MongoDB Finishing Note のmapreduce

Mongdb's mapreduce is equivalent to "group by" in MySQL, so it's easy to use map/reduce for parallel "stats" on MongoDB.

Using MapReduce to implement the two function map functions and the reduce function, the map function calls emit (Key,value), iterates through all the records in the collection, and passes the key and value to the reduce function for processing. The map function and the reduce function can be implemented using JS, and a mapreduce operation can be performed with the Db.runcommand or MapReduce command.

Sample Shell

Db.runcommand ({mapreduce:<collection>, Map:<mapfunction>, reduce:<reducefunction>[, Query:<query Filter Object>[, Sort:<sorts the input objects using ThisKey. Useful foroptimization, like sorting by Theemit key forFewer reduces>[, Limit:<number of objects toreturnFrom collection>[, Out:<see Output Options Below>[, Keeptemp:<true|false>[, Finalize:<finalizefunction>[, Scope:<object where fields go into JavaScript global scope >[, Verbose:true]});

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Parameter description:
MapReduce: The set of targets to manipulate.
Map: Map functions (Generate key-value pairs of sequences, as parameters of the reduce function).
Reduce: Statistical functions.
Query: Target record filtering.
Sort: Target record sorting.
Limit: Limits the number of target records.
Out: The statistical result holds the collection (without specifying the use of a temporary collection, which is automatically deleted after the client disconnects).
Keeptemp: Whether to keep the temporary collection.
Finalize: The final processing function (save the result set after the final collation of the reduce return result).
Scope: Import external variables to map, reduce, finalize.
Verbose: Displays detailed time statistics.

Let's prepare the data for the following example

> Db.students.insert ({classid:1, age:14, Name: ' Tom '})> Db.students.insert ({classid:1, age:12, Name: ' Jacky '})> Db.students.insert ({classid:2, age:16, Name: ' Lily '})> Db.students.insert ({ Classid:2, Age:9, Name: ' Tony '})> Db.students.insert ({classid:2, age:19, Name: ' Harry '})> Db.students.insert ({classid:2, age:13, Name: ' Vincent '})> Db.students.insert ({classid:1, age:14, Name: ' Bill '})> Db.students.insert ({classid:2, age:17, Name: ' Bruce '})>

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Now we show how to count the number of students in Class 1 and Class 2.

The MAP function must call emit (key, value) to return a key-value pair, using this to access the currently pending Document.

Here this must not forget!!!

> m = function () {Emit (this. ClassID, 1)}function () {Emit (this. ClassID, 1);} >

Value can be passed using JSON Object (multiple property values are supported). For example:
Emit (This.classid, {count:1})
The reduce function receives a parameter similar to the group effect, combining the sequence of key values returned by the Map into {key, [Value1,value2, Value3, value ...] and passing it to reduce.

> r == 0+=return=0 + = v;}); return x;} >

The Reduce function "counts" These values and returns the result using JSON Object.

The results are as follows:

> res = db.runcommand ({... mapreduce:"Students",... map:m,... reduce:r,... out:"Students_res" ... }); {"result": "Students_res","Timemillis": 1587,"counts": {"input": 8,"emit"  : 8,"Output": 2},"OK": 1}>"_id": 1, "value": 3"_id": 2, "value": 5 }>

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MapReduce () stores the results in the "students_res" table.

Using Finalize () we can further process the results of reduce ().

return {classid:key, count:value};} function (key, value) {return  {classid:key, count:value};} >

Let's recalculate it again to see the result of the return:

> res = db.runcommand ({... mapreduce:"Students",... map:m,... reduce:r,... out:"Students_res" ,... finalize:f ...}); {"result": "Students_res","Timemillis": 804,"counts": {"input": 8,"emit": 8,"Output": 2},"OK": 1}>"_id": 1, "value": {"ClassID": 1, "Count": 3 /c11> "_id": 2, "value": {"ClassID": 2, "Count": 5 }}>

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Column names are changed to "ClassID" and "count", and such lists are easier to understand.

We can also add more control details.

> res = db.runcommand ({... mapreduce:"Students",... map:m,... reduce:r,... out:"Students_res" ,... finalize:f,... query:{age:{$lt:Ten}}); {"result": "Students_res","Timemillis": 358,"counts": {"input": 1,"emit": 1,"Output": 1},"OK": 1}>"_id": 2, "value": {"ClassID": 2, "Count": 1
   
    }}>
   

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You can see the first filter, only take age<10 data, and then the statistics, so there is no 1 classes of statistical data.

MongoDB Finishing Note のmapreduce