(新零售)商戶網格化運營 - 阿裡雲RDS PostgreSQL最佳實踐

摘要： 標籤PostgreSQL , PostGIS , 地理位置, KNN , 近鄰取出, 網格取出, polygon中心點, 半徑搜尋幕後偉大的馬老師說：“純電商年齡很快會結束，未來的十年、二十年，沒有電子商務這一說，只有新零售這一說，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”線上是指雲平臺，線下是指銷售門店或生產商，新物流消滅庫存，減少囤貨量。

偉大的馬老師說：

“純電商年齡很快會結束，未來的十年、二十年，沒有電子商務這一說，只有新零售這一說，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”

線上是指雲平臺，線下是指銷售門店或生產商，新物流消滅庫存，減少囤貨量。

電子商務平臺消失是指，現有的電商平臺分散，每個人都有自己的電商平臺，不再入駐天貓、京東、亞馬遜大型電子商務平臺。舉例：每個人在電商平臺都有自己的店鋪，暫留在平臺下進行銷售，只能在一塊水池裡生活，這是很局限性的。

要打通線上線下、消滅庫存，需要發揮資料的價值，比如通過資料預測銷量。同時線上線下對接，也對資料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。

1、支援基於地理位置（GIS）的快速資料取出。

2、支援海量銷售資料的剖析、挖掘。

1、海量的銷量資料通過OSS並行進入到阿裡雲HybridDBfor PostgreSQL資料庫。

2、RDS PostgreSQL負責線上交易處理，網格化運營的手繪多邊形圈選商戶。

3、ETL程式，負責資料調度。

4、BI套用對接HDB for PG和PG，驅動和文法與PostgreSQL相容。

5、HybridDB for PostgreSQL提供高可用、備份的準系統，同時提供了一鍵擴容的功能。使用者不需要擔心未來資料增長的效能壓力。

6、HDB PG和RDS PG可以通過OSS_EXT外部表格外掛程式，透明存取（讀寫）OSS的資料。OSS提供海量共用隱藏，RDSPG和HDB PG之間通過OSS可共用資料，同時OSS還可以作為外部海量資料來源並行匯入到HDBPG的高速通道。OSS還可以作為RDS PG和HDB PG的冷資料存放區。

1 商戶網格搜尋

1 內建幾何類型 商戶網格搜尋 測試

用到內建的polygon, box, circle, point類型，GiST空間索引，<->近鄰排序操作符，@>操作符。

1、建構1億商戶地理位置資料

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、建立空間索引

postgres=# create index idx_pos on pos using gist(pos);

3、建立查詢最佳化函數

輸入手繪多邊形，返回落在多邊形中的商戶。

select * from pos where polygon('((10,2),(-10,-100),(0,10))') @> pos;

如果需要帶其他有條件的空間查詢，可以使用空間複合分區索引（PARTIALINDEX），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

《分區索引的套用和實踐 - 阿裡雲RDSPostgreSQL最佳實踐》

4、空間索引效能驗證，一億資料格查詢約 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空間資料庫 商戶網格搜尋 測試

實際生產上隱藏的是經緯度，用得其他的是PostGIS空間資料庫。前面使用內建幾何類型是為了測試方便。

我們需要用到PostGIS的 商戶網格搜尋 的函數有兩個

http://postgis.net/docs/manual-2.3/ST_Within.html

1、ST_within

ST_Within — Returns true if the geometry A iscompletely inside geometry B

boolean ST_Within(geometry A, geometry B);

Returns TRUE if geometry A is completely insidegeometry B. For this function to make sense, the source geometries must both beof the same coordinate projection, having the same SRID. It is a given that ifST_Within(A,B) is true and ST_Within(B,A) is true, then the two geometries areconsidered spatially equal.

This function call will automatically include abounding box comparison that will make use of any indexes that are available onthe 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 pointsof B lie in the exterior of A, and at least one point of the interior of B liesin the interior of A.

boolean ST_Contains(geometry geomA, geometrygeomB);

Returns TRUE if geometry B is completely insidegeometry A. For this function to make sense, the source geometries must both beof the same coordinate projection, having the same SRID. ST_Contains is theinverse of ST_Within. So ST_Contains(A,B) implies ST_Within(B,A) except in thecase of invalid geometries where the result is always false regardless or notdefined.

This function call will automatically include abounding box comparison that will make use of any indexes that are available onthe 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，資料建構函式ST_PointFromText等。詳見postgis手冊

http://postgis.net/docs/manual-2.3/reference.html

1、建表、建立空間索引

postgres=# create table pos(id int, pos geometry);

CREATE TABLE

postgres=# create index idx_pos on pos using gist(pos);

2、建構1億測試資料

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、商戶網格搜尋 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

執行計畫，使用了空間索引，同時包含了部份遮罩（做法應該和我後面提到的類似，外切圓，按距離輸出，遮罩不在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（PostGIS2.3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以前的組建可以這樣來求半徑，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，同時遮罩不在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 distfrom 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，原因是前面轉換為圓時，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暫時不支援GIST索引的KNNSORT，以及KNN MERGE SORT。

所以我們看到多了一個外排的節點。

不過沒關係只要輸出的結果不多，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 distfrom 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 資料分析效能

資料分析能力如何呢？

這裡有一組單機1TB的TPC-H測試資料，HybridDBfor PostgreSQL是MPP分散式資料庫，可以通過增加節點線性升階效能。

另外還有一些測試資料可以參考如下：

《TPC-H測試 - PostgreSQL10 vs Deepgreen(Greenplum)》

《100TB級, 日增量1TB(100億)的OLTP OLAP多工場景資料庫設計方向》

1、空間索引，GiST索引是PostgreSQL獨有的空間索引，支援精准的距離索引搜尋，同時支援按舉例遠近排序返回結果。效能杠杠的，也是很多科研機構、空間商務的首選。

2、KNN查詢，按距離由近到遠輸出記錄。

3、OSS外部表格，阿裡雲RDS PG和HDB PG增加的功能，與雲端海量物件隱藏OSS打通，在資料庫中以外部表格的形式透明的讀寫OSS中的檔案。可以達到每個執行緒約30MB/s的讀寫頻寬，增加並行即可提高整體的吞吐。

4、ETL，雲端或用戶的ETL程式，只要支援OSS物件串連、PG的連線協定即可。

5、MADlib，是一個開源的機器學習服務庫，支援大多數的學習庫，通過RDSPG，HDB PG的SQL介面實現機器學習服務。

MADlib支援Classification,Regression, Clustering, Topic Modeling, Association Rule Mining, DescriptiveStatistics, Validation等眾多採礦模型。

SHAPE* MERGEFORMAT

http://madlib.incubator.apache.org/product.html

madlib手冊

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

http://postgis.net/docs/manual-2.3/reference.html

7、以上效能測試涉及到的多邊形搜尋是PG 10的測試，如果你發現老組建存在空間索引的效能問題，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，同時遮罩多邊形包含的資料。

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;--強制空間索引, KNN搜尋

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)

阿裡雲 RDSPostgreSQL

阿裡雲 HybridDB for PostgreSQL

阿裡雲 OSS

《(AR虛擬實境)紅包 技術思考 - GIS與影像辨識的完美結合》

《從難纏的柔邊查詢聊開 - PostgreSQL獨門絕招之一 GIN , GiST ,SP-GiST , RUM 索引原理與技術幕後》

《時間、空間、物件多維屬性 海量資料任意多維 高效取出 - 阿裡雲RDSPostgreSQL最佳實踐》

《空間複合索引加速空間搜尋》

《奔跑吧，大屏 - 時間+空間 即時四維資料樞紐》

《視覺挖掘與PostGIS空間資料庫的完美邂逅 - 廣告行銷圈人》

《PostgreSQLGPDB毫秒級海量時空資料樞紐 典型案例分享》

新零售產業，通過打通線上線下、消滅庫存，需要發揮資料的價值，比如通過資料預測銷量。同時線上線下對接，也對資料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。

要求資料庫具備：

1、支援基於地理位置（GIS）的快速資料取出的能力。

2、支援海量銷售資料的剖析、挖掘的能力。

通過阿裡雲的RDS PostgreSQL、HybridDBfor PostgreSQL、OSS，實現了億級地理位置資料一毫秒內回應，同時支援剖析、挖掘需求的全鏈路需求。

1、海量的銷量資料通過OSS並行進入到阿裡雲HybridDBfor PostgreSQL資料庫。

2、RDS PostgreSQL負責線上交易處理，網格化運營的手繪多邊形圈選商戶。

3、ETL程式，負責資料調度。

4、BI套用對接HDB for PG和PG，驅動和文法與PostgreSQL相容。

5、HybridDB for PostgreSQL提供高可用、備份的準系統，同時提供了一鍵擴容的功能。使用者不需要擔心未來資料增長的效能壓力。

6、HDB PG和RDS PG可以通過OSS_EXT外部表格外掛程式，透明存取（讀寫）OSS的資料。OSS提供海量共用隱藏，RDSPG和HDB PG之間通過OSS可共用資料，同時OSS還可以作為外部海量資料來源並行匯入到HDBPG的高速通道。OSS還可以作為RDS PG和HDB PG的冷資料存放區。

《GIS附近尋找效能優化 - PostGISlong lat geometry distance search tuning using gist knn function》

https://www.postgresql.org/docs/9.6/static/functions-geometry.html

http://postgis.net/docs/manual-2.3/reference.html

相關產品：

1.雲資料庫RDS

2.物件隱藏OSS

3.安全管家

4.雲端服務器ECS