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(新零售)商戶網格化運營 - 阿裏雲RDS PostgreSQL最佳實踐
標簽
PostgreSQL , PostGIS , 地理位置 , KNN , 近鄰檢索 , 網格檢索 , polygon中心點 , 半徑搜索
背景
偉大的馬老師說：
“純電商時代很快會結束，未來的十年、二十年，沒有電子商務這一說，隻有新零售這一說，也就是說線上線下和物流必須結合在一起，才能誕生真正的新零售”
線上是指雲平台，線下是指銷售門店或生產商，新物流消滅庫存，減少囤貨量。
電子商務平台消失是指，現有的電商平台分散，每個人都有自己的電商平台，不再入駐天貓、京東、亞馬遜大型電子商務平台。舉例：每個人在電商平台都有自己的店鋪，集中在平台下進行銷售，隻能在一塊水池裏生活，這是很局限性的。
要打通線上線下、消滅庫存，需要發揮數據的價值，比如通過數據預測銷量。同時線上線下對接，也對數據運營產生了新的挑戰，比如基於地理位置的網格化運營由來而生。
一、需求
1、支持基於地理位置（GIS）的快速數據檢索。
2、支持海量銷售數據的分析、挖掘。
二、架構設計
1、海量的銷量數據通過OSS並行進入到阿裏雲HybridDB for PostgreSQL數據庫。
2、RDS PostgreSQL負責在線事務處理，網格化運營的任意多邊形圈選商戶。
3、ETL程序，負責數據調度。
4、BI應用對接HDB for PG和PG，驅動和語法與PostgreSQL兼容。
5、HybridDB for PostgreSQL提供高可用、備份的基本功能，同時提供了一鍵擴容的功能。用戶不需要擔心未來數據增長的性能壓力。
6、HDB PG和RDS PG可以通過OSS_EXT外部表插件，透明訪問（讀寫）OSS的數據。OSS提供海量共享存儲，RDS PG和HDB PG之間通過OSS可共享數據，同時OSS還可以作為外部海量數據來源並行導入到HDB PG的高速通道。OSS還可以作為RDS PG和HDB PG的冷數據存儲。
三、DEMO與性能
1 商戶網格搜索
1 內置幾何類型 商戶網格搜索 測試
用到內置的polygon, box, circle, point類型，GiST空間索引，<->近鄰排序操作符，@>操作符。
1、構造1億商戶地理位置數據
postgres=# create table pos(id int, pos point);
CREATE TABLE
postgres=# insert into pos select generate_series(1,100000000), point(5000-random()*10000, 5000-random()*10000);
INSERT 0 10000000
postgres=# select * from pos limit 10;
id | pos
----+---------------------------------------
1 | (603.396683000028,3740.25050085038)
2 | (4177.6926163584,4295.85348349065)
3 | (-2897.50102907419,4393.90230923891)
4 | (-2756.50105439126,2930.08491862565)
5 | (-1679.21951506287,-2329.10942286253)
6 | (2323.99420812726,-4727.32939757407)
7 | (-1572.33712729067,-3614.81220461428)
8 | (-1383.57343617827,312.93470878154)
9 | (-2942.08695180714,4876.54477357864)
10 | (-2387.8013016656,-141.320424154401)
(10 rows)
2、創建空間索引
postgres=# create index idx_pos on pos using gist(pos);
3、創建查詢優化函數
輸入任意多邊形，返回落在多邊形中的商戶。
select * from pos where polygon('((10,2),(-10,-100),(0,10))') @> pos;
如果需要帶其他條件的空間查詢，可以使用空間複合分區索引（PARTIAL INDEX），例如
create index idx_pos on pos using gist(pos) where 分區條件1;
...
create index idx_pos on pos using gist(pos) where 分區條件n;
詳見
《分區索引的應用和實踐 - 阿裏雲RDS PostgreSQL最佳實踐》
4、空間索引性能驗證，一億數據網格查詢約 0.8 毫秒。
postgres=# explain (analyze,verbose,timing,costs,buffers)
select * from pos where polygon('((10,2),(-10,-100),(0,10))') @> pos;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------
Index Scan using idx_pos on postgres.pos (cost=0.42..123470.72 rows=100000 width=20) (actual time=0.099..0.737 rows=618 loops=1)
Output: id, pos
Index Cond: ('((10,2),(-10,-100),(0,10))'::polygon @> pos.pos)
Buffers: shared hit=660
Planning time: 0.031 ms
Execution time: 0.778 ms
(6 rows)
postgres=# select * from pos where polygon('((10,2),(-10,-100),(0,10))') @> pos;
id | pos
----------+------------------------------------------
14028137 | (-9.47874505072832,-94.8515953496099)
43891480 | (-9.1992225497961,-92.9797394201159)
1247175 | (-0.888188369572163,-28.0744722113013)
4631961 | (-0.548232346773148,-31.1226723715663)
5458615 | (-1.67813152074814,-29.4832326471806)
6057261 | (-0.965241342782974,-24.8730508610606)
......
72818882 | (-0.214213505387306,-38.5544309392571)
84374336 | (-0.350810587406158,-38.3379962295294)
93014418 | (1.69238075613976,-38.5063700377941)
94375565 | (-0.0325776636600494,-43.1329058483243)
(618 rows)
2 PostGIS空間數據庫 商戶網格搜索 測試
實際生產上存儲的是經緯度，用得更多的是PostGIS空間數據庫。前麵使用內置幾何類型是為了測試方便。
我們需要用到PostGIS的 商戶網格搜索 的函數有兩個
https://postgis.net/docs/manual-2.3/ST_Within.html
1、ST_within
ST_Within — Returns true if the geometry A is completely inside geometry B
boolean ST_Within(geometry A, geometry B);
Returns TRUE if geometry A is completely inside geometry B. For this function to make sense, the source geometries must both be of the same coordinate projection, having the same SRID. It is a given that if ST_Within(A,B) is true and ST_Within(B,A) is true, then the two geometries are considered spatially equal.
This function call will automatically include a bounding box comparison that will make use of any indexes that are available on the geometries. To avoid index use, use the function _ST_Within.
-- a circle within a circle
SELECT ST_Within(smallc,smallc) As smallinsmall,
ST_Within(smallc, bigc) As smallinbig,
ST_Within(bigc,smallc) As biginsmall,
ST_Within(ST_Union(smallc, bigc), bigc) as unioninbig,
ST_Within(bigc, ST_Union(smallc, bigc)) as biginunion,
ST_Equals(bigc, ST_Union(smallc, bigc)) as bigisunion
FROM
(
SELECT ST_Buffer(ST_GeomFromText('POINT(50 50)'), 20) As smallc,
ST_Buffer(ST_GeomFromText('POINT(50 50)'), 40) As bigc) As foo;
-- Result
smallinsmall | smallinbig | biginsmall | unioninbig | biginunion | bigisunion
--------------+------------+------------+------------+------------+------------
t | t | f | t | t | t
(1 row)
2、ST_Contains
ST_Contains — Returns true if and only if no points of B lie in the exterior of A, and at least one point of the interior of B lies in the interior of A.
boolean ST_Contains(geometry geomA, geometry geomB);
Returns TRUE if geometry B is completely inside geometry A. For this function to make sense, the source geometries must both be of the same coordinate projection, having the same SRID. ST_Contains is the inverse of ST_Within. So ST_Contains(A,B) implies ST_Within(B,A) except in the case of invalid geometries where the result is always false regardless or not defined.
This function call will automatically include a bounding box comparison that will make use of any indexes that are available on the geometries. To avoid index use, use the function _ST_Contains.
-- A circle within a circle
SELECT ST_Contains(smallc, bigc) As smallcontainsbig,
ST_Contains(bigc,smallc) As bigcontainssmall,
ST_Contains(bigc, ST_Union(smallc, bigc)) as bigcontainsunion,
ST_Equals(bigc, ST_Union(smallc, bigc)) as bigisunion,
ST_Covers(bigc, ST_ExteriorRing(bigc)) As bigcoversexterior,
ST_Contains(bigc, ST_ExteriorRing(bigc)) As bigcontainsexterior
FROM (SELECT ST_Buffer(ST_GeomFromText('POINT(1 2)'), 10) As smallc,
ST_Buffer(ST_GeomFromText('POINT(1 2)'), 20) As bigc) As foo;
-- Result
smallcontainsbig | bigcontainssmall | bigcontainsunion | bigisunion | bigcoversexterior | bigcontainsexterior
------------------+------------------+------------------+------------+-------------------+---------------------
f | t | t | t | t | f
-- Example demonstrating difference between contains and contains properly
SELECT ST_GeometryType(geomA) As geomtype, ST_Contains(geomA,geomA) AS acontainsa, ST_ContainsProperly(geomA, geomA) AS acontainspropa,
ST_Contains(geomA, ST_Boundary(geomA)) As acontainsba, ST_ContainsProperly(geomA, ST_Boundary(geomA)) As acontainspropba
FROM (VALUES ( ST_Buffer(ST_Point(1,1), 5,1) ),
( ST_MakeLine(ST_Point(1,1), ST_Point(-1,-1) ) ),
( ST_Point(1,1) )
) As foo(geomA);
geomtype | acontainsa | acontainspropa | acontainsba | acontainspropba
--------------+------------+----------------+-------------+-----------------
ST_Polygon | t | f | f | f
ST_LineString | t | f | f | f
ST_Point | t | t | f | f
同時還需要用到GiST空間索引，可能用到<->KNN排序操作符，外切圓，圓心等函數，數據構造函數ST_PointFromText等。詳見postgis手冊
https://postgis.net/docs/manual-2.3/reference.html
1、建表、創建空間索引
postgres=# create table pos(id int, pos geometry);
CREATE TABLE
postgres=# create index idx_pos on pos using gist(pos);
2、構造1億測試數據
postgres=# insert into pos select generate_series(1,100000000), ST_PointFromText('POINT('||180-random()*180||' '||90-random()*90||')', 4326);
INSERT 0 100000000
postgres=# select id,st_astext(pos) from pos limit 10;
id | st_astext
----+-------------------------------------------
1 | POINT(33.1504055019468 0.432478752918541)
2 | POINT(21.6662147920579 76.3528884295374)
3 | POINT(23.3734973240644 72.9692681785673)
4 | POINT(24.6665199659765 8.37537375278771)
5 | POINT(42.0769318845123 56.6206424776465)
6 | POINT(151.37722584419 81.2602719990537)
7 | POINT(137.438789913431 52.9424488730729)
8 | POINT(36.6096187848598 87.09903978277)
9 | POINT(3.17187242209911 50.9399658115581)
10 | POINT(177.163629597053 49.6609620703384)
(10 rows)
3、商戶網格搜索 查詢
select id,st_astext(pos) from pos where st_within(
pos,
ST_PolygonFromText('POLYGON((10 10, 20 10, 15 15, 10 10))', 4326)
);
id | st_astext
--------+------------------------------------------
117850 | POINT(19.6388734783977 10.0914861587808)
447534 | POINT(19.6453922521323 10.0930827856064)
735712 | POINT(19.5879830047488 10.1306327059865)
828120 | POINT(19.6418435219675 10.054949526675)
965836 | POINT(19.5668494608253 10.052738590166)
45480 | POINT(18.0746335722506 10.0232297228649)
65043 | POINT(19.3460685387254 10.1494021341205)
......
981674 | POINT(16.9359557982534 10.033694235608)
998555 | POINT(15.9493325371295 10.1035685883835)
999472 | POINT(14.3828116636723 10.001640371047)
(1536 rows)
Time: 11.678 ms
執行計劃，使用了空間索引，同時包含了部分過濾（做法應該和我後麵提到的類似，外切圓，按距離輸出，過濾不在POLYGON內的點）。
postgres=# explain (analyze,verbose,timing,costs,buffers) select id,st_astext(pos) from pos where st_within(
pos,
ST_PolygonFromText('POLYGON((10 10, 20 10, 15 15, 10 10))', 4326)
);
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Index Scan using idx_pos on public.pos (cost=0.29..1219.62 rows=333 width=36) (actual time=0.143..12.563 rows=1536 loops=1)
Output: id, st_astext(pos)
Index Cond: ('0103000020E6100000010000000400000000000000000024400000000000002440000000000000344000000000000024400000000000002E400000000000002E4000000000000024400000000000002440'::geometry ~ pos.pos)
Filter: _st_contains('0103000020E6100000010000000400000000000000000024400000000000002440000000000000344000000000000024400000000000002E400000000000002E4000000000000024400000000000002440'::geometry, pos.pos)
Rows Removed by Filter: 1611
Buffers: shared hit=3151
Planning time: 0.394 ms
Execution time: 12.688 ms
(8 rows)
4、查詢分解
4.1 求包含POLYGON的最小圓(實際上是個多段polygon)
select st_astext(ST_MinimumBoundingCircle('POLYGON((10 10, 20 10, 15 15, 10 10))'::text));
POLYGON((20 10,19.9973229373818 9.83640458589112,19.989294616193 9.67298435384929,19.975923633361 9.5099142983522,19.9572243068691 9.34736903889974,19.9332166604244 9.18552263302706,19.9039264020162 9.02454838991936,19.8693848963867 8.8
6461868482814,19.8296291314453 8.7059047744874,19.784701678661 8.54857661372769,19.7346506474755 8.39280267348419,19.6795296337866 8.23874976039383,19.6193976625564 8.08658283817455,19.5543191246059 7.93646485097803,19.4843637076634 7.78
8556548905,19.4096063217418 7.64301631587001,19.3301270189222 7.5,19.2460109076329 7.35966074674816,19.1573480615127 7.22214883490199,19.0642334229581 7.08761151566099,18.9667667014562 6.9561928549564,18.8650522668137 6.82803357918178,18
.7591990373949 6.70327092449966,18.6493203634892 6.58203848988565,18.5355339059327 6.46446609406727,18.4179615101144 6.35067963651082,18.2967290755003 6.24080096260512,18.1719664208182 6.13494773318632,18.0438071450436 6.03323329854383,1
7.912388484339 5.93576657704193,17.777851165098 5.84265193848728,17.6403392532518 5.75398909236711,17.5 5.66987298107781,17.35698368413 5.59039367825823,17.211443451095 5.51563629233656,17.063535149022 5.44568087539412,16.9134171618255 5
.38060233744357,16.7612502396062 5.32047036621337,16.6071973265158 5.26534935252447,16.4514233862723 5.21529832133896,16.2940952255126 5.17037086855466,16.1353813151719 5.13061510361333,15.9754516100806 5.09607359798385,15.814477366973 5
.06678333957561,15.6526309611003 5.04277569313095,15.4900857016478 5.02407636663902,15.3270156461507 5.01070538380698,15.1635954141089 5.00267706261817,15 5,14.8364045858911 5.00267706261817,14.6729843538493 5.01070538380698,14.509914298
3522 5.02407636663902,14.3473690388998 5.04277569313095,14.1855226330271 5.0667833395756,14.0245483899194 5.09607359798385,13.8646186848281 5.13061510361333,13.7059047744874 5.17037086855466,13.5485766137277 5.21529832133895,13.392802673
4842 5.26534935252447,13.2387497603938 5.32047036621337,13.0865828381746 5.38060233744356,12.936464850978 5.44568087539412,12.788556548905 5.51563629233655,12.64301631587 5.59039367825822,12.5 5.6698729810778,12.3596607467482 5.753989092
3671,12.222148834902 5.84265193848727,12.087611515661 5.93576657704192,11.9561928549564 6.03323329854382,11.8280335791818 6.13494773318631,11.7032709244997 6.24080096260511,11.5820384898856 6.35067963651082,11.4644660940673 6.46446609406
726,11.3506796365108 6.58203848988564,11.2408009626051 6.70327092449966,11.1349477331863 6.82803357918177,11.0332332985438 6.9561928549564,10.9357665770419 7.08761151566099,10.8426519384873 7.22214883490199,10.7539890923671 7.35966074674
817,10.6698729810778 7.50000000000001,10.5903936782582 7.64301631587002,10.5156362923366 7.788556548905,10.4456808753941 7.93646485097804,10.3806023374436 8.08658283817456,10.3204703662134 8.23874976039384,10.2653493525245 8.392802673484
2,10.215298321339 8.5485766137277,10.1703708685547 8.70590477448741,10.1306151036133 8.86461868482815,10.0960735979838 9.02454838991937,10.0667833395756 9.18552263302707,10.0427756931309 9.34736903889976,10.024076366639 9.50991429835222,
10.010705383807 9.6729843538493,10.0026770626182 9.83640458589114,10 10,10.0026770626182 10.1635954141089,10.010705383807 10.3270156461507,10.024076366639 10.4900857016478,10.042775693131 10.6526309611003,10.0667833395756 10.814477366973
,10.0960735979839 10.9754516100807,10.1306151036133 11.1353813151719,10.1703708685547 11.2940952255126,10.215298321339 11.4514233862723,10.2653493525245 11.6071973265158,10.3204703662134 11.7612502396062,10.3806023374436 11.9134171618255
,10.4456808753941 12.063535149022,10.5156362923366 12.211443451095,10.5903936782582 12.35698368413,10.6698729810778 12.5,10.7539890923671 12.6403392532519,10.8426519384873 12.777851165098,10.9357665770419 12.912388484339,11.0332332985438
13.0438071450436,11.1349477331863 13.1719664208183,11.2408009626051 13.2967290755004,11.3506796365109 13.4179615101144,11.4644660940673 13.5355339059328,11.5820384898857 13.6493203634892,11.7032709244997 13.7591990373949,11.828033579181
8 13.8650522668137,11.9561928549564 13.9667667014562,12.087611515661 14.0642334229581,12.222148834902 14.1573480615128,12.3596607467482 14.2460109076329,12.5 14.3301270189222,12.6430163158701 14.4096063217418,12.788556548905 14.484363707
6635,12.9364648509781 14.5543191246059,13.0865828381746 14.6193976625565,13.2387497603939 14.6795296337866,13.3928026734842 14.7346506474755,13.5485766137277 14.7847016786611,13.7059047744875 14.8296291314454,13.8646186848282 14.86938489
63867,14.0245483899194 14.9039264020162,14.1855226330271 14.9332166604244,14.3473690388998 14.9572243068691,14.5099142983523 14.975923633361,14.6729843538493 14.989294616193,14.8364045858912 14.9973229373818,15.0000000000001 15,15.163595
4141089 14.9973229373818,15.3270156461508 14.989294616193,15.4900857016479 14.975923633361,15.6526309611003 14.957224306869,15.814477366973 14.9332166604244,15.9754516100807 14.9039264020161,16.1353813151719 14.8693848963867,16.294095225
5127 14.8296291314453,16.4514233862724 14.784701678661,16.6071973265159 14.7346506474755,16.7612502396062 14.6795296337866,16.9134171618255 14.6193976625564,17.063535149022 14.5543191246058,17.2114434510951 14.4843637076634,17.3569836841
301 14.4096063217417,17.5000000000001 14.3301270189222,17.6403392532519 14.2460109076329,17.7778511650981 14.1573480615127,17.9123884843391 14.064233422958,18.0438071450437 13.9667667014561,18.1719664208183 13.8650522668136,18.2967290755
004 13.7591990373948,18.4179615101144 13.6493203634891,18.5355339059328 13.5355339059327,18.6493203634892 13.4179615101143,18.7591990373949 13.2967290755003,18.8650522668137 13.1719664208182,18.9667667014562 13.0438071450435,19.064233422
9581 12.9123884843389,19.1573480615128 12.7778511650979,19.2460109076329 12.6403392532518,19.3301270189222 12.4999999999999,19.4096063217418 12.3569836841299,19.4843637076635 12.2114434510949,19.5543191246059 12.0635351490219,19.61939766
25565 11.9134171618254,19.6795296337867 11.7612502396061,19.7346506474756 11.6071973265157,19.7847016786611 11.4514233862722,19.8296291314454 11.2940952255125,19.8693848963867 11.1353813151718,19.9039264020162 10.9754516100805,19.9332166
604244 10.8144773669728,19.9572243068691 10.6526309611002,19.975923633361 10.4900857016477,19.989294616193 10.3270156461506,19.9973229373818 10.1635954141088,20 10))
4.2 求包含POLYGON的最小圓的圓心
select st_astext(ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text));
st_astext
----------------------------
POINT(15 11.6666666666667)
(1 row)
4.3 求包含POLYGON的最小圓的圓心、半徑（PostGIS 2.3引入的功能）
SELECT ST_AsText(center), radius FROM ST_MinimumBoundingRadius('POLYGON((26426 65078,26531 65242,26075 65136,26096 65427,26426 65078))');
st_astext | radius
------------------------------------------+------------------
POINT(26284.8418027133 65267.1145090825) | 247.436045591407
2.3以前的版本可以這樣來求半徑，分解步驟如下
1. 外切圓
ST_MinimumBoundingCircle('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)
2. 外切圓的輪廓
ST_Boundary(ST_MinimumBoundingCircle('POLYGON((10 10, 20 10, 15 15, 10 10))'::text))
3. 圓心
ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)
4. 外切圓輪廓離圓心最近的點
ST_ClosestPoint(ST_Boundary(ST_MinimumBoundingCircle('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)), ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text))
5. 外切圓輪廓離圓心最近的點<->圓心的距離(即半徑)
ST_Distance(
ST_ClosestPoint(ST_Boundary(ST_MinimumBoundingCircle('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)), ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text))
,
ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)
)
4.4 距離排序，截止半徑大小，同時過濾不在polygon內的點
postgres=# select id,st_astext(pos),pos<->ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) as dis from pos order by pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) limit 10;
id | st_astext | dis
--------+------------------------------------------+--------------------
690722 | POINT(15.0562715157866 11.711938586086) | 0.0722219442478186
65270 | POINT(15.1074255164713 11.6899066697806) | 0.109910986215585
731760 | POINT(14.8607909493148 11.6321958834305) | 0.143413762872333
312402 | POINT(15.1385483611375 11.7407551081851) | 0.157113676140352
498870 | POINT(14.8981332499534 11.7865430982783) | 0.15731333481142
508068 | POINT(14.9112858809531 11.5105070360005) | 0.179598833059301
357440 | POINT(15.1979697681963 11.6899668937549) | 0.199335886908595
802618 | POINT(14.7933903057128 11.6609365912154) | 0.206689158005409
857485 | POINT(15.0578639935702 11.4632821781561) | 0.211455356859305
395057 | POINT(15.2343154605478 11.6581913502887) | 0.234469065270423
(10 rows)
Time: 0.750 ms
postgres=# explain select id,st_astext(pos),pos<->ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) as dis from pos order by pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) limit 10;
QUERY PLAN
------------------------------------------------------------------------------------
Limit (cost=0.29..0.66 rows=10 width=36)
-> Index Scan using idx_pos on pos (cost=0.29..37485.29 rows=1000000 width=36)
Order By: (pos <-> '01010000000000000000002E405655555555552740'::geometry)
(3 rows)
Time: 0.663 ms
select id, st_astext(pos), pos<->ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) as dis
from pos
where
pos<->ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)
<=
ST_Distance(
ST_ClosestPoint(ST_Boundary(ST_MinimumBoundingCircle('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)), ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text))
,
ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)
)
and
st_within(
pos,
ST_PolygonFromText('POLYGON((10 10, 20 10, 15 15, 10 10))', 4326)
)
order by pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text)
;
id | st_astext | dis
--------+------------------------------------------+--------------------
690722 | POINT(15.0562715157866 11.711938586086) | 0.0722219442478186
65270 | POINT(15.1074255164713 11.6899066697806) | 0.109910986215585
731760 | POINT(14.8607909493148 11.6321958834305) | 0.143413762872333
......
307780 | POINT(18.1456628255546 10.7256762916222) | 3.28339251039349
220569 | POINT(18.2607466075569 11.2290304061025) | 3.28998316913906
651843 | POINT(18.1355669908226 10.6703097978607) | 3.29006167141813
333919 | POINT(11.7259097937495 11.2818037485704) | 3.29663215368952
653102 | POINT(18.2955473475158 11.3890661671758) | 3.3072185623031
143163 | POINT(18.2968946546316 11.3691051676869) | 3.31029507214487
128755 | POINT(12.0493835303932 10.1579119032249) | 3.31398258174282
803774 | POINT(11.9626270607114 10.3377026785165) | 3.31538507246702
477386 | POINT(18.3217689581215 11.5706447605044) | 3.32315726274045
636124 | POINT(18.2356625888497 10.8860507654026) | 3.32849567354729
800873 | POINT(18.2074238732457 10.7637690240517) | 3.33208531471695
443193 | POINT(18.3322164136916 11.6595554212108) | 3.33222575678116
(1360 rows)
Time: 16.899 ms
對比原始方法
postgres=# select id,st_astext(pos),pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) as dist from pos where st_within(
pos,
ST_PolygonFromText('POLYGON((10 10, 20 10, 15 15, 10 10))', 4326)
) order by pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) ;
id | st_astext | dist
--------+------------------------------------------+--------------------
690722 | POINT(15.0562715157866 11.711938586086) | 0.0722219442478186
65270 | POINT(15.1074255164713 11.6899066697806) | 0.109910986215585
731760 | POINT(14.8607909493148 11.6321958834305) | 0.143413762872333
312402 | POINT(15.1385483611375 11.7407551081851) | 0.157113676140352
498870 | POINT(14.8981332499534 11.7865430982783) | 0.15731333481142
。。。。。。
874126 | POINT(19.5105647295713 10.1697491202503) | 4.75246676154538
205780 | POINT(19.5122638251632 10.1721187261865) | 4.75333557456583
470466 | POINT(10.4844846390188 10.0222505908459) | 4.80562042343902
46089 | POINT(10.3699134103954 10.2971605863422) | 4.82837948383337
368116 | POINT(10.3925226721913 10.206622495316) | 4.83327750755081
735712 | POINT(19.5879830047488 10.1306327059865) | 4.83828273014706
965836 | POINT(19.5668494608253 10.052738590166) | 4.84364228928311
890979 | POINT(19.6381107252091 10.1740973582491) | 4.87235434260042
117850 | POINT(19.6388734783977 10.0914861587808) | 4.89901466522263
447534 | POINT(19.6453922521323 10.0930827856064) | 4.9046758233415
223530 | POINT(10.3186767641455 10.1891682296991) | 4.90895020589323
828120 | POINT(19.6418435219675 10.054949526675) | 4.91368787446771
500541 | POINT(19.6874961443245 10.17231578473) | 4.91992923779193
(1536 rows)
Time: 14.855 ms
記錄數有一定的差異，原因是前麵轉換為圓時，實際上是48段的polygon，有一些失真。詳見ST_MinimumBoundingCircle函數
3 HybridDB for PostgreSQL的PostGIS測試
postgres=# create table pos(id int, pos geometry);
postgres=# insert into pos select id, ST_PointFromText('POINT('||180-random()*180||' '||90-random()*90||')', 4326) from generate_series(1,1000000) t(id);
postgres=# create index idx_pos on pos using gist(pos);
GPDB暫時不支持GIST索引的KNN SORT，以及KNN MERGE SORT。
所以我們看到多了一個外排的節點。
不過沒關係隻要輸出的結果不多，排序不是瓶頸。因為st_within還是能用上空間索引的。
postgres=# explain analyze select id,st_astext(pos),pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) as dist from pos where st_within(
pos,
ST_PolygonFromText('POLYGON((10 10, 20 10, 15 15, 10 10))', 4326)
) order by pos <-> ST_Centroid('POLYGON((10 10, 20 10, 15 15, 10 10))'::text) ;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 2:1 (slice1; segments: 2) (cost=600.91..600.92 rows=3 width=36)
Merge Key: dist
Rows out: 1563 rows at destination with 9.586 ms to first row, 10 ms to end, start offset by 0.223 ms.
-> Sort (cost=600.91..600.92 rows=2 width=36)
Sort Key: dist
Rows out: Avg 781.5 rows x 2 workers. Max 814 rows (seg0) with 7.203 ms to first row, 7.271 ms to end, start offset by 1.709 ms.
Executor memory: 145K bytes avg, 145K bytes max (seg0).
Work_mem used: 145K bytes avg, 145K bytes max (seg0). Workfile: (0 spilling, 0 reused)
-> Index Scan using idx_pos on pos (cost=0.00..600.89 rows=2 width=36)
Index Cond: pos && '0103000020E6100000010000000400000000000000000024400000000000002440000000000000344000000000000024400000000000002E400000000000002E4000000000000024400000000000002440'::geometry
Filter: _st_contains('0103000020E6100000010000000400000000000000000024400000000000002440000000000000344000000000000024400000000000002E400000000000002E4000000000000024400000000000002440'::geometry, pos)
Rows out: Avg 781.5 rows x 2 workers. Max 814 rows (seg0) with 0.144 ms to first row, 6.741 ms to end, start offset by 1.721 ms.
Slice statistics:
(slice0) Executor memory: 203K bytes.
(slice1) Executor memory: 442K bytes avg x 2 workers, 442K bytes max (seg0). Work_mem: 145K bytes max.
Statement statistics:
Memory used: 2047000K bytes
Settings: effective_cache_size=8GB; enable_bitmapscan=off; enable_seqscan=off; gp_statistics_use_fkeys=on
Optimizer status: legacy query optimizer
Total runtime: 10.533 ms
(20 rows)
2 數據分析性能
數據分析能力如何呢？
這裏有一組單機1TB的TPC-H測試數據，HybridDB for PostgreSQL是MPP分布式數據庫，可以通過增加節點線性提升性能。
另外還有一些測試數據可以參考如下：
《TPC-H測試 - PostgreSQL 10 vs Deepgreen(Greenplum)》
《100TB級, 日增量1TB(100億)的OLTP OLAP混合場景數據庫設計方向》
四、技術點
1、空間索引，GiST索引是PostgreSQL獨有的空間索引，支持精準的距離索引搜索，同時支持按舉例遠近排序返回結果。性能杠杠的，也是很多科研機構、空間業務的首選。
2、KNN查詢，按距離由近到遠輸出記錄。
3、OSS外部表，阿裏雲RDS PG和HDB PG增加的功能，與雲端海量對象存儲OSS打通，在數據庫中以外部表的形式透明的讀寫OSS中的文件。可以達到每個線程約30MB/s的讀寫帶寬，增加並發即可提高整體的吞吐。
4、ETL，雲端或用戶的ETL程序，隻要支持OSS對象連接、PG的連接協議即可。
5、MADlib，是一個開源的機器學習庫，支持大多數的學習庫，通過RDS PG，HDB PG的SQL接口實現機器學習。
MADlib支持Classification, Regression, Clustering, Topic Modeling, Association Rule Mining, Descriptive Statistics, Validation等眾多挖掘模型。
https://madlib.incubator.apache.org/product.html
6、幾何知識
多邊形的內切圓，circle(polygon)
多邊形BOX和外圓，circle(box(polygon))
PG的幾何函數如下
https://www.postgresql.org/docs/9.6/static/functions-geometry.html
PostGIS的幾何函數如下
https://postgis.net/docs/manual-2.3/reference.html
7、以上性能測試涉及到的多邊形搜索是PG 10的測試，如果你發現老版本存在空間索引的性能問題，可以用以下這個方法進行優化。
首先將多邊形轉換為BOX，再求BOX的外圓，通過KNN索引順序返回記錄，同時過濾多邊形包含的數據。
create or replace function ff(polygon) returns setof record as $$
declare
v_rec record;
cir circle := circle(box($1)); -- 擴散邊界
dist float8 := radius(circle(box($1))); -- 求多邊形外圓的半徑
centrid point := point(circle(box($1))); -- 求多邊形外圓的中心點
begin
set local enable_seqscan=off; -- 強製空間索引, KNN搜索
for v_rec in
select * from pos order by pos <-> centrid
loop
if not cir @> v_rec.pos then
return;
elsif ($1 @> v_rec.pos) then
return next v_rec;
end if;
end loop;
return;
end;
$$ language plpgsql strict volatile;
postgres=# select * from ff(polygon('((10,2),(-10,-100),(0,10))')) as t(id int, pos point);
id | pos
----------+------------------------------------------
36646218 | (-0.0167591497302055,-45.0508715584874)
42498944 | (0.139414332807064,-44.4842409342527)
83455402 | (-0.350065529346466,-44.2021945491433)
......
10828319 | (2.18123663216829,7.54482112824917)
70772435 | (2.13983003050089,8.06822907179594)
79346114 | (2.12917104363441,8.25083814561367)
(618 rows)
五、雲端產品
六、類似場景、案例
《(AR虛擬現實)紅包 技術思考 - GIS與圖像識別的完美結合》
《從難纏的模煳查詢聊開 - PostgreSQL獨門絕招之一 GIN , GiST , SP-GiST , RUM 索引原理與技術背景》
《時間、空間、對象多維屬性 海量數據任意多維 高效檢索 - 阿裏雲RDS PostgreSQL最佳實踐》
《視覺挖掘與PostGIS空間數據庫的完美邂逅 - 廣告營銷\圈人》
《PostgreSQL\GPDB 毫秒級海量時空數據透視 典型案例分享》
七、小結
新零售行業，通過打通線上線下、消滅庫存，需要發揮數據的價值，比如通過數據預測銷量。同時線上線下對接，也對數據運營產生了新的挑戰，比如基於地理位置的網格化運營由來而生。
要求數據庫具備：
1、支持基於地理位置（GIS）的快速數據檢索的能力。
2、支持海量銷售數據的分析、挖掘的能力。
通過阿裏雲的RDS PostgreSQL、HybridDB for PostgreSQL、OSS，實現了億級地理位置數據一毫秒內響應，同時支持分析、挖掘需求的全鏈路需求。
1、海量的銷量數據通過OSS並行進入到阿裏雲HybridDB for PostgreSQL數據庫。
2、RDS PostgreSQL負責在線事務處理，網格化運營的任意多邊形圈選商戶。
3、ETL程序，負責數據調度。
4、BI應用對接HDB for PG和PG，驅動和語法與PostgreSQL兼容。
5、HybridDB for PostgreSQL提供高可用、備份的基本功能，同時提供了一鍵擴容的功能。用戶不需要擔心未來數據增長的性能壓力。
6、HDB PG和RDS PG可以通過OSS_EXT外部表插件，透明訪問（讀寫）OSS的數據。OSS提供海量共享存儲，RDS PG和HDB PG之間通過OSS可共享數據，同時OSS還可以作為外部海量數據來源並行導入到HDB PG的高速通道。OSS還可以作為RDS PG和HDB PG的冷數據存儲。
參考
《GIS附近查找性能優化 - PostGIS long lat geometry distance search tuning using gist knn function》
https://www.postgresql.org/docs/9.6/static/functions-geometry.html
https://postgis.net/docs/manual-2.3/reference.html
最後更新：2017-08-13 22:40:39