BRIN索引，這降低了成本。有條理地組織數(shù)據(jù)后，會(huì)使濾波性能良好。
感謝各位的閱讀，以上就是“數(shù)據(jù)庫(kù)中如何搜索時(shí)空行為數(shù)據(jù)”的內(nèi)容了，經(jīng)過(guò)本文的學(xué)習(xí)后，相信大家對(duì)數(shù)據(jù)庫(kù)中如何搜索時(shí)空行為數(shù)據(jù)這一問(wèn)題有了更深刻的體會(huì)，具體使用情況還需要大家實(shí)踐驗(yàn)證。這里是創(chuàng)新互聯(lián)，小編將為大家推送更多相關(guān)知識(shí)點(diǎn)的文章，歡迎關(guān)注！
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