MongoDB Aggregate
1. $sum: add 1 for each document in group
example1:
select manufacturer,count(*) from xx
group by manufacturer
{ "_id" : ObjectId("50b1aa983b3d0043b51b2c52"), "name" : "Nexus 7", "category" : "Tablets", "manufacturer" : "Google", "price" : 199 }
db.products.aggregate([ // don't forget the bracket
{$group:
{
_id:"$manufacturer",
num_products:{$sum:1}
}
}
])
result
{ "_id" : "Amazon", "num_products" : 2 }
{ "_id" : "Sony", "num_products" : 1 }
{ "_id" : "Samsung", "num_products" : 2
{ "_id" : "Google", "num_products" : 1 }
{ "_id" : "Apple", "num_products" : 4 }
example2:
sum pop for each state
{ "city" : "CLANTON", "loc" : [ -86.642472, 32.835532 ], "pop" : 13990, "state" : "AL", "_id" : "35045" }
db.zips.aggregate([ {$group: {_id:"$state","population":{$sum:"$pop"}} } ])
{ "_id" : "WA", "population" : 4866692 }
{ "_id" : "HI", "population" : 1108229 }
{ "_id" : "CA", "population" : 29754890 }
{ "_id" : "OR", "population" : 2842321 }
{ "_id" : "NM", "population" : 1515069 }
{ "_id" : "UT", "population" : 1722850 }
{ "_id" : "OK", "population" : 3145585 }
{ "_id" : "LA", "population" : 4217595 }
group id as a document
db.zips.aggregate([{$group:{"_id":{"state":"$state","city":"$city"}}}])
2. $avg: average
average population of a zipcode for each state
db.zips.aggregate([ {$group: {_id:"$state","avg_pop":{$avg:"$pop"}} } ])
3. $addToSet
find all zipcode for a city
{ "city" : "PALO ALTO", "loc" : [ -122.149685, 37.444324 ], "pop" : 15965, "state" : "CA", "_id" : "94301" }
{ "city" : "PALO ALTO", "loc" : [ -122.184234, 37.433424 ], "pop" : 1835, "state" : "CA", "_id" : "94304" }
{ "city" : "PALO ALTO", "loc" : [ -122.127375, 37.418009 ], "pop" : 24309, "state" : "CA", "_id" : "94306" }
db.zips.aggregate([ {$group: {"_id":"$city","postal_codes":{$addToSet:"$_id"}} }])
4. $push: similar to $addToSet, but has duplicate
5. $max, $min
6. double $group stage
{ "_id" : 0, "a" : 0, "b" : 0, "c" : 21 }
{ "_id" : 1, "a" : 0, "b" : 0, "c" : 54 }
{ "_id" : 2, "a" : 0, "b" : 1, "c" : 52 }
{ "_id" : 3, "a" : 0, "b" : 1, "c" : 17 }
{ "_id" : 4, "a" : 1, "b" : 0, "c" : 22 }
{ "_id" : 5, "a" : 1, "b" : 0, "c" : 5 }
{ "_id" : 6, "a" : 1, "b" : 1, "c" : 87 }
{ "_id" : 7, "a" : 1, "b" : 1, "c" : 97 }
db.fun.aggregate([ {$group: {_id:{a:"$a", b:"$b"}, c:{$max:"$c"}} }, {$group: {_id:"$_id.a", c:{$min:"$c"}} }])
7. $projection
data
{ "city" : "ACMAR", "loc" : [ -86.51557, 33.584132 ], "pop" : 6055, "state" : "AL", "_id" : "35004" }
project to
{ "city" : "acmar", "pop" : 6055, "state" : "AL", "zip" : "35004" }
cmd
db.zips.aggregate([ {$project: {_id:0, city:{$toLower:"$city"}, pop:1, state:1, zip:"$_id"} } ])
8. $match
filters for zipcodes with pop greater than 100,000 people
db.zips.aggregate([ {$match: {"pop":{$gt:100000}} } ])
match or
db.zips.aggregate([{$match:{"state":{$in:["NY","CA"]}}}])
$match:{$or:[{author:"dave"},{author:"john"}]}
http://stackoverflow.com/questions/16902930/mongodb-aggregation-framework-match-or
9. full text search
the search field should have text index before searcg
db.s.aggregate([ {$match: {$text:{$search:"tree rat"}} } ])
10. $sort
sort by (state, city), both ascending
db.zips.aggregate([ {$sort: {"state":1,"city":1}} ])
11. $unwind: opposite of $push
db.posts.aggregate([ /* unwind by tags */ {"$unwind":"$tags"} ]}
12. $out: redirect to a new collection, drop previous if exists
db.a.aggregate([ {$out:"collection_name"} ])
13. aggregation option
explain
db.zips.aggregate([{$group:{_id:"$state",popu:{$sum:"$pop"}}}],
{explain:true}
)
db.zips.aggregate([{$group:{_id:"$state",popu:{$sum:"$pop"}}}],
{allowDiskUse:true}
)