MongoDB aggregate__ function of aggregate function

The use of aggregate helps us to further split the set in MongoDB.

Example:

Db.collection.aggregate (
    {$match: {x:1}, {
    limit:10},
    {$group: {_id: "$age"}}
);

Operator Introduction:

$project: Include, exclude, rename, and display fields

$match: Query, requires the same parameters as find ()

$limit: Limit the number of results

$skip: Ignore number of results

$sort: Sort results by the given fields

$group: Combining results by given expression

$unwind: Dividing embedded arrays into their own top-level files

============================================================

This is a data Model on the MongoDB website:

{"
  _id": "10280",//zipcode
  "City": "NEW YORK",//
  "state": "NY",//city abbreviation
  "Pop": 5574,//population
  "loc": [/Latitude and longitude
    -74.016323,
    40.710537
  ]
}

1. Find a city with more than 10 million people

Db.zipcodes.aggregate (
    {$group: {_id: "$state", totalpop:{$sum: "$pop"}},
    {$match: {totalpop:{$get : 10000000}}
);

The above statement is equivalent to: SELECT state, sum (POPs) Totalpop from ZipCodes Group by Totalpop >= 10000000;

Analysis:

$group is used primarily for grouping, where _id is a collection of groups of types, Totalpop is a new field generated that is used to store totals.

In fact, after the document has been $group, the system generates a new document for it (the new documment is {"_id": "AK", "Totalpop": 550043}), which we will see more clearly in the following example.

$match, which is equivalent to providing query functionality for the newly generated document

2, the average population, each state

db.zipcodes.aggregate {$group: {_id: {state
                         : ' $state ', City: ' $city '},
                           pop: {$sum: ' $pop '}}},
                       { $group:
                       {_id: "$_id.state",
                         avgcitypop: {$avg: "$pop"}}})

There are two $group in the example above, what does that mean?

The first $group is to turn the original ZipCodes document into a new one, such as:

{'
  _id ': {' state
    ': ' CO ',
    ' city ': ' Edgewater '
  },
  ' Pop ': 13154
}

The second $group is based on the original, again reformatting the data, and then generate a new document, such as:

{
  "_id": "MN",
  "Avgcitypop": 5335
},

3. Check the largest and smallest cities in each state

Db.zipcodes.aggregate (
	{$group: {_id:{state: "$state", City: "$city"}, totalpop:{$sum: "$pop"}}},//the people of the statistical State, Generate a new document about the state and its total population
	{$sort: {"Totalpop": -1}},//to new documents, sorted by population in reverse order
	{$group: {_id: "$_id.state",
		" Biggestcity ": {$first:" $_id.city "},//the largest population of the city
		" Biggestpop ": {$first:" Totalpop "},//the number of the largest population
		" smallestcity ": {$last: "$_id.city"},
		"Smallestpop": {$last: "Totalpop"}
	}},//re-forming a new file containing, state, maximum population and minimum population
	// The structure is basically the same
	//But we need to format the data again
	{$project:
		{_id:0, State
		: "$_id",
		biggestcity:{name: "$ Biggestcity ", Pop:" $biggestPop "},
		smallestcity:{name:" $smallestCity ", Pop:" $smallestPop "}		
		}
);

Data structure, as follows:

{"State
  ": "RI",
  "biggestcity": {
    "name": "Cranston",
    "Pop": 176404
  },
  "smallestcity ': {
    ' name ': ' Clayville ',
    ' Pop ':}
}