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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" : 1
15                 },
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+308
20                         ]
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+308
20                         ]
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+308
26                         ]
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+308
26                         ]
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+308
26                         ]
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" : 1
27                                 },
28                                 {
29                                         "$maxElement" : 1
30                                 }
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" : 1
27                                 },
28                                 {
29                                         "$maxElement" : 1
30                                 }
31                         ]
32                 ],
33                 "age" : [
34                         [
35                                 23,
36                                 1.7976931348623157e+308
37                         ]
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

作者：田小计划

出处：http://www.cnblogs.com/wilber2013/

本文版权归作者和博客园共有，欢迎转载，但未经作者同意必须保留此段声明，且在文章页面明显位置给出原文连接，否则保留追究法律责任的权利。

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原文地址：https://www.cnblogs.com/wilber2013/p/4136318.html