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Implementing MongoDB map-reduce
Map-reduce is a data processing paradigm for condensing large volumes of data into useful aggregated results.
Let us create a collection with documents −
> db.demo280.insertOne({"CustomerName":"Chris","isMarried":true});
{
"acknowledged" : true,
"insertedId" : ObjectId("5e49116edd099650a5401a62")
}
> db.demo280.insertOne({"CustomerName":"Mike","isMarried":false});
{
"acknowledged" : true,
"insertedId" : ObjectId("5e491170dd099650a5401a63")
}
> db.demo280.insertOne({"CustomerName":"David","isMarried":false});
{
"acknowledged" : true,
"insertedId" : ObjectId("5e491170dd099650a5401a64")
}
> db.demo280.insertOne({"CustomerName":"Bob","isMarried":true});
{
"acknowledged" : true,
"insertedId" : ObjectId("5e491171dd099650a5401a65")
}Display all documents from a collection with the help of find() method −
> db.demo280.find();
This will produce the following output −
{ "_id" : ObjectId("5e49116edd099650a5401a62"), "CustomerName" : "Chris", "isMarried" : true }
{ "_id" : ObjectId("5e491170dd099650a5401a63"), "CustomerName" : "Mike", "isMarried" : false }
{ "_id" : ObjectId("5e491170dd099650a5401a64"), "CustomerName" : "David", "isMarried" : false }
{ "_id" : ObjectId("5e491171dd099650a5401a65"), "CustomerName" : "Bob", "isMarried" : true }Following is the query to implement Mongo DB mapreduce −
> db.demo280.mapReduce(
... function() { emit(this.isMarried,true); },
...
... function(key, values) {return Array.sum(values)}, {
... query:{isMarried:true},
... out:"Output"
... }
...)This will produce the following output displaying total 2 documents (input: 2) matched the query and 2 results were emitted (emit: 2) −
{
"result" : "Output",
"timeMillis" : 1241,
"counts" : {
"input" : 2,
"emit" : 2,
"reduce" : 1,
"output" : 1
},
"ok" : 1
}
Updated on: 31-Mar-2020
103 Views
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