我使用mongodb存储大量带有2dsphere索引的文档（662430756）。但我的问题是非常缓慢。我使用的是12核Intel（R）Xeon（R）CPU E5-2643 v2 @ 3.50GHz和128 G RAM的专用服务器。我在MongoDB上使用带有XFS和WiredTiger的Ubuntu Server 14.04。
文件架构：
> db.las_prod.findOne()
{
"_id" : ObjectId("56c301386d1b420a58c12534"),
"pointsourceid" : 4083,
"elevation" : 44.01,
"numberofreturns" : 1,
"gpstime" : 27779511.554264,
"returnnumber" : 1,
"red" : 0,
"scananglerank" : -12,
"userdata" : 3,
"geometry" : {
"coordinates" : [
-0.847718518,
44.855975882
],
"type" : "Point"
},
"edgeofflightline" : 0,
"classification" : 1,
"blue" : 0,
"intensity" : 33,
"green" : 0,
"scandirectionflag" : 0
}
我的索引：
> db.las_prod.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "lidar.las_prod"
},
{
"v" : 1,
"key" : {
"geometry" : "2dsphere"
},
"name" : "geometry_2dsphere",
"ns" : "lidar.las_prod",
"2dsphereIndexVersion" : 3
}
]
总索引大小：
> db.las_prod.totalIndexSize()
15326851072
例如：
查询
db.las_prod.find({geometry:{$geoWithin:{$geometry:{type:"Polygon",coordinates:[[[-0.88,44.87],[-0.86,44.87],[-0.86,44.84],[-0.88,44.84],[-0.88,44.87]]]}}}}).explain("executionStats")
结果
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "lidar.las_prod",
"indexFilterSet" : false,
"parsedQuery" : {
"geometry" : {
"$geoWithin" : {
"$geometry" : {
"type" : "Polygon",
"coordinates" : [
[
[
-0.88,
44.87
],
[
-0.86,
44.87
],
[
-0.86,
44.84
],
[
-0.88,
44.84
],
[
-0.88,
44.87
]
]
]
}
}
}
},
"winningPlan" : {
"stage" : "FETCH",
"filter" : {
"geometry" : {
"$geoWithin" : {
"$geometry" : {
"type" : "Polygon",
"coordinates" : [
[
[
-0.88,
44.87
],
[
-0.86,
44.87
],
[
-0.86,
44.84
],
[
-0.88,
44.84
],
[
-0.88,
44.87
]
]
]
}
}
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"geometry" : "2dsphere"
},
"indexName" : "geometry_2dsphere",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"geometry" : [
"[936748722493063168, 936748722493063168]",
"[954763121002545152, 954763121002545152]",
"[959266720629915648, 959266720629915648]",
"[960392620536758272, 960392620536758272]",
"[960603726769291264, 960603726769291264]",
"[960608124815802368, 960608124815802368]",
"[960610495637749761, 960610529997488127]",
"[960610529997488128, 960610529997488128]",
"[960610598716964864, 960610598716964864]",
"[960611423350685696, 960611423350685696]",
"[960612247984406528, 960612247984406528]",
"[960612316703883264, 960612316703883264]",
"[960612316703883265, 960612351063621631]",
"[960612351063621633, 960612359653556223]",
"[960612359653556225, 960612368243490815]",
"[960612368243490816, 960612368243490816]",
"[960612797740220416, 960612797740220416]",
"[960612806330155009, 960612814920089599]",
"[960612814920089600, 960612814920089600]",
"[960612866459697152, 960612866459697152]",
"[960612917999304704, 960612917999304704]",
"[960612917999304705, 960612926589239295]",
"[960612926589239297, 960612935179173887]",
"[960612935179173889, 960612969538912255]",
"[960612969538912257, 960612978128846847]",
"[960612986718781440, 960612986718781440]",
"[960612995308716033, 960613003898650623]",
"[960613003898650624, 960613003898650624]",
"[960613003898650625, 960613012488585215]",
"[960613021078519808, 960613021078519808]",
"[960613029668454401, 960613038258388991]",
"[960613038258388993, 960613072618127359]",
"[960613072618127361, 960613106977865727]",
"[960613106977865729, 960613141337604095]",
"[960613141337604096, 960613141337604096]",
"[960613347496034304, 960613347496034304]",
"[960613553654464512, 960613553654464512]",
"[960613562244399105, 960613570834333695]",
"[960613570834333696, 960613570834333696]",
"[960613570834333697, 960613579424268287]",
"[960613622373941248, 960613622373941248]",
"[960616920908824576, 960616920908824576]",
"[960621318955335680, 960621318955335680]",
"[960674095513468928, 960674095513468928]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 13776129,
"executionTimeMillis" : 93540,
"totalKeysExamined" : 16923284,
"totalDocsExamined" : 16923267,
"executionStages" : {
"stage" : "FETCH",
"filter" : {
"geometry" : {
"$geoWithin" : {
"$geometry" : {
"type" : "Polygon",
"coordinates" : [
[
[
-0.88,
44.87
],
[
-0.86,
44.87
],
[
-0.86,
44.84
],
[
-0.88,
44.84
],
[
-0.88,
44.87
]
]
]
}
}
}
},
"nReturned" : 13776129,
"executionTimeMillisEstimate" : 89200,
"works" : 16923285,
"advanced" : 13776129,
"needTime" : 3147155,
"needYield" : 0,
"saveState" : 132218,
"restoreState" : 132218,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 16923267,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 16923267,
"executionTimeMillisEstimate" : 16230,
"works" : 16923285,
"advanced" : 16923267,
"needTime" : 17,
"needYield" : 0,
"saveState" : 132218,
"restoreState" : 132218,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"geometry" : "2dsphere"
},
"indexName" : "geometry_2dsphere",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"geometry" : [
"[936748722493063168, 936748722493063168]",
"[954763121002545152, 954763121002545152]",
"[959266720629915648, 959266720629915648]",
"[960392620536758272, 960392620536758272]",
"[960603726769291264, 960603726769291264]",
"[960608124815802368, 960608124815802368]",
"[960610495637749761, 960610529997488127]",
"[960610529997488128, 960610529997488128]",
"[960610598716964864, 960610598716964864]",
"[960611423350685696, 960611423350685696]",
"[960612247984406528, 960612247984406528]",
"[960612316703883264, 960612316703883264]",
"[960612316703883265, 960612351063621631]",
"[960612351063621633, 960612359653556223]",
"[960612359653556225, 960612368243490815]",
"[960612368243490816, 960612368243490816]",
"[960612797740220416, 960612797740220416]",
"[960612806330155009, 960612814920089599]",
"[960612814920089600, 960612814920089600]",
"[960612866459697152, 960612866459697152]",
"[960612917999304704, 960612917999304704]",
"[960612917999304705, 960612926589239295]",
"[960612926589239297, 960612935179173887]",
"[960612935179173889, 960612969538912255]",
"[960612969538912257, 960612978128846847]",
"[960612986718781440, 960612986718781440]",
"[960612995308716033, 960613003898650623]",
"[960613003898650624, 960613003898650624]",
"[960613003898650625, 960613012488585215]",
"[960613021078519808, 960613021078519808]",
"[960613029668454401, 960613038258388991]",
"[960613038258388993, 960613072618127359]",
"[960613072618127361, 960613106977865727]",
"[960613106977865729, 960613141337604095]",
"[960613141337604096, 960613141337604096]",
"[960613347496034304, 960613347496034304]",
"[960613553654464512, 960613553654464512]",
"[960613562244399105, 960613570834333695]",
"[960613570834333696, 960613570834333696]",
"[960613570834333697, 960613579424268287]",
"[960613622373941248, 960613622373941248]",
"[960616920908824576, 960616920908824576]",
"[960621318955335680, 960621318955335680]",
"[960674095513468928, 960674095513468928]"
]
},
"keysExamined" : 16923284,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
},
"serverInfo" : {
"host" : "MDB01",
"port" : 27017,
"version" : "3.2.1",
"gitVersion" : "a14d55980c2cdc565d4704a7e3ad37e4e535c1b2"
},
"ok" : 1
}
我不明白为什么在FETCH和IXSCAN执行时之间有72970 ms的差异和总共93540 ms！
我的方法存在一些问题？或者MongoDB对地理空间索引效率不高？为什么需要这么长时间？