AGGREGATE examples

#ENCODING
import io
import sys
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-16')
#MONGO
from pymongo import MongoClient
client = MongoClient()
client.progzoo.authenticate('scott','tiger')
db = client['progzoo']
#PRETTY
import pprint
pp = pprint.PrettyPrinter(indent=4)

Introducing the aggregation framework

These examples introduce the aggregation framework and its operators. Again we will be using the collection world

$match performs queries in a similar way to find()

Show all the details for France

pp.pprint(list(
    db.world.aggregate([
        {"$match":{"name":"France"}}
    ])
))

pp.pprint(list(db.world.aggregate([{"$match":{"name":"France"}}])))

$project selects what fields to display.
It can also has the ability to create new fields and to compare fields against each other without using $where

Show the name and population density of all Asian countries. (population/area)

Note that "density" is a new field, made from the result of dividing two existing fields, and that $divide is an aggregate function.

To avoid diving by 0 insert a $match to remove any countries with 0 area (Vatican City), then pipe these results through to $project
There is no need to check if values are null, MongoDB will ignore these documents.

pp.pprint(list(
     db.world.aggregate([
        {"$match":{"area":{"$ne":0},"continent":"Asia"}},
        {"$project":{
            "_id":0,
            "name":1,
            "density": {"$divide": ["$population","$area"]}
        }}
    ])
))

pp.pprint(list(db.world.aggregate([{"$match":{"area":{"$ne":0},"continent":"Asia"}},{"$project":{"_id":0,"name":1,"density":{"$divide":["$population","$area"]}}}])))

Because aggregate is a pipeline stages may be repeated, and stages don't have to be used in a specific order.

Show the name of Asian countries with a density that's over 500 people per km². (population/area)

pp.pprint(list(
     db.world.aggregate([
        {"$match":{"area":{"$ne":0},"continent":"Asia"}},
        {"$project":{
            "_id":0,
            "name":1,
            "density": {"$divide": ["$population","$area"]}
        }},
        {"$match":{"density":{"$gt":500}}}
    ])
))

pp.pprint(list(db.world.aggregate([{"$match":{"area":{"$ne":0},"continent":"Asia"}},{"$project":{"_id":0,"name":1,"density":{"$divide":["$population","$area"]}}},{"$match":{"density":{"$gt":500}}}])))

$sort allows ordering of the results set, where 1 is ascending and -1 is descending.
Note that not including $match is the same as {"$match":{}}

Show the name of all countries in descending order.

pp.pprint(list(
     db.world.aggregate([
        {"$project":{
            "_id":0,
            "name":1,
        }},
        {"$sort":{
            "name":-1
        }}  
    ])
))

pp.pprint(list(db.world.aggregate([{"$project":{"_id":0,"name":1,}},{"$sort":{"name":-1}}])))

Grouping

Grouping provides accumulator operations such as $sum
All groups must have an _id. To see why this is useful imagine the following:

So far you've been using the collection world
As every country has a continent, it would make sense to have countries as a nested document inside continents: e.g:

[    
    {"name":"Africa",
     "countries":[
         {"name":"Algeria",    "capital":"Algiers",   ...},
         {"name":"Angola",     "capital":"Luanda",    ...},
         {"name":"Benin",      "capital":"Porto-Novo",...}.
         {...},
         ...
    ]},
    {"name":"Asia",
     "countries":[
         {"name":"Afghanistan","capital":"Kabul", ...},
         {"name":"Azerbaijan", "capital":"Baku",  ...},
         {"name":"Bahrain",    "capital":"Manama",...},
         {...},
         ...
    ]},
    {...},
    ...
]

The world collection isn't like this however. It uses the following structure, which has a redundancy where continent is repeated for each country.

[ 
    {"name":"Afghanistan","capital":"Kabul",  "continent":"Asia",  ...},
    {"name":"Albania",    "capital":"Tirana", "continent":"Europe, ...},
    {"name":"Algeria",    "capital":"Algiers","contiennt":"Africa",...},
    {...},
    ...
]

The code to group by continent is "_id":"$continent"
If instead the question was to group by country the code would be "_id":"$name".
To operate over the whole document (which would have the same effect as "_id":"$name") "_id":"null" or "_id":None can be used.

$max and $min can be used to get the largest and smallest values in a group.

Get the smallest and largest GDPs of each continent.

pp.pprint(list(
    db.world.aggregate([
        {"$group":{
            '_id':'$continent',
            'min':{"$min":"$gdp"},
            'max':{"$max":"$gdp"}
        }},
        {"$project":{
            "_id":1,
            "min":1,
            "max":1
        }},
    ])
))

pp.pprint(list(db.world.aggregate([{"$group":{'_id':'$continent','min':{"$min":"$gdp"},'max':{"$max":"$gdp"}}},{"$project":{"_id":1,"min":1,"max":1}},])))

A downside of grouping is that it is not possible to find the $name of the country with the min/max value.

A simple way is to do this combine a sort sort=[(<fieldname>, <direction>)] and a find_one(). Performance will be improved if these fields have previously been indexed.

Get the names and GDPs of the two countries with the smallest and largest GDPs.

It is possible that we will occasionally encounter null values in a data collection. This could be either where the field is simply not present in a certain document - MongoDB will simply ignore this document and not include it in the results set - or a document has a redundancy where the field is specified as null - which MongoDB will include. e.g: {"name": "Bob", "age": None} etc.

To deal with this we can use {<field>: {"$ne": None}} to prevent any null values from being included.

pp.pprint(
    db.world.find_one({"gdp":{"$ne":None}},{"name":1,"gdp":1,"_id":0},sort=[("gdp", 1)])
)
pp.pprint(
    db.world.find_one({},{"name":1,"gdp":1,"_id":0},sort=[("gdp", -1)])
)

pp.pprint(db.world.find_one({"gdp":{"$ne":None}},{"name":1,"gdp":1,"_id":0},sort=[("gdp", 1)])) pp.pprint(db.world.find_one({},{"name":1,"gdp":1,"_id":0},sort=[("gdp", -1)]))

Some other useful aggregate functions to know are $sum and average: $avg
This example combines all the material in these examples.

Order the continents in descending order by total GDP, Include the average GDP for each country.

pp.pprint(list(
    db.world.aggregate([
        {"$match":{}},
        {"$group":{
            "_id":"$continent",
            "Total GDP": {"$sum": "$gdp"},
            "Average GDP": {"$avg": "$gdp"}
        }},
        {"$sort":{
            "Total GDP":-1
        }},
        {"$project":{
            "Area":"$_id",
            "Total GDP": 1,
            "Average GDP":1,
            "_id":0
        }}
    ])
))

pp.pprint(list(db.world.aggregate([{"$group":{"_id":"$continent","Total GDP":{"$sum":"$gdp"},"Average GDP":{"$avg":"$gdp"}}},{"$sort":{"Total GDP":-1}},{"$project":{"Area":"$_id","Total GDP":1,"Average GDP":1,"_id":0}}])))