MapReduce applications in MongoDB

MapReduce can be used in MongoDB for complex aggregate queries.

Map and Reduce functions can be implemented using JavaScript.

You can use db. runCommand or mapReduce command to execute a MapReduce operation:

1. Db. runCommand (

2. {Mapreduce:<Collection>,

3. Map:<Mapfunction>,

4. Reduce:<Performancefunction>

5. [, Query:<QueryFilter object>]

6. [, Sort:<SortThe query. useful for optimization>]

7. [, Limit:<NumberOf objects to return from collection>]

8. [, Out:<Output-collectionName>]

9. [, Keeptemp:<True| False>]

10. [, Finalize:<Finalizefunction>]

11. [, Scope:<ObjectWhere fields go into javascript global scope>]

12. [, Verbose: true]

13. }

14. );

15. # Or use a packaged Helper command

16. Db. collection. mapReduce (mapfunction, performancefunction [, options]);

If no output is defined, a temporary collection is generated by default after it is executed. when the client is disconnected, the collection is automatically cleared.

A simple column has a collection of user_addr. The result is as follows:

1. Db. user_addr.find ({'uid': 'test @ sohu.com '})

2. {"_ Id": ObjectId ("4bbde0bf600ac3c3cc7245e3"), "Uid": "yangsong@sohu.com", "Al ":[

3. {

4. "Nn": "test-1 ",

5. "Em": "test-1@sohu.com ",

6. },

7. {

8. "Nn": "test-2 ",

9. "Em": "test-2@sohu.com ",

10. },

11. {

12. "Nn": "test-3 ",

13. "Em": "test-3@sohu.com ",

14. }

15. ]}

Stores the contact information (Al) corresponding to a user (Uid). to query the number of contacts corresponding to each Em, create the following MapReduce

1. M=Function(){

2. For (index in this. Al ){

3. Emit (this. Al [index]. Em, 1 );

4. }

5. }

6. R=Function(K, vals ){

7. VarSum=0;

8. For (index in vals ){

9. Sum + = vals [index];

10. >}

11. Return sum;

12. }

13. Res=Db. User_addr.mapReduce (m, r)

14. {

15. "Result": "tmp. mr. mapreduce_1272267853_1 ",

16. "TimeMillis": 29,

17. "Counts ":{

18. "Input": 5,

19. "Emit": 26,

20. "Output": 26

21. },

22. "OK": 1,

23. }

24. Db [res. result]. find ()

The group function in MongoDB also needs to be implemented using MapReduce.

For example, group by uid and calculate the number of contacts for each uid.

1. R=Function(Obj, prev ){

2. Prev. sum + = obj. Al. length;

3. }

4. Db. user_addr.group ({key: {'uid': 1}, reduce: r, initial: {sum: 0 }})