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

时间： 2024-10-29 19:11:39

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MongoDB中的索引和其他数据库索引类似,也是使用B-Tree结构.MongoDB的索引是在collection级别上的,并且支持在任何列或者集合内的文档的子列中创建索引. 下面是官方给出的一个使用索引查询和排序的一个结构图. 所有的MongoDB集合默认都有一个唯一索引在字段"_id"上,如果应用程序没有为 "_id"列定义一个值,MongoDB将创建一个带有ObjectId值的列.(ObjectId是基于 时间.计算机ID.进程ID.本地进程计数器 生成的)

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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MongoDB里的索引,在我进行对500万数据进行查询测试时,发现如果你的查询字段不加索引,那是相当恐怖的,一个简单的查询(单字段)要耗时30多秒,这种操作,基本可以认为服务器挂了,哈哈!当为字段加了索引之后,查询速度为ms级,100毫秒以内的速度真是把经兴奋坏了,呵呵! 建立索引 db.tableName.ensure

mongodb学习(六)索引

准备工作: 先插入100万条数据 for(i=0;i<=1000000;i++){ db.users.insert({ "i":i, "username":"user"+i, "age":Math.floor(Math.random()*120), "created":new Date() }) } 1. 创建索引: 数据量越大创建索引时间越长 db.users.ensureIndex({"