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|
/* sample aggregate command queries */
// make sure we're using the right db; this is the same as "use mydb;" in shell
db = db.getSisterDB("mydb");
// just passing through fields
var p1 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
tags : 1,
pageViews : 1
}}
]});
// unwinding an array
var p2 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
tag : { $unwind : "tags" },
pageViews : 1
}}
]});
// pulling values out of subdocuments
var p3 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
otherfoo : "other.foo",
otherbar : "other.bar"
}}
]});
// projection includes a computed value
var p4 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
daveWroteIt : { $eq:["$author", "dave"] }
}}
]});
// projection includes a virtual (fabricated) document
var p5 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
pageViews : 1,
tag : { $unwind : "tags" }
}},
{ $project : {
author : 1,
subDocument : { foo : "pageViews", bar : "tag" }
}}
]});
// multi-step aggregate
// nested expressions in computed fields
var p6 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
tag : { $unwind : "tags" },
pageViews : 1
}},
{ $project : {
author : 1,
tag : 1,
pageViews : 1,
daveWroteIt : { $eq:["$author", "dave"] },
weLikeIt : { $or:[ { $eq:["$author", "dave"] },
{ $eq:["$tag", "good"] } ] }
}}
]});
// slightly more complex computed expression; $ifnull
var p7 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
theSum : { $add:["$pageViews",
{ $ifnull:["$other.foo",
"$other.bar"] } ] }
}}
]});
// dotted path inclusion; _id exclusion
var p8 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
_id : 0,
author : 1,
tag : { $unwind : "tags" },
"comments.author" : 1
}}
]});
// simple matching
var m1 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $match : { author : "dave" } }
]});
// combining matching with a projection
var m2 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
title : 1,
author : 1,
pageViews : 1,
tag : { $unwind : "tags" },
comments : 1
}},
{ $match : { tag : "nasty" } }
]});
// group by tag
var g1 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
tag : { $unwind : "tags" },
pageViews : 1
}},
{ $group : {
_id: { tag : 1 },
docsByTag : { $sum : 1 },
viewsByTag : { $sum : "$pageViews" }
}}
]});
// $max, and averaging in a final projection
var g2 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
tag : { $unwind : "tags" },
pageViews : 1
}},
{ $group : {
_id: { tag : 1 },
docsByTag : { $sum : 1 },
viewsByTag : { $sum : "$pageViews" },
mostViewsByTag : { $max : "$pageViews" },
}},
{ $project : {
_id: false,
tag : "_id.tag",
mostViewsByTag : 1,
docsByTag : 1,
viewsByTag : 1,
avgByTag : { $divide:["$viewsByTag", "$docsByTag"] }
}}
]});
// $push as an accumulator; can pivot data
var g3 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
tag : { $unwind : "tags" }
}},
{ $group : {
_id : { tag : 1 },
authors : { $push : "$author" }
}}
]});
// $avg, and averaging in a final projection
var g4 = db.runCommand(
{ aggregate : "article", pipeline : [
{ $project : {
author : 1,
tag : { $unwind : "tags" },
pageViews : 1
}},
{ $group : {
_id: { tag : 1 },
docsByTag : { $sum : 1 },
viewsByTag : { $sum : "$pageViews" },
avgByTag : { $avg : "$pageViews" },
}}
]});
|
