MongoDB索引

時間  2019-11-13
標籤 mongodb 索引 欄目 MongoDB 简体版
原文   原文鏈接

數據庫中的索引就是用來提升查詢操做的性能，可是會影響插入、更新和刪除的效率，由於數據庫不只要執行這些操做，還要負責索引的更新。shell

經過創建索引，影響一部分插入、更新和刪除的效率，可是能大大挺高查詢的效率，這個仍是很值得的。數據庫

爲了開始後面的操做，首先經過MongoDB shell插入一些測試數據。dom

 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 /*    個人數據，如下測試都是基於這個測試，因爲數據是隨機生成，因此測試每次都會不一樣
 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 */

 

索引的操做

建立索引：在MongoDB shell中，能夠經過ensureIndex()來建立因此，第一個參數是指定要建立因此的鍵。性能

經過unique參數能夠建立惟一索引。測試

1 > db.school.students.ensureIndex({"name": 1}, {"unique": true})
2 >

 查看索引：優化

 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 >

 

刪除索引：spa

1 > db.school.students.dropIndex("name_1")
2 { "nIndexesWas" : 2, "ok" : 1 }
3 >

 

索引名稱：默認狀況下，索引的名稱是"鍵_值_鍵_值…"的形式，當鍵的數量不少的時候，索引的名字就會很長。code

因此，在建立索引的時候，能夠經過"name"參數自定義索引的名字。server

1 > db.school.students.ensureIndex({"name": 1}, {"name": "myIndex"})
2 >

 

 

explain()和hint()

經過explain()能夠獲得不少跟find相關的信息，對索引的分析頗有幫助。blog

當有多個可使用的索引時，MongoDB會自動選擇最優索引，可是咱們能夠經過hint()操做選擇咱們想要使用的索引。

下面來看看沒有索引時explain()的輸出：

 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：下面選擇了幾個咱們比較關心的字段

  • cursor：BasicCursor表示是full Collection scan，即沒有索引的全表掃描
  • n：知足查詢條件的文檔數量
  • nscannedObjects：總共掃描的文檔的數量
  • nscanned：總共掃描的索引節點的數量
  • scanAndOrder：false表示，MongoDB現有索引下文檔的順序來返回排序結果；true表示，MongoDB須要在獲得查詢結果後從新排序
  • millis：完成查詢須要的毫秒數

添加索引，再次檢查explain()的輸出：

 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 >

 

 

組合索引

單鍵索引仍是比較簡單的，當使用組合索引的時候，就要多考慮一些了。本身也不肯定可否總結的很好，若是錯誤，但願你們指出、討論。

索引創建可能有多種方式，咱們的目標就是減小"nscanned"（固然也有特例，請參照"索引和排序"）。

下面分析基於前面生成的數據來分析一下組合索引，假設咱們要查詢年齡大於等於23的女學生。

  • 使用"age_1"索引的輸出以下 
     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"索引的輸出以下 
     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"索引的輸出以下 
     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 }

  

 

經過上面的例子能夠看出，在使用組合索引的時候仍是要考慮不少東西的，因此能夠結合explain()來進行分析。

 

索引選擇機制

因爲咱們前面建立了三個索引，下面咱們直接使用默認查詢。

 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 >

 

存在多條索引的狀況下，MongoDB首選nscanned值最低的索引。

 

索引和排序

基於上面的例子，咱們加上對"name"的排序操做。這時，咱們能夠看到"scanAndOrder"變成了"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 }

 

在這個例子中，"nscanned"是最小的，因此這個方案是查詢效率最高的。可是，咱們要注意一下"scanAndOrder"，根據MongoDB文檔的解釋，查詢結果的排序不能利用現有的索引，MongoDB會把find找到的結果放入內存從新排序。這樣的話，若是數據量很大，會對性能產生很大的影響。

最好的辦法是利用索引來進行排序。

在這種狀況下，就要加入一個"name"的索引，同時在find操做時使用hint來指定索引方式，由於默認狀況MongoDB會選擇"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 >

 

經過這種方式，就能夠利用索引的排序來避免"scanAndOrder"爲true的狀況。可是再看看上面的方式，彷佛能夠進一步優化，雖然不能減小"nscanned"，可是能夠減小"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 >

 

 

總結

MongoDB中，索引還有不少東西，本文只是經過一些例子來介紹了索引的使用，以及組合索引的簡單分析

Ps: 本文中全部例子中的命令均可以參考如下連接

http://files.cnblogs.com/wilber2013/index.js

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