Mongo3.2.10
稠密集下geo查询返回最近的30条，需要扫描20000+的记录。
同样的点集，尝试建立2d索引和2dsphere索引查询，执行效率相差不大。
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",
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"[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)]"
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}
}
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2dsphere index
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},
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connecting to: 10.108.93.135:7004/admin
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PHP中文网2017-05-24 11:33:32
因为 2d 索引支持球面查询本身就有很大的性能问题，我主要看第二个用 2dsphere 索引的结果。
从查询explain结果来看，工作是正常的。下图中圆圈是查询的maxDistance，方格是数据被读到的地方。图中的坐标是GPS坐标，如果数据是火星坐标，显示的地方会有偏差。
可能是在这1公里范围里，数据分布很集中，但是离初始点比较远，$nearSphere 在最初估计点密度时在周围找不到数据，就会扩大到 maxDistance，对于这一点没有特别好的办法，可能的解决方案也会让普通的查询性能下降。建议下载 MongoDB Compass，看一下 loc 的数据分布，确认这一点。或者如果能把方圆5公里的位置数据公布出来，我可以在本地复现，就能明白问题到底在哪里了。一般来说，如果查询的起始点的分布与数据分布相似，平均来看，结果不会这么极端。
最后，explain 的查询不是 limit 30，而是 limit 1，不过问题性质应该是一样的。使用的版本是你自己编译的，应该没有改2dshpere相关的代码吧。
世界只因有你2017-05-24 11:33:32
我们这边一直是自己编译的代码。这个版本是没有修改过2d 或者 2dsphere 索引相关逻辑的。
你说的没错，我们跟踪代码逻辑也是发现离查询点距离稍微远有一坨很密集的点。而步长递增的算法太狂野直接就跳到最大步长了。
抽象后的数据是否能够给出，我需要申请一下，不过问题不大。
mongo compass没有用过，如此神器，我学习一下。
limit 1 和limit 30 的问题，可能是我在采集数据时贴的语句疏忽了。不过这两种都测过，结果差不多的。
其实可以考虑开放一个模糊近似算法的接口，让用户选择放弃严格的正确性，带来性能上的成倍提升。我们这边是这么个优化的思路
