Mongo3.2.10
The geo query under dense cluster returns the latest 30 records, and 20,000 records need to be scanned.
For the same point set, try to create 2d index and 2dsphere index query, and the execution efficiency is not much different.
2d index
{
"v" : 1,
"key" : {
"lag" : "2d"
},
"name" : "lag",
"min" : -180,
"ns" : "mobike_default.mBKBikeInfo",
"bits" : 26,
"max" : 180
},db.coll.find({lag:{'$nearSphere':[120.993965,31.449034 ], '$maxDistance':7.83927971443699e-05*2}}).limit(30).explain(true)
MongoDB shell version: 3.2.8
connecting to: 10.108.93.135:7004/admin
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "{db}.{coll}",
"indexFilterSet" : false,
"parsedQuery" : {
"lag" : {
"$nearSphere" : [
120.993965,
31.449034
],
"$maxDistance" : 0.0000783927971443699
}
},
"winningPlan" : {
"stage" : "LIMIT",
"limitAmount" : 30,
"inputStage" : {
"stage" : "GEO_NEAR_2D",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"inputStages" : [
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [
"[BinData(128, E33C11D200000000), BinData(128, E33C11D2FFFFFFFF)]",
"[BinData(128, E33C11D300000000), BinData(128, E33C11D33FFFFFFF)]",
"[BinData(128, E33C11D360000000), BinData(128, E33C11D36FFFFFFF)]",
"[BinData(128, E33C11D370000000), BinData(128, E33C11D37FFFFFFF)]",
"[BinData(128, E33C11D380000000), BinData(128, E33C11D3BFFFFFFF)]",
"[BinData(128, E33C11D3C0000000), BinData(128, E33C11D3FFFFFFFF)]",
"[BinData(128, E33C11D688000000), BinData(128, E33C11D68BFFFFFF)]",
"[BinData(128, E33C11D6A0000000), BinData(128, E33C11D6AFFFFFFF)]",
"[BinData(128, E33C11D800000000), BinData(128, E33C11D8FFFFFFFF)]",
"[BinData(128, E33C11D900000000), BinData(128, E33C11D9FFFFFFFF)]",
"[BinData(128, E33C11DA10000000), BinData(128, E33C11DA1FFFFFFF)]",
"[BinData(128, E33C11DA40000000), BinData(128, E33C11DA7FFFFFFF)]",
"[BinData(128, E33C11DB00000000), BinData(128, E33C11DB3FFFFFFF)]",
"[BinData(128, E33C11DB40000000), BinData(128, E33C11DB7FFFFFFF)]",
"[BinData(128, E33C11DC00000000), BinData(128, E33C11DC3FFFFFFF)]",
"[BinData(128, E33C11DC80000000), BinData(128, E33C11DCBFFFFFFF)]"
]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [ ]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [ ]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [
"[BinData(128, E33C11D340000000), BinData(128, E33C11D34FFFFFFF)]",
"[BinData(128, E33C11D350000000), BinData(128, E33C11D35FFFFFFF)]",
"[BinData(128, E33C11D680000000), BinData(128, E33C11D683FFFFFF)]",
"[BinData(128, E33C11D684000000), BinData(128, E33C11D687FFFFFF)]",
"[BinData(128, E33C11D68C000000), BinData(128, E33C11D68FFFFFFF)]"
]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [ ]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [
"[BinData(128, E33C11CD40000000), BinData(128, E33C11CD7FFFFFFF)]",
"[BinData(128, E33C11D690000000), BinData(128, E33C11D69FFFFFFF)]",
"[BinData(128, E33C11D6B0000000), BinData(128, E33C11D6BFFFFFFF)]",
"[BinData(128, E33C11DA00000000), BinData(128, E33C11DA0FFFFFFF)]",
"[BinData(128, E33C11DA20000000), BinData(128, E33C11DA2FFFFFFF)]",
"[BinData(128, E33C11DA30000000), BinData(128, E33C11DA3FFFFFFF)]"
]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [
"[BinData(128, E33C11C7FC000000), BinData(128, E33C11C7FFFFFFFF)]",
"[BinData(128, E33C11CDD0000000), BinData(128, E33C11CDDFFFFFFF)]",
"[BinData(128, E33C11D62AA00000), BinData(128, E33C11D62AAFFFFF)]",
"[BinData(128, E33C11DE00000000), BinData(128, E33C11DE03FFFFFF)]"
]
}
}
},
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"lag" : [
"[BinData(128, E33C11C7C0000000), BinData(128, E33C11C7CFFFFFFF)]",
"[BinData(128, E33C11C7D0000000), BinData(128, E33C11C7DFFFFFFF)]",
"[BinData(128, E33C11C7E0000000), BinData(128, E33C11C7EFFFFFFF)]",
"[BinData(128, E33C11C7F0000000), BinData(128, E33C11C7F3FFFFFF)]",
"[BinData(128, E33C11C7F4000000), BinData(128, E33C11C7F7FFFFFF)]",
"[BinData(128, E33C11C7F8000000), BinData(128, E33C11C7FBFFFFFF)]",
"[BinData(128, E33C11CDC0000000), BinData(128, E33C11CDCFFFFFFF)]",
"[BinData(128, E33C11CDE0000000), BinData(128, E33C11CDEFFFFFFF)]",
"[BinData(128, E33C11CDF0000000), BinData(128, E33C11CDFFFFFFFF)]",
"[BinData(128, E33C11D620000000), BinData(128, E33C11D623FFFFFF)]",
"[BinData(128, E33C11D624000000), BinData(128, E33C11D627FFFFFF)]",
"[BinData(128, E33C11D628000000), BinData(128, E33C11D628FFFFFF)]",
"[BinData(128, E33C11D629000000), BinData(128, E33C11D629FFFFFF)]",
"[BinData(128, E33C11D62A000000), BinData(128, E33C11D62A3FFFFF)]",
"[BinData(128, E33C11D62A400000), BinData(128, E33C11D62A7FFFFF)]",
"[BinData(128, E33C11D62A800000), BinData(128, E33C11D62A8FFFFF)]",
"[BinData(128, E33C11D62A900000), BinData(128, E33C11D62A9FFFFF)]",
"[BinData(128, E33C11D62AB00000), BinData(128, E33C11D62ABFFFFF)]",
"[BinData(128, E33C11D62AC00000), BinData(128, E33C11D62AFFFFFF)]",
"[BinData(128, E33C11D62B000000), BinData(128, E33C11D62BFFFFFF)]",
"[BinData(128, E33C11D62C000000), BinData(128, E33C11D62FFFFFFF)]",
"[BinData(128, E33C11DA80000000), BinData(128, E33C11DABFFFFFFF)]",
"[BinData(128, E33C11DAC0000000), BinData(128, E33C11DAFFFFFFFF)]",
"[BinData(128, E33C11DB80000000), BinData(128, E33C11DBBFFFFFFF)]",
"[BinData(128, E33C11DE04000000), BinData(128, E33C11DE07FFFFFF)]",
"[BinData(128, E33C11DE08000000), BinData(128, E33C11DE0BFFFFFF)]",
"[BinData(128, E33C11DE0C000000), BinData(128, E33C11DE0FFFFFFF)]"
]
}
}
}
]
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 30,
"executionTimeMillis" : 185,
"totalKeysExamined" : 24613,
"totalDocsExamined" : 24599,
"executionStages" : {
"stage" : "LIMIT",
"nReturned" : 30,
"executionTimeMillisEstimate" : 160,
"works" : 24664,
"advanced" : 30,
"needTime" : 24633,
"needYield" : 0,
"saveState" : 192,
"restoreState" : 192,
"isEOF" : 1,
"invalidates" : 0,
"limitAmount" : 30,
"inputStage" : {
"stage" : "GEO_NEAR_2D",
"nReturned" : 30,
"executionTimeMillisEstimate" : 160,
"works" : 24663,
"advanced" : 30,
"needTime" : 24633,
"needYield" : 0,
"saveState" : 192,
"restoreState" : 192,
"isEOF" : 0,
"invalidates" : 0,
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"searchIntervals" : [
{
"minDistance" : 0,
"maxDistance" : 305.746339475419,
"maxInclusive" : false,
"nBuffered" : 5,
"nReturned" : 5
},
{
"minDistance" : 305.746339475419,
"maxDistance" : 306.16860060551534,
"maxInclusive" : false,
"nBuffered" : 0,
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},
{
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"maxDistance" : 307.01312286570794,
"maxInclusive" : false,
"nBuffered" : 0,
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},
{
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"maxInclusive" : false,
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},
{
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"maxDistance" : 312.0802564268636,
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"nBuffered" : 0,
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},
{
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"maxDistance" : 318.8364345084046,
"maxInclusive" : false,
"nBuffered" : 0,
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},
{
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},
{
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}
],
"inputStages" : [
{
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},
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"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
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"[BinData(128, E33C11DC80000000), BinData(128, E33C11DCBFFFFFFF)]"
]
},
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"dupsTested" : 0,
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}
},
{
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{
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{
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]
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},
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]
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}
},
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]
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}
},
{
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bye
2dsphere index
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"key" : {
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},
"name" : "loc_2dsphere",
"ns" : "mobike_default.kdytbl",
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}db.kdytbl.find({loc:{ $nearSphere: { $geometry: { type : "Point",coordinates : [120.993965,31.449034]},$maxDistance:1000}}}).limit(30).explain(true)MongoDB shell version: 3.2.8
connecting to: 10.108.93.135:7004/admin
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![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
Because 2d index supports spherical query itself, which has big performance problems, I mainly look at the second result using 2dsphere index.
Judging from the query explain results, it works normally. In the figure below, the circle is the maxDistance of the query, and the square is where the data is read. The coordinates in the picture are GPS coordinates. If the data is Mars coordinates, the displayed location will be biased.
It may be that in this 1 kilometer range, the data distribution is very concentrated, but it is far away from the initial point. When $nearSphere initially estimates the point density and cannot find data around it, it will expand to maxDistance. There is no particularly good solution for this. Methods, possible solutions will also degrade the performance of ordinary queries. It is recommended to download MongoDB Compass and take a look at the data distribution of loc to confirm this. Or if the location data within a radius of 5 kilometers can be published, I can reproduce it locally and understand where the problem lies. Generally speaking, if the distribution of the starting points of the query is similar to the data distribution, the results will not be so extreme on average.
Finally, the query of explain is not limit 30, but limit 1, but the nature of the problem should be the same. The version you used was compiled by yourself, and there should be no changes to the 2dshpere related code.
We have always compiled the code ourselves. This version has not modified the 2d or 2dsphere index related logic.
You are right. Our tracking code logic also found that there is a dense point slightly far away from the query point. The algorithm of increasing step size is too wild and jumps directly to the maximum step size.
Whether the abstracted data can be given, I need to apply, but it’s not a big problem.
I have never used mongo compass before. It is such an amazing tool. I will try it out.
The problem with limit 1 and limit 30 may be due to an oversight in the statements I posted when collecting data. However, I have tested both and the results are similar.
In fact, you can consider opening an interface for the fuzzy approximation algorithm, allowing users to choose to give up strict correctness, which will lead to exponential improvements in performance. This is the optimization idea we have here