We usually measured by the sum of the expressions $ sum. Because the array of field MongoDB document, it is possible to simply calculate the sum of two types: the sum of 1, all the statistics meet the conditions of a field of the document; 2, document statistics for each array fields inside respective data values and . In both cases it can be done by $ sum expression. In both cases the statistical polymerization, polymerization corresponding frame $ group $ project steps and procedures.
1.$group
Direct look at an example of it.
Case 1
Mycol test data set as follows:
{
title: 'MongoDB Overview',
description: 'MongoDB is no sql database',
by_user: 'runoob.com',
url: 'http://www.runoob.com',
tags: ['mongodb', 'database', 'NoSQL'],
likes: 100
},
{
title: 'NoSQL Overview',
description: 'No sql database is very fast',
by_user: 'runoob.com',
url: 'http://www.runoob.com',
tags: ['mongodb', 'database', 'NoSQL'],
likes: 10
},
{
title: 'Neo4j Overview',
description: 'Neo4j is no sql database',
by_user: 'Neo4j',
url: 'http://www.neo4j.com',
tags: ['neo4j', 'database', 'NoSQL'],
likes: 750
}
Now we have the number of articles written by more than a collection of computing each author, using aggregate () is calculated
db.mycol.aggregate([{$group : {_id : "$by_user", num_tutorial : {$sum : 1}}}])
Query results are as follows:
/* 1 */
{
"_id" : "Neo4j",
"num_tutorial" : 1
},
/* 2 */
{
"_id" : "runoob.com",
"num_tutorial" : 2
}
Case 2
Each author is like the sum of statistics, the computational expressions:
db.mycol.aggregate([{$group : {_id : "$by_user", num_tutorial : {$sum : "$likes"}}}])
Query results are as follows;
/* 1 */
{
"_id" : "Neo4j",
"num_tutorial" : 750
},
/* 2 */
{
"_id" : "runoob.com",
"num_tutorial" : 110
}
Case 3
Some simple examples above, we'll look rich, sales data test set as follows:
{ "_id" : 1, "item" : "abc", "price" : 10, "quantity" : 2, "date" : ISODate("2014-01-01T08:00:00Z") }
{ "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1, "date" : ISODate("2014-02-03T09:00:00Z") }
{ "_id" : 3, "item" : "xyz", "price" : 5, "quantity" : 5, "date" : ISODate("2014-02-03T09:05:00Z") }
{ "_id" : 4, "item" : "abc", "price" : 10, "quantity" : 10, "date" : ISODate("2014-02-15T08:00:00Z") }
{ "_id" : 5, "item" : "xyz", "price" : 5, "quantity" : 10, "date" : ISODate("2014-02-15T09:05:00Z") }
The goal is to be done, based on the date grouping, daily sales statistics, aggregate formula is:
db.sales.aggregate(
[
{
$group:
{
_id: { day: { $dayOfYear: "$date"}, year: { $year: "$date" } },
totalAmount: { $sum: { $multiply: [ "$price", "$quantity" ] } },
count: { $sum: 1 }
}
}
]
)
Query results are:
{ "_id" : { "day" : 46, "year" : 2014 }, "totalAmount" : 150, "count" : 2 }
{ "_id" : { "day" : 34, "year" : 2014 }, "totalAmount" : 45, "count" : 2 }
{ "_id" : { "day" : 1, "year" : 2014 }, "totalAmount" : 20, "count" : 1 }
Case 4
Above, we can see $ group, we have used _id, using grouping, if so, we need not require grouping, how should I do it?
E.g. We now want to set sales statistics total number of items sold.
If directly remove _id group stages, as follows:
db.sales.aggregate(
[
{
$group:
{
totalAmount: { $sum: "$quantity" }
}
}
]
)
The error:
{
"message" : "a group specification must include an _id",
"ok" : 0,
"code" : 15955,
"codeName" : "Location15955",
"name" : "MongoError"
}
We still need to add on _id, but you can add a constant, constant in time according to the packet, which can be _id: "0" can be _id: "a", _id: "b", also can make _id: "x ", _id:" y "and so on.
。
E.g:
db.sales.aggregate(
[
{
$group:
{
_id : "Total"
totalAmount: { $sum: "$quantity" }
}
}
]
)
Query results:
{
"_id" : "Total",
"totalAmount" : 28
}
2. $ project stage
Case 5
Suppose there exists a set of students, the data structure is as follows:
{ "_id": 1, "quizzes": [ 10, 6, 7 ], "labs": [ 5, 8 ], "final": 80, "midterm": 75 }
{ "_id": 2, "quizzes": [ 9, 10 ], "labs": [ 8, 8 ], "final": 95, "midterm": 80 }
{ "_id": 3, "quizzes": [ 4, 5, 5 ], "labs": [ 6, 5 ], "final": 78, "midterm": 70 }
Now demand is the usual statistical test scores sum, sum test scores, the end of which the sum of the scores of each student.
db.students.aggregate([
{
$project: {
quizTotal: { $sum: "$quizzes"},
labTotal: { $sum: "$labs" },
examTotal: { $sum: [ "$final", "$midterm" ] }
}
}
])
Its output query results are as follows:
{ "_id" : 1, "quizTotal" : 23, "labTotal" : 13, "examTotal" : 155 }
{ "_id" : 2, "quizTotal" : 19, "labTotal" : 16, "examTotal" : 175 }
{ "_id" : 3, "quizTotal" : 14, "labTotal" : 11, "examTotal" : 148 }