mongodb的地理位置索引
mongoDB支持二维空间索引,使用空间索引,mongoDB支持一种特殊查询,如某地图网站上可以查找离你最近的咖啡厅,银行等信息。这个使用mongoDB的空间索引结合特殊的查询方法很容易实现。
前提条件:
建立空间索引的key可以使用array或内嵌文档存储,但是前两个elements必须存储固定的一对空间位置数值。如
{ loc : [ 50 , 30 ] }
{ loc : { x : 50 , y : 30 } }
{ loc : { foo : 50 , y : 30 } }
{ loc : { lat : 40.739037, long: 73.992964 } }
# 使用范例1:
> db.mapinfo.drop()
true
> db.mapinfo.insert({"category" : "coffee","name" : "digoal coffee bar","loc" : [70,80]})
> db.mapinfo.insert({"category" : "tea","name" : "digoal tea bar","loc" : [70,80]})
> db.mapinfo.insert({"category" : "tea","name" : "hangzhou tea bar","loc" : [71,81]})
> db.mapinfo.insert({"category" : "coffee","name" : "hangzhou coffee bar","loc" : [71,81]})
# 未创建2d索引时，不可以使用$near进行查询
> db.mapinfo.find({loc : {$near : [50,50]}})
error: {
"$err" : "can't find special index: 2d for: { loc: { $near: [ 50.0, 50.0 ] } }",
"code" : 13038
}
# 在loc上面创建2d索引
> db.mapinfo.ensureIndex({"loc" : "2d"},{"background" : true})
> db.mapinfo.getIndexes()
[
{
"name" : "_id_",
"ns" : "test.mapinfo",
"key" : {
"_id" : 1
}
},
{
"_id" : ObjectId("4d242e1f3238ba30f9ca05ad"),
"ns" : "test.mapinfo",
"key" : {
"loc" : "2d"
},
"name" : "loc_",
"background" : true
}
]
# 查询测试,返回结果按照从最近到最远的顺序排序输出.
> db.mapinfo.find({loc : {$near : [72,82]},"category" : "coffee"}).explain()
{
"cursor" : "GeoSearchCursor",
"nscanned" : 2,
"nscannedObjects" : 2,
"n" : 2,
"millis" : 0,
"indexBounds" : {
}
}
> db.mapinfo.find({loc : {$near : [72,82]},"category" : "coffee"})
{ "_id" : ObjectId("4d242dce3238ba30f9ca05ac"), "category" : "coffee", "name" : "hangzhou coffee bar", "loc" : [ 71, 81 ] }
{ "_id" : ObjectId("4d242d8b3238ba30f9ca05a9"), "category" : "coffee", "name" : "digoal coffee bar", "loc" : [ 70, 80 ] }
# 换一个经纬度后结果相反.
> db.mapinfo.find({loc : {$near : [69,69]},"category" : "coffee"})
{ "_id" : ObjectId("4d242d8b3238ba30f9ca05a9"), "category" : "coffee", "name" : "digoal coffee bar", "loc" : [ 70, 80 ] }
{ "_id" : ObjectId("4d242dce3238ba30f9ca05ac"), "category" : "coffee", "name" : "hangzhou coffee bar", "loc" : [ 71, 81 ] }
# 2d默认取值范围[-179,-179]到[180,180] 包含这两个点，超出范围将报错
> db.mapinfo.insert({"category" : "bank","name" : "china people bank","loc" : [181,181]})
point not in range
> db.mapinfo.insert({"category" : "bank","name" : "china people bank","loc" : [-179,-180]})
in > 0
# 如果已经存在超过范围的值，建2D索引将报错
> db.mapinfo.insert({"category" : "bank","name" : "china people bank","loc" : [-180,-180]})
> db.mapinfo.ensureIndex({"loc" : "2d"})
in > 0
# 在建2d索引的时候可以指定取值范围
# 如,以上包含了[-180,-180]这个点之后,建2d索引将报错,使用以下解决.或者把这条记录先处理掉.
# 在限制条件下,min不包含,max包含,从下面建索引的语句中可以看出.
> db.mapinfo.ensureIndex({"loc" : "2d"},{min:-181,max:180})
> 成功
# 注意官方文档上说you can only have 1 geo2d index per collection right now,不过测试可以建多个，如下
> db.mapinfo.drop()
true
> db.mapinfo.insert({"category" : "bank","name" : "china people bank","loc" : [71,81],"HQ_loc" : [91,101]})
> db.mapinfo.ensureIndex({"loc" : "2d"},{"background" : "true"})
> db.mapinfo.ensureIndex({"HQ_loc" : "2d"},{"background" : "true"})
> db.mapinfo.getIndexes()
[
{
"name" : "_id_",
"ns" : "test.mapinfo",
"key" : {
"_id" : 1
}
},
{
"_id" : ObjectId("4d2439803238ba30f9ca05cd"),
"ns" : "test.mapinfo",
"key" : {
"loc" : "2d"
},
"name" : "loc_",
"background" : "true"
},
{
"_id" : ObjectId("4d2439863238ba30f9ca05ce"),
"ns" : "test.mapinfo",
"key" : {
"HQ_loc" : "2d"
},
"name" : "HQ_loc_",
"background" : "true"
}
]
> db.mapinfo.find({"loc" : {"$near" : [20,21]}})
{ "_id" : ObjectId("4d2439643238ba30f9ca05cc"), "category" : "bank", "name" : "china people bank", "loc" : [ 71, 81 ], "HQ_loc" : [ 91, 101 ] }
> db.mapinfo.find({"HQ_loc" : {"$near" : [20,21]}})
{ "_id" : ObjectId("4d2439643238ba30f9ca05cc"), "category" : "bank", "name" : "china people bank", "loc" : [ 71, 81 ], "HQ_loc" : [ 91, 101 ] }
# 使用范例2:
# 测试数据
> db.mapinfo.find()
{ "_id" : ObjectId("4d2439643238ba30f9ca05cc"), "category" : "bank", "name" : "china people bank", "loc" : [ 71, 81 ], "HQ_loc" : [ 91, 101 ] }
{ "_id" : ObjectId("4d243a743238ba30f9ca05cf"), "category" : "coffee", "name" : "digoal coffee bar", "loc" : [ 100, 81 ], "HQ_loc" : [ 100, 101 ] }
{ "_id" : ObjectId("4d243a8b3238ba30f9ca05d0"), "category" : "tea", "name" : "digoal tea bar", "loc" : [ 110, 81 ], "HQ_loc" : [ 110, 101 ] }
{ "_id" : ObjectId("4d243ab23238ba30f9ca05d1"), "category" : "shop", "name" : "digoal supermarket", "loc" : [ 120, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aba3238ba30f9ca05d2"), "category" : "shop", "name" : "digoal supermarket1", "loc" : [ 121, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243abe3238ba30f9ca05d3"), "category" : "shop", "name" : "digoal supermarket2", "loc" : [ 122, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243ac33238ba30f9ca05d4"), "category" : "shop", "name" : "digoal supermarket3", "loc" : [ 123, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243ac83238ba30f9ca05d5"), "category" : "shop", "name" : "digoal supermarket4", "loc" : [ 124, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243ace3238ba30f9ca05d6"), "category" : "shop", "name" : "digoal supermarket5", "loc" : [ 125, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243ad63238ba30f9ca05d7"), "category" : "shop", "name" : "digoal supermarket6", "loc" : [ 126, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aee3238ba30f9ca05d8"), "category" : "shop", "name" : "digoal supermarket7", "loc" : [ 26, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af43238ba30f9ca05d9"), "category" : "shop", "name" : "digoal supermarket8", "loc" : [ 27, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af93238ba30f9ca05da"), "category" : "shop", "name" : "digoal supermarket9", "loc" : [ 29, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aff3238ba30f9ca05db"), "category" : "shop", "name" : "digoal supermarket10", "loc" : [ 30, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243b063238ba30f9ca05dc"), "category" : "shop", "name" : "digoal supermarket11", "loc" : [ 31, 81 ], "HQ_loc" : [ 120, 101 ] }
# 索引
> db.mapinfo.getIndexes()
[
{
"name" : "_id_",
"ns" : "test.mapinfo",
"key" : {
"_id" : 1
}
},
{
"_id" : ObjectId("4d2439803238ba30f9ca05cd"),
"ns" : "test.mapinfo",
"key" : {
"loc" : "2d"
},
"name" : "loc_",
"background" : "true"
},
{
"_id" : ObjectId("4d2439863238ba30f9ca05ce"),
"ns" : "test.mapinfo",
"key" : {
"HQ_loc" : "2d"
},
"name" : "HQ_loc_",
"background" : "true"
}
]
# 查询离[50,50]最近的5家商店
> db.mapinfo.find({"loc" : {"$near" : [50,50]},"category" : "shop"}).limit(5)
{ "_id" : ObjectId("4d243b063238ba30f9ca05dc"), "category" : "shop", "name" : "digoal supermarket11", "loc" : [ 31, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aff3238ba30f9ca05db"), "category" : "shop", "name" : "digoal supermarket10", "loc" : [ 30, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af93238ba30f9ca05da"), "category" : "shop", "name" : "digoal supermarket9", "loc" : [ 29, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af43238ba30f9ca05d9"), "category" : "shop", "name" : "digoal supermarket8", "loc" : [ 27, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aee3238ba30f9ca05d8"), "category" : "shop", "name" : "digoal supermarket7", "loc" : [ 26, 81 ], "HQ_loc" : [ 120, 101 ] }
# 找出限制离[50,50]在37 的商店,使用maxDistance
> db.mapinfo.find({"loc" : {"$near" : [50,50], "$maxDistance" : 37},"category" : "shop"})
{ "_id" : ObjectId("4d243b063238ba30f9ca05dc"), "category" : "shop", "name" : "digoal supermarket11", "loc" : [ 31, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aff3238ba30f9ca05db"), "category" : "shop", "name" : "digoal supermarket10", "loc" : [ 30, 81 ], "HQ_loc" : [ 120, 101 ] }
# 复合索引
> db.mapinfo.ensureIndex({"loc" : "2d","category" : 1})
> db.mapinfo.getIndexes()
[
{
"name" : "_id_",
"ns" : "test.mapinfo",
"key" : {
"_id" : 1
}
},
{
"_id" : ObjectId("4d2439803238ba30f9ca05cd"),
"ns" : "test.mapinfo",
"key" : {
"loc" : "2d"
},
"name" : "loc_",
"background" : "true"
},
{
"_id" : ObjectId("4d2439863238ba30f9ca05ce"),
"ns" : "test.mapinfo",
"key" : {
"HQ_loc" : "2d"
},
"name" : "HQ_loc_",
"background" : "true"
},
{
"_id" : ObjectId("4d243ce13238ba30f9ca05dd"),
"ns" : "test.mapinfo",
"key" : {
"loc" : "2d",
"category" : 1
},
"name" : "loc__category_1"
}
]
3. 范例 3
# 除了使用find来搜索以外,还可以使用runCommand
> db.runCommand({"geoNear" : "mapinfo","near" : [50,50],"num" : 10})
{ "errmsg" : "more than 1 geo indexes :(", "ok" : 0 }
# 这里报错，原因是mapinfo超过一个2d索引,但是使用find来查询不会报错,
# 只保留一个“2d"索引后，使用runCommand正常
> db.mapinfo.dropIndex({"loc" : "2d","category" : 1})
{ "nIndexesWas" : 4, "ok" : 1 }
> db.runCommand({"geoNear" : "mapinfo","near" : [50,50],"num" : 10})
{ "errmsg" : "more than 1 geo indexes :(", "ok" : 0 }
> db.mapinfo.dropIndex({"HQ_loc" : "2d"})
{ "nIndexesWas" : 3, "ok" : 1 }
# "num" 限制返回的记录数
# 使用runCommand和geoNear的好处是可以返回距离.本例"dis" : 36.3593194466869,
> db.runCommand({"geoNear" : "mapinfo","near" : [50,50],"num" : 1})
{
"ns" : "test.mapinfo",
"near" : "1100110000001111110000001111110000001111110000001111",
"results" : [
{
"dis" : 36.3593194466869,
"obj" : {
"_id" : ObjectId("4d243b063238ba30f9ca05dc"),
"category" : "shop",
"name" : "digoal supermarket11",
"loc" : [
31,
81
],
"HQ_loc" : [
120,
101
]
}
}
],
"stats" : {
"time" : 0,
"btreelocs" : 6,
"nscanned" : 7,
"objectsLoaded" : 3,
"avgDistance" : 36.3593194466869,
"maxDistance" : 36.3593194466869
},
"ok" : 1
}
# 使用runCommand同样也可以使用普通的FIND的限制条件,如下放在query : { "category" : "coffee" }
> db.runCommand({"geoNear" : "mapinfo","near" : [50,50],"num" : 1,query : { "category" : "coffee" }})
{
"ns" : "test.mapinfo",
"near" : "1100110000001111110000001111110000001111110000001111",
"results" : [
{
"dis" : 58.830266786369556,
"obj" : {
"_id" : ObjectId("4d243a743238ba30f9ca05cf"),
"category" : "coffee",
"name" : "digoal coffee bar",
"loc" : [
100,
81
],
"HQ_loc" : [
100,
101
]
}
}
],
"stats" : {
"time" : 0,
"btreelocs" : 15,
"nscanned" : 15,
"objectsLoaded" : 7,
"avgDistance" : 58.830266786369556,
"maxDistance" : 58.830266786369556
},
"ok" : 1
}
4. 范例4
# 空间索引还支持范围搜索，目前支持圆和矩阵的范围
# 使用box
> box = [[19,19],[90,90]]
[ [ 19, 19 ], [ 90, 90 ] ]
> db.mapinfo.find({"loc" : {"$within" : {"$box" : box}}})
{ "_id" : ObjectId("4d2439643238ba30f9ca05cc"), "category" : "bank", "name" : "china people bank", "loc" : [ 71, 81 ], "HQ_loc" : [ 91, 101 ] }
{ "_id" : ObjectId("4d243b063238ba30f9ca05dc"), "category" : "shop", "name" : "digoal supermarket11", "loc" : [ 31, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aff3238ba30f9ca05db"), "category" : "shop", "name" : "digoal supermarket10", "loc" : [ 30, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af93238ba30f9ca05da"), "category" : "shop", "name" : "digoal supermarket9", "loc" : [ 29, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af43238ba30f9ca05d9"), "category" : "shop", "name" : "digoal supermarket8", "loc" : [ 27, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aee3238ba30f9ca05d8"), "category" : "shop", "name" : "digoal supermarket7", "loc" : [ 26, 81 ], "HQ_loc" : [ 120, 101 ] }
# 使用center point and radius
> center = [29,81]
[ 29, 81 ]
> radius = 10
10
> db.mapinfo.find({"loc" : {"$within" : {"$center" : [center,radius]}}})
{ "_id" : ObjectId("4d243af93238ba30f9ca05da"), "category" : "shop", "name" : "digoal supermarket9", "loc" : [ 29, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243af43238ba30f9ca05d9"), "category" : "shop", "name" : "digoal supermarket8", "loc" : [ 27, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aff3238ba30f9ca05db"), "category" : "shop", "name" : "digoal supermarket10", "loc" : [ 30, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243b063238ba30f9ca05dc"), "category" : "shop", "name" : "digoal supermarket11", "loc" : [ 31, 81 ], "HQ_loc" : [ 120, 101 ] }
{ "_id" : ObjectId("4d243aee3238ba30f9ca05d8"), "category" : "shop", "name" : "digoal supermarket7", "loc" : [ 26, 81 ], "HQ_loc" : [ 120, 101 ] }
注意事项:
1. mongoDB处理的是平面距离，但是实际生活中如果涉及到大范围的距离搜索，可能会有偏差，因为地球是球型的。The current implementation assumes an idealized model of a flat earth, meaning that an arcdegree of latitude (y) and longitude (x) represent the same distance everywhere. This is only true at the equator where they are both about equal to 69 miles or 111km. However, at the 10gen offices at { x : -74 , y : 40.74 } one arcdegree of longitude is about 52 miles or 83 km (latitude is unchanged). This means that something 1 mile to the north would seem closer than something 1 mile to the east.
2. 2d索引目前还不支持sharding,In the meantime sharded clusters can use geospatial indexes for unsharded collections within the cluster.
3. New Spherical Model,1.7.0以后将引入新的空间模型.
其他:
The current implementation encodes geographic hash codes atop standard MongoDB b-trees. Results of $near queries are exact. The problem with geohashing is that prefix lookups don't give you exact results, especially around bit flip areas. MongoDB solves this by doing a grid by grid search after the initial prefix scan. This guarantees performance remains very high while providing correct results
mongodb的地理位置索引的更多相关文章
- Mongodb添加地理位置索引
1.同步站点信息到mongo中(支持mysql.sqlserver数据同步) 2.在Collections文件夹下所在文档右键,在菜单中选择Add Index, 3.然后进行数据查询{ "m ...
- 地理位置索引 2d索引
地址位置索引:将一些点的位置存储在mongodb中,创建索引后,可以按照位置来查找其他点 子分类: .2d索引:平面地理位置索引,用于存储和查找平面上的点. .2dsphere索引:球面地理位置索引, ...
- 图解 MongoDB 地理位置索引的实现原理
地理位置索引支持是MongoDB的一大亮点,这也是全球最流行的LBS服务foursquare 选择MongoDB的原因之一.我们知道,通常的数据库索引结构是B+ Tree,如何将地理位置转化为可建立B ...
- 图解 MongoDB 地理位置索引的实现原理（转）
原文链接:图解 MongoDB 地理位置索引的实现原理 地理位置索引支持是MongoDB的一大亮点,这也是全球最流行的LBS服务foursquare 选择MongoDB的原因之一.我们知道,通常的数据 ...
- MongoDB数据模型和索引学习总结
MongoDB数据模型和索引学习总结 1. MongoDB数据模型: MongoDB数据存储结构: MongoDB针对文档(大文件採用GridFS协议)採用BSON(binary json,採用二进制 ...
- MongoDB学习笔记~索引提高查询效率
回到目录 索引这个东西大家不会陌生,只要接触到稍微大一点的数据,都会用到这东西,它可以提升查询的速度,相当代价就是占用了更多的存储空间,这也是正常的,符合“能量守恒定理”,哈哈!今天说的是MongoD ...
- MongoDB学习笔记(索引)
一.索引基础: MongoDB的索引几乎与传统的关系型数据库一模一样,这其中也包括一些基本的优化技巧.下面是创建索引的命令: > db.test.ensureIndex({" ...
- MongoDB的学习--索引
索引可以用来优化查询,而且在某些特定类型的查询中,索引是必不可少的.为集合选择合适的索引是提高性能的关键. 先来mock数据 for (i = 0; i < 1000000; i++) { db ...
- MongoDB学习笔记(索引)（转）
一.索引基础: MongoDB的索引几乎与传统的关系型数据库一模一样,这其中也包括一些基本的优化技巧.下面是创建索引的命令: > db.test.ensureIndex({" ...
随机推荐
- [jobdu]用两个栈实现队列
思路比较简单.就是当要pop的时候,如果s2为空,才把s1的转过来.总之就是区分一下此时s2为空和非空的情况. http://ac.jobdu.com/problem.php?pid=1512 #in ...
- leetcode面试准备:Triangle
leetcode面试准备:Triangle 1 题目 Given a triangle, find the minimum path sum from top to bottom. Each step ...
- IPv6 tutorial 1 Get started now
https://4sysops.com/archives/ipv6-part-1-get-started-now/ You’ve probably heard the news that the In ...
- 【转】Beyond Compare for Mac中文版震撼来袭！-- 不错
原文网址:http://mt.sohu.com/20160329/n442685522.shtml Beyond Compare想必大家都知道,它是一个专业级的一个文件对比工具,由于工作原因,我们会经 ...
- 让人爱不释手的13套精美 Web 应用程序图标素材（转）
图标用于向用户传递信息,不管是在网页还是 Web 应用程序中都非常需要.这些小小的图标元素能够告诉用户怎么到下一页,如何添加.删除和取消等等各种操作.设计精美的图标不仅能增加界面的美观,也能够让应用程 ...
- OpenXml操作Word的一些操作总结．无word组件生成word.
OpenXml相对于用MS提供的COM组件来生成WORD,有如下优势: 1.相对于MS 的COM组件,因为版本带来的不兼容问题,及各种会生成WORD半途会崩溃的问题. 2.对比填满一张30多页的WOR ...
- CLR C++ Set Word CustomDocumentProperties
// WordIssue.cpp : main project file. #include "stdafx.h" using namespace System; using na ...
- 让ASP.NET MVC页面返回不同类型的内容
在ASP.NET MVC的controller中大部分方法返回的都是ActionResult,更确切的是ViewResult.它返回了一个View,一般情况下是一个HTML页面.但是在某些情况下我们可 ...
- POJ2492 A Bug's Life 带权并查集
分析:所谓带权并查集,就是比朴素的并查集多了一个数组,记录一些东西,例如到根的距离,或者和根的关系等 这个题,权数组为relation 代表的关系 1 和父节点不同性别,0,和父节点同性别 并查集一 ...
- Fiddler On Linux
参考链接: http://www.development-cycle.com/2013/08/debugging-web-applications-with-fiddler-on-linux/ htt ...