首页 > 代码库 > MongoDB索引
MongoDB索引
数据库中的索引就是用来提高查询操作的性能,但是会影响插入、更新和删除的效率,因为数据库不仅要执行这些操作,还要负责索引的更新。
通过建立索引,影响一部分插入、更新和删除的效率,但是能大大挺高查询的效率,这个还是很值得的。
为了开始后面的操作,首先通过MongoDB shell插入一些测试数据。
1 for(var i=0;i<10;i++){ 2 var randAge = parseInt(5*Math.random()) + 20; 3 var gender = (randAge%2)?"Male":"Female"; 4 db.school.students.insert({"name":"Will"+i, "gender": gender, "age": randAge}); 5 } 6 7 8 /* 我的数据,以下测试都是基于这个测试,由于数据是随机生成,所以测试每次都会不同 9 { "name" : "Will0", "gender" : "Female", "age" : 22 },10 { "name" : "Will1", "gender" : "Female", "age" : 20 },11 { "name" : "Will2", "gender" : "Male", "age" : 24 },12 { "name" : "Will3", "gender" : "Male", "age" : 23 },13 { "name" : "Will4", "gender" : "Male", "age" : 21 },14 { "name" : "Will5", "gender" : "Male", "age" : 20 },15 { "name" : "Will6", "gender" : "Female", "age" : 20 },16 { "name" : "Will7", "gender" : "Female", "age" : 24 },17 { "name" : "Will8", "gender" : "Male", "age" : 21 },18 { "name" : "Will9", "gender" : "Female", "age" : 24 },19 */
索引的操作
创建索引:在MongoDB shell中,可以通过ensureIndex()来创建所以,第一个参数是指定要创建所以的键。
通过unique参数可以创建唯一索引。
1 > db.school.students.ensureIndex({"name": 1}, {"unique": true})2 >
查看索引:
1 > db.school.students.getIndexes() 2 [ 3 { 4 "v" : 1, 5 "key" : { 6 "_id" : 1 7 }, 8 "ns" : "test.school.students", 9 "name" : "_id_"10 },11 {12 "v" : 1,13 "key" : {14 "name" : 115 },16 "unique" : true,17 "ns" : "test.school.students",18 "name" : "name_1"19 }20 ]21 >
删除索引:
1 > db.school.students.dropIndex("name_1")2 { "nIndexesWas" : 2, "ok" : 1 }3 >
索引名称:默认情况下,索引的名称是"键_值_键_值…"的形式,当键的数量很多的时候,索引的名字就会很长。
所以,在创建索引的时候,可以通过"name"参数自定义索引的名字。
1 > db.school.students.ensureIndex({"name": 1}, {"name": "myIndex"})2 >
explain()和hint()
通过explain()可以得到很多跟find相关的信息,对索引的分析很有帮助。
当有多个可以使用的索引时,MongoDB会自动选择最优索引,但是我们可以通过hint()操作选择我们想要使用的索引。
下面来看看没有索引时explain()的输出:
1 > db.school.students.find({"name": "Will5"}).explain() 2 { 3 "cursor" : "BasicCursor", 4 "isMultiKey" : false, 5 "n" : 1, 6 "nscannedObjects" : 6, 7 "nscanned" : 6, 8 "nscannedObjectsAllPlans" : 6, 9 "nscannedAllPlans" : 6,10 "scanAndOrder" : false,11 "indexOnly" : false,12 "nYields" : 0,13 "nChunkSkips" : 0,14 "millis" : 0,15 "indexBounds" : {16 17 },18 "server" : "××××:27017"19 }20 >
分析:下面选择了几个我们比较关心的字段
- cursor:BasicCursor表示是full Collection scan,即没有索引的全表扫描
- n:满足查询条件的文档数量
- nscannedObjects:总共扫描的文档的数量
- nscanned:总共扫描的索引节点的数量
- scanAndOrder:false表示,MongoDB现有索引下文档的顺序来返回排序结果;true表示,MongoDB需要在得到查询结果后重新排序
- millis:完成查询需要的毫秒数
添加索引,再次检查explain()的输出:
1 > db.school.students.ensureIndex({"name": 1}, {"unique": true}) 2 > db.school.students.find({"name": "Will5"}).explain() 3 { 4 "cursor" : "BtreeCursor name_1", 5 "isMultiKey" : false, 6 "n" : 1, 7 "nscannedObjects" : 1, 8 "nscanned" : 1, 9 "nscannedObjectsAllPlans" : 1,10 "nscannedAllPlans" : 1,11 "scanAndOrder" : false,12 "indexOnly" : false,13 "nYields" : 0,14 "nChunkSkips" : 0,15 "millis" : 0,16 "indexBounds" : {17 "name" : [18 [19 "Will5",20 "Will5"21 ]22 ]23 },24 "server" : "××××:27017"25 }26 >
组合索引
单键索引还是比较简单的,当使用组合索引的时候,就要多考虑一些了。自己也不确定能否总结的很好,如果错误,希望大家指出、讨论。
索引建立可能有多种方式,我们的目标就是减少"nscanned"(当然也有特例,请参照"索引和排序")。
下面分析基于前面生成的数据来分析一下组合索引,假设我们要查询年龄大于等于23的女学生。
- 使用"age_1"索引的输出如下
1 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).hint("age_1").explain() 2 { 3 "cursor" : "BtreeCursor age_1", 4 "isMultiKey" : false, 5 "n" : 2, 6 "nscannedObjects" : 4, 7 "nscanned" : 4, 8 "nscannedObjectsAllPlans" : 4, 9 "nscannedAllPlans" : 4,10 "scanAndOrder" : false,11 "indexOnly" : false,12 "nYields" : 0,13 "nChunkSkips" : 0,14 "millis" : 0,15 "indexBounds" : {16 "age" : [17 [18 23,19 1.7976931348623157e+30820 ]21 ]22 },23 "server" : "××××:27017"24 }25 >
索引的分析:
Index | Documents | Result |
age:20 | { "name" : "Will1", "gender" : "Female", "age" : 20 } | "n" : 2 |
age:20 | { "name" : "Will5", "gender" : "Male", "age" : 20 } | "nscannedObjects" : 4 |
age:20 | { "name" : "Will6", "gender" : "Female", "age" : 20 } | "nscanned" : 4 |
age:21 | { "name" : "Will4", "gender" : "Male", "age" : 21 } | |
age:21 | { "name" : "Will8", "gender" : "Male", "age" : 21 } | |
age:22 | { "name" : "Will0", "gender" : "Female", "age" : 22 } | |
age:23 | { "name" : "Will3", "gender" : "Male", "age" : 23 } | |
age:24 | { "name" : "Will2", "gender" : "Male", "age" : 24 } | |
age:24 | { "name" : "Will7", "gender" : "Female", "age" : 24 } | |
age:24 | { "name" : "Will9", "gender" : "Female", "age" : 24 } |
- 使用"age_1_gender_1"索引的输出如下
1 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).hint("age_1_gender_1").explain() 2 { 3 "cursor" : "BtreeCursor age_1_gender_1", 4 "isMultiKey" : false, 5 "n" : 2, 6 "nscannedObjects" : 2, 7 "nscanned" : 4, 8 "nscannedObjectsAllPlans" : 2, 9 "nscannedAllPlans" : 4,10 "scanAndOrder" : false,11 "indexOnly" : false,12 "nYields" : 0,13 "nChunkSkips" : 0,14 "millis" : 0,15 "indexBounds" : {16 "age" : [17 [18 23,19 1.7976931348623157e+30820 ]21 ],22 "gender" : [23 [24 "Female",25 "Female"26 ]27 ]28 },29 "server" : "××××:27017"30 }31 >
索引的分析:
Index | Documents | Result |
age:20, gender:Female | { "name" : "Will1", "gender" : "Female", "age" : 20 } | "n" : 2 |
age:20, gender:Female | { "name" : "Will6", "gender" : "Female", "age" : 20 } | "nscannedObjects" : 2 |
age:20, gender:Male | { "name" : "Will5", "gender" : "Male", "age" : 20 } | "nscanned" : 4 |
age:21, gender:Male | { "name" : "Will4", "gender" : "Male", "age" : 21 } | |
age:21, gender:Male | { "name" : "Will8", "gender" : "Male", "age" : 21 } | |
age:22, gender:Female | { "name" : "Will0", "gender" : "Female", "age" : 22} | |
age:23, gender:Male | { "name" : "Will3", "gender" : "Male", "age" : 23 } | |
age:24, gender:Female | { "name" : "Will7", "gender" : "Female", "age" : 24 } | |
age:24, gender:Female | { "name" : "Will9", "gender" : "Female", "age" : 24 } | |
age:24, gender:Male | { "name" : "Will2", "gender" : "Male", "age" : 24 } |
- 使用"gender_1_age_1"索引的输出如下
1 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).hint("gender_1_age_1").explain() 2 { 3 "cursor" : "BtreeCursor gender_1_age_1", 4 "isMultiKey" : false, 5 "n" : 2, 6 "nscannedObjects" : 2, 7 "nscanned" : 2, 8 "nscannedObjectsAllPlans" : 2, 9 "nscannedAllPlans" : 2,10 "scanAndOrder" : false,11 "indexOnly" : false,12 "nYields" : 0,13 "nChunkSkips" : 0,14 "millis" : 0,15 "indexBounds" : {16 "gender" : [17 [18 "Female",19 "Female"20 ]21 ],22 "age" : [23 [24 23,25 1.7976931348623157e+30826 ]27 ]28 },29 "server" : "××××:27017"30 }31 >
索引的分析:
Index | Documents | Result |
gender:Female, age:20 | { "name" : "Will1", "gender" : "Female", "age" : 20 } | "n" : 2 |
gender:Female, age:20 | { "name" : "Will6", "gender" : "Female", "age" : 20 } | "nscannedObjects" : 2 |
gender:Female, age:22 | { "name" : "Will0", "gender" : "Female", "age" : 22 } | "nscanned" : 2 |
gender:Female, age:24 | { "name" : "Will7", "gender" : "Female", "age" : 24 } | |
gender:Female, age:24 | { "name" : "Will9", "gender" : "Female", "age" : 24 } | |
gender:Male, age:20 | { "name" : "Will5", "gender" : "Male", "age" : 20 } | |
gender:Male, age:21 | { "name" : "Will4", "gender" : "Male", "age" : 21 } | |
gender:Male, age:21 | { "name" : "Will8", "gender" : "Male", "age" : 21 } | |
gender:Male, age:23 | { "name" : "Will3", "gender" : "Male", "age" : 23 } | |
gender:Male, age:24 | { "name" : "Will2", "gender" : "Male", "age" : 24 } |
通过上面的例子可以看出,在使用组合索引的时候还是要考虑很多东西的,所以可以结合explain()来进行分析。
索引选择机制
由于我们前面创建了三个索引,下面我们直接使用默认查询。
1 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).explain() 2 { 3 "cursor" : "BtreeCursor gender_1_age_1", 4 "isMultiKey" : false, 5 "n" : 2, 6 "nscannedObjects" : 2, 7 "nscanned" : 2, 8 "nscannedObjectsAllPlans" : 2, 9 "nscannedAllPlans" : 2,10 "scanAndOrder" : false,11 "indexOnly" : false,12 "nYields" : 0,13 "nChunkSkips" : 0,14 "millis" : 0,15 "indexBounds" : {16 "gender" : [17 [18 "Female",19 "Female"20 ]21 ],22 "age" : [23 [24 23,25 1.7976931348623157e+30826 ]27 ]28 },29 "server" : "××××:27017"30 }31 >
存在多条索引的情况下,MongoDB首选nscanned值最低的索引。
索引和排序
基于上面的例子,我们加上对"name"的排序操作。这时,我们可以看到"scanAndOrder"变成了"true"。
1 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).sort({"name":1}).explain() 2 { 3 "cursor" : "BtreeCursor gender_1_age_1", 4 "isMultiKey" : false, 5 "n" : 2, 6 "nscannedObjects" : 2, 7 "nscanned" : 2, 8 "nscannedObjectsAllPlans" : 7, 9 "nscannedAllPlans" : 9,10 "scanAndOrder" : true,11 "indexOnly" : false,12 "nYields" : 0,13 "nChunkSkips" : 0,14 "millis" : 0,15 "indexBounds" : {16 "gender" : [17 [18 "Female",19 "Female"20 ]21 ],22 "age" : [23 [24 23,25 1.7976931348623157e+30826 ]27 ]28 },29 "server" : "××××:27017"30 }
在这个例子中,"nscanned"是最小的,所以这个方案是查询效率最高的。但是,我们要注意一下"scanAndOrder",根据MongoDB文档的解释,查询结果的排序不能利用现有的索引,MongoDB会把find找到的结果放入内存重新排序。这样的话,如果数据量很大,会对性能产生很大的影响。
最好的办法是利用索引来进行排序。
在这种情况下,就要加入一个"name"的索引,同时在find操作时使用hint来指定索引方式,因为默认情况MongoDB会选择"nscanned"最小的方式。
1 > db.school.students.ensureIndex({"gender":1,"name":1}) 2 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).sort({"name":1}).hint("gender_1_name_1").explain() 3 { 4 "cursor" : "BtreeCursor gender_1_name_1", 5 "isMultiKey" : false, 6 "n" : 2, 7 "nscannedObjects" : 5, 8 "nscanned" : 5, 9 "nscannedObjectsAllPlans" : 5,10 "nscannedAllPlans" : 5,11 "scanAndOrder" : false,12 "indexOnly" : false,13 "nYields" : 0,14 "nChunkSkips" : 0,15 "millis" : 0,16 "indexBounds" : {17 "gender" : [18 [19 "Female",20 "Female"21 ]22 ],23 "name" : [24 [25 {26 "$minElement" : 127 },28 {29 "$maxElement" : 130 }31 ]32 ]33 },34 "server" : "xxxx:27017"35 }36 >
通过这种方式,就可以利用索引的排序来避免"scanAndOrder"为true的情况。但是再看看上面的方式,似乎可以进一步优化,虽然不能减少"nscanned",但是可以减少"nscannedObjects"。
1 > db.school.students.ensureIndex({"gender":1,"name":1,"age":1}) 2 > db.school.students.find({"age":{"$gte":23}, "gender":"Female"}).sort({"name":1}).hint("gender_1_name_1_age_1").explain() 3 { 4 "cursor" : "BtreeCursor gender_1_name_1_age_1", 5 "isMultiKey" : false, 6 "n" : 2, 7 "nscannedObjects" : 2, 8 "nscanned" : 5, 9 "nscannedObjectsAllPlans" : 2,10 "nscannedAllPlans" : 5,11 "scanAndOrder" : false,12 "indexOnly" : false,13 "nYields" : 0,14 "nChunkSkips" : 0,15 "millis" : 0,16 "indexBounds" : {17 "gender" : [18 [19 "Female",20 "Female"21 ]22 ],23 "name" : [24 [25 {26 "$minElement" : 127 },28 {29 "$maxElement" : 130 }31 ]32 ],33 "age" : [34 [35 23,36 1.7976931348623157e+30837 ]38 ]39 },40 "server" : "xxxx:27017"41 }42 >
总结
MongoDB中,索引还有很多东西,本文只是通过一些例子来介绍了索引的使用,以及组合索引的简单分析
Ps: 本文中所有例子中的命令都可以参考以下链接
http://files.cnblogs.com/wilber2013/index.js
MongoDB索引