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MongoDB索引

那海蓝蓝 人气:0

MongoDB 4.2官方支持索引类型如下：

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：

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

 到此这篇关于MongoDB索引类型汇总分享的文章就介绍到这了,更多相关MongoDB索引内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持！

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