MongoDB索引类型汇总分享
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MongoDB 4.2官方支持索引类型如下：
- 单字段索引
- 复合索引
- 多键索引
- 文本索引
- 2dsphere索引
- 2d索引
- geoHaystack索引
- 哈希索引
单字段索引
在单个字段上创建升序索引
handong1:PRIMARY> db.test.getIndexes()
[
{
"v" : 2,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "db6.test"
}
]
在字段id上添加升序索引
handong1:PRIMARY> db.test.createIndex({"id":1})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1,
"$clusterTime" : {
"clusterTime" : Timestamp(1621322378, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
},
"operationTime" : Timestamp(1621322378, 1)
}
handong1:PRIMARY> db.test.getIndexes()
[
{
"v" : 2,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "db6.test"
},
{
"v" : 2,
"key" : {
"id" : 1
},
"name" : "id_1",
"ns" : "db6.test"
}
]
handong1:PRIMARY> db.test.find({"id":100})
{ "_id" : ObjectId("60a35d061f183b1d8f092114"), "id" : 100, "name" : "handong", "ziliao" : { "name" : "handong", "age" : 25, "hobby" : "mongodb" } }
上述查询可以使用新建的单字段索引。
在嵌入式字段上创建索引
handong1:PRIMARY> db.test.createIndex({"ziliao.name":1})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 2,
"numIndexesAfter" : 3,
"ok" : 1,
"$clusterTime" : {
"clusterTime" : Timestamp(1621323677, 2),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
},
"operationTime" : Timestamp(1621323677, 2)
}
以下查询可以用的新建的索引。
db.test.find({"ziliao.name":"handong"})
在内嵌文档上创建索引
handong1:PRIMARY> db.test.createIndex({ziliao:1})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 3,
"numIndexesAfter" : 4,
"ok" : 1,
"$clusterTime" : {
"clusterTime" : Timestamp(1621324059, 2),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
},
"operationTime" : Timestamp(1621324059, 2)
}
以下查询可以使用新建的索引。
db.test.find({ziliao:{ "name" : "handong", "age" : 25, "hobby" : "mongodb" }})
复合索引
创建复合索引
db.user.createIndex({"product_id":1,"type":-1})
以下查询可以用到新建的复合索引
db.user.find({"product_id":"e5a35cfc70364d2092b8f5d14b1a3217","type":0})
多键索引
基于一个数组创建索引，MongoDB会自动创建为多键索引，无需刻意指定。
多键索引也可以基于内嵌文档来创建。
多键索引的边界值的计算依赖于特定的规则。
查看文档：
handong1:PRIMARY> db.score.find()
{ "_id" : ObjectId("60a32d7f1f183b1d8f0920ad"), "name" : "dandan", "age" : 30, "score" : [ { "english" : 90, "math" : 99, "physics" : 88 } ], "is_del" : false }
{ "_id" : ObjectId("60a32d8b1f183b1d8f0920ae"), "name" : "dandan", "age" : 30, "score" : [ 99, 98, 97, 96 ], "is_del" : false }
{ "_id" : ObjectId("60a32d9a1f183b1d8f0920af"), "name" : "dandan", "age" : 30, "score" : [ 100, 100, 100, 100 ], "is_del" : false }
{ "_id" : ObjectId("60a32e8c1f183b1d8f0920b0"), "name" : "dandan", "age" : 30, "score" : [ { "english" : 70, "math" : 99, "physics" : 88 } ], "is_del" : false }
{ "_id" : ObjectId("60a37b141f183b1d8f0aa751"), "name" : "dandan", "age" : 30, "score" : [ 96, 95 ] }
{ "_id" : ObjectId("60a37b1d1f183b1d8f0aa752"), "name" : "dandan", "age" : 30, "score" : [ 96, 95, 94 ] }
{ "_id" : ObjectId("60a37b221f183b1d8f0aa753"), "name" : "dandan", "age" : 30, "score" : [ 96, 95, 94, 93 ] }
创建score字段多键索引：
db.score.createIndex("score":1)
handong1:PRIMARY> db.score.find({"score":[ 96, 95 ]})
{ "_id" : ObjectId("60a37b141f183b1d8f0aa751"), "name" : "dandan", "age" : 30, "score" : [ 96, 95 ] }
查看执行计划：
handong1:PRIMARY> db.score.find({"score":[ 96, 95 ]}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "db6.score",
"indexFilterSet" : false,
"parsedQuery" : {
"score" : {
"$eq" : [
96,
95
]
}
},
"queryHash" : "8D76FC59",
"planCacheKey" : "E2B03CA1",
"winningPlan" : {
"stage" : "FETCH",
"filter" : {
"score" : {
"$eq" : [
96,
95
]
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"score" : 1
},
"indexName" : "score_1",
"isMultiKey" : true,
"multiKeyPaths" : {
"score" : [
"score"
]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"score" : [
"[96.0, 96.0]",
"[[ 96.0, 95.0 ], [ 96.0, 95.0 ]]"
]
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "mongo3",
"port" : 27017,
"version" : "4.2.12",
"gitVersion" : "5593fd8e33b60c75802edab304e23998fa0ce8a5"
},
"ok" : 1,
"$clusterTime" : {
"clusterTime" : Timestamp(1621326912, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
},
"operationTime" : Timestamp(1621326912, 1)
}
可以看到已经使用了新建的多键索引。
文本索引
为了支持对字符串内容的文本搜索查询，MongoDB提供了文本索引。文本(text )索引可以包含任何值为字符串或字符串元素数组的字段
db.user.createIndex({"sku_attributes":"text"})
db.user.find({$text:{$search:"测试"}})
查看执行计划：
handong1:PRIMARY> db.user.find({$text:{$search:"测试"}}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "db6.user",
"indexFilterSet" : false,
"parsedQuery" : {
"$text" : {
"$search" : "测试",
"$language" : "english",
"$caseSensitive" : false,
"$diacriticSensitive" : false
}
},
"queryHash" : "83098EE1",
"planCacheKey" : "7E2D582B",
"winningPlan" : {
"stage" : "TEXT",
"indexPrefix" : {
},
"indexName" : "sku_attributes_text",
"parsedTextQuery" : {
"terms" : [
"测试"
],
"negatedTerms" : [ ],
"phrases" : [ ],
"negatedPhrases" : [ ]
},
"textIndexVersion" : 3,
"inputStage" : {
"stage" : "TEXT_MATCH",
"inputStage" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "OR",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"_fts" : "text",
"_ftsx" : 1
},
"indexName" : "sku_attributes_text",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
}
}
}
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "mongo3",
"port" : 27017,
"version" : "4.2.12",
"gitVersion" : "5593fd8e33b60c75802edab304e23998fa0ce8a5"
},
"ok" : 1,
"$clusterTime" : {
"clusterTime" : Timestamp(1621328543, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
},
"operationTime" : Timestamp(1621328543, 1)
}
可以看到通过文本索引可以查到包含测试关键字的数据。
**注意：**可以根据自己需要创建复合文本索引。
2dsphere索引
创建测试数据
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.291226, 39.981198 ] },
name: "火器营桥",
category : "火器营桥"
}
)
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.281452, 39.914226 ] },
name: "五棵松",
category : "五棵松"
}
)
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.378038, 39.851467 ] },
name: "角门西",
category : "角门西"
}
)
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.467833, 39.881581 ] },
name: "潘家园",
category : "潘家园"
}
)
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.468264, 39.914766 ] },
name: "国贸",
category : "国贸"
}
)
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.46618, 39.960213 ] },
name: "三元桥",
category : "三元桥"
}
)
db.places.insert(
{
loc : { type: "Point", coordinates: [ 116.400064, 40.007827 ] },
name: "奥林匹克森林公园",
category : "奥林匹克森林公园"
}
)
添加2dsphere索引
db.places.createIndex( { loc : "2dsphere" } )
db.places.createIndex( { loc : "2dsphere" , category : -1, name: 1 } )
利用2dsphere索引查询多边形里的点
凤凰岭
[116.098234,40.110569]
天安门
[116.405239,39.913839]
四惠桥
[116.494351,39.912068]
望京
[116.494494,40.004594]
handong1:PRIMARY> db.places.find( { loc :
... { $geoWithin :
... { $geometry :
... { type : "Polygon" ,
... coordinates : [ [
... [116.098234,40.110569] ,
... [116.405239,39.913839] ,
... [116.494351,39.912068] ,
... [116.494494,40.004594] ,
... [116.098234,40.110569]
... ] ]
... } } } } )
{ "_id" : ObjectId("60a4c950d4211a77d22bf7f8"), "loc" : { "type" : "Point", "coordinates" : [ 116.400064, 40.007827 ] }, "name" : "奥林匹克森林公园", "category" : "奥林匹克森林公园" }
{ "_id" : ObjectId("60a4c94fd4211a77d22bf7f7"), "loc" : { "type" : "Point", "coordinates" : [ 116.46618, 39.960213 ] }, "name" : "三元桥", "category" : "三元桥" }
{ "_id" : ObjectId("60a4c94fd4211a77d22bf7f6"), "loc" : { "type" : "Point", "coordinates" : [ 116.468264, 39.914766 ] }, "name" : "国贸", "category" : "国贸" }
可以看到把集合中包含在指定四边形里的点，全部列了出来。
利用2dsphere索引查询球体上定义的圆内的点
handong1:PRIMARY> db.places.find( { loc :
... { $geoWithin :
... { $centerSphere :
... [ [ 116.439518, 39.954751 ] , 2/3963.2 ]
... } } } )
{ "_id" : ObjectId("60a4c94fd4211a77d22bf7f7"), "loc" : { "type" : "Point", "coordinates" : [ 116.46618, 39.960213 ] }, "name" : "三元桥", "category" : "三元桥" }
返回所有半径为经度 116.439518 E 和纬度 39.954751 N 的2英里内坐标。示例将2英里的距离转换为弧度，通过除以地球近似的赤道半径3963.2英里。
2d索引
在以下情况下使用2d索引：
- 您的数据库具有来自MongoDB 2.2或更早版本的旧版旧版坐标对。
- 您不打算将任何位置数据存储为GeoJSON对象。
哈希索引
要创建hashed索引，请指定 hashed 作为索引键的值，如下例所示：
handong1:PRIMARY> db.test.createIndex({"_id":"hashed"})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 4,
"numIndexesAfter" : 5,
"ok" : 1,
"$clusterTime" : {
"clusterTime" : Timestamp(1621419338, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
},
"operationTime" : Timestamp(1621419338, 1)
}
注意事项
- MongoDB支持任何单个字段的 hashed 索引。hashing函数折叠嵌入的文档并计算整个值的hash值，但不支持多键(即.数组)索引。
- 您不能创建具有hashed索引字段的复合索引，也不能在索引上指定唯一约束hashed；但是，您可以hashed在同一字段上创建索引和升序/降序（即非哈希）索引：MongoDB将对范围查询使用标量索引。
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