MongoDB学习七--MongoDB的Map-Reduce示例

In the mongo shell,
the db.collection.mapReduce() method
is a wrapper around the mapReducecommand.
The following examples use the db.collection.mapReduce() method: Consider
the following map-reduce operations on a collection orders that
contains documents of the following prototype:

{
_id: ObjectId("50a8240b927d5d8b5891743c"),
cust_id: "abc123",
ord_date: new Date("Oct 04, 2012"),
status: 'A',
price: 25,
items: [ { sku: "mmm", qty: 5, price: 2.5 },
{ sku: "nnn", qty: 5, price: 2.5 } ]
}

db.orders.insert({"cust_id":"zhang1","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang2","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang3","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang4","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang5","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang6","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang7","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang8","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang9","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

db.orders.insert({"cust_id":"zhang10","order_date":new Date(),"status":'A',"price":25.23, items:[{size:"S",qty:20,price:2.5},{size:"X",qty:2,price:3.0}]})

Return the Total Price Per Customer

Perform
the map-reduce operation on the orders collection
to group by the cust_id,
and calculate the sum of the price for
each cust_id:

Define the map function to process each input document:

In the function, this refers to the document that the map-reduce operation is processing.
The function maps the price to the cust_id for
each document and emits the cust_id andprice pair.

var mapFunction1 = function() {
emit(this.cust_id, this.price);
};

Define the corresponding reduce function with two arguments keyCustId and valuesPrices:

The valuesPrices is an array whose elements are the price values
emitted by the map function and grouped by keyCustId.
The function reduces the valuesPrice array to the sum of its elements.

var reduceFunction1 = function(keyCustId, valuesPrices) {
return Array.sum(valuesPrices);
};

Perform the map-reduce on all documents in the orders collection
using the mapFunction1 map
function and the reduceFunction1 reduce
function.

db.orders.mapReduce(
mapFunction1,
reduceFunction1,
{ out: "map_reduce_example" }
)

This operation outputs the results to a collection named map_reduce_example.
If themap_reduce_example collection
already exists, the operation will replace the contents with the results of this map-reduce operation:

Calculate Order and Total Quantity with Average Quantity Per Item

In this example, you will perform a map-reduce operation on the orders collection
for all documents that have an ord_date value
greater than 01/01/2012.
The operation groups by the item.sku field,
and calculates the number of orders and the total quantity ordered for each sku.
The operation concludes by calculating the average quantity per order for each sku value:

Define the map function to process each input document:

In the function, this refers to the document that the map-reduce operation is processing.
For each item, the function associates the sku with a new object value that
contains thecount of 1 and the item qty for
the order and emits the sku and value pair.

var mapFunction2 = function() {
for (var idx = 0; idx < this.items.length; idx++) {
var key = this.items[idx].sku;
var value = {
count: 1,
qty: this.items[idx].qty
};
emit(key, value);
}
};

Define the corresponding reduce function with two arguments keySKU and countObjVals:

countObjVals is an array whose elements are the objects mapped to the grouped keySKUvalues
passed by map function to the reducer function.
The function reduces the countObjVals array to a single object reducedValue that
contains the count and the qty fields.
In reducedVal, the count field
contains the sum of the count fields from the individual array elements, and the qty field
contains the sum of the qty fields from the individual array elements.

var reduceFunction2 = function(keySKU, countObjVals) {
reducedVal = { count: 0, qty: 0 };

for (var idx = 0; idx < countObjVals.length; idx++) {
reducedVal.count += countObjVals[idx].count;
reducedVal.qty += countObjVals[idx].qty;
}

return reducedVal;
};

Define a finalize function with two arguments key and reducedVal.
The function modifies thereducedVal object
to add a computed field named avg and
returns the modified object:

var finalizeFunction2 = function (key, reducedVal) {

reducedVal.avg = reducedVal.qty/reducedVal.count;

return reducedVal;

};

Perform the map-reduce operation on the orders collection
using the mapFunction2,reduceFunction2,
and finalizeFunction2 functions.

db.orders.mapReduce( mapFunction2,
reduceFunction2,
{
out: { merge: "map_reduce_example" },
query: { ord_date:
{ $gt: new Date('01/01/2012') }
},
finalize: finalizeFunction2
}
)

This operation uses the query field
to select only those documents with ord_date greater
than newDate(01/01/2012).
Then it output the results to a collection map_reduce_example.
If themap_reduce_example collection
already exists, the operation will merge the existing contents with the results of this map-reduce operation.