5 Aggregations in 5 Minutes: The Nickel Tour of the Aggregation Framework

#5 Aggregations in 5 Minutes: The Nickel Tour of the Aggregation Framework

###Sample Document
```
mongos> db.logs.findOne()
{
	"_id" : ObjectId("556172a53004b760dde8a488"),
	"d_id" : ObjectId("556172a53004b760dde8a443"),
	"v" : 6205,
	"timestamp" : ISODate("3129-12-13T02:03:17.906Z"),
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-174.95596353219008,
			40.654427078258834
		]
	}
}

```

##1 
###A simple projection
```
var project = { $project: { device: "$d_id", value: "$v", _id : 0 } }
db.logs.aggregate( project ) 
```

###Results
```
mongos> db.logs.aggregate( project ) 
{ "device" : ObjectId("556172a53004b760dde8a443"), "value" : 6205 }
{ "device" : ObjectId("556172a53004b760dde8a43f"), "value" : 3467 }
{ "device" : ObjectId("556172a53004b760dde8a457"), "value" : 3535 }
{ "device" : ObjectId("556172a53004b760dde8a45d"), "value" : 5196 }
{ "device" : ObjectId("556172a53004b760dde8a430"), "value" : 5063 }
{ "device" : ObjectId("556172a53004b760dde8a45b"), "value" : 3429 }
{ "device" : ObjectId("556172a53004b760dde8a433"), "value" : 2791 }
{ "device" : ObjectId("556172a53004b760dde8a448"), "value" : 3024 }
{ "device" : ObjectId("556172a53004b760dde8a43b"), "value" : 6727 }
{ "device" : ObjectId("556172a53004b760dde8a46d"), "value" : 7381 }
{ "device" : ObjectId("556172a53004b760dde8a43a"), "value" : 3193 }
{ "device" : ObjectId("556172a53004b760dde8a463"), "value" : 7480 }
{ "device" : ObjectId("556172a53004b760dde8a485"), "value" : 1925 }
{ "device" : ObjectId("556172a53004b760dde8a487"), "value" : 2748 }
{ "device" : ObjectId("556172a53004b760dde8a42b"), "value" : 4152 }
{ "device" : ObjectId("556172a53004b760dde8a453"), "value" : 4699 }
{ "device" : ObjectId("556172a53004b760dde8a447"), "value" : 7381 }
{ "device" : ObjectId("556172a53004b760dde8a427"), "value" : 1190 }
{ "device" : ObjectId("556172a53004b760dde8a43c"), "value" : 7392 }
{ "device" : ObjectId("556172a53004b760dde8a475"), "value" : 8124 }
Type "it" for more
```

##2
###Grouping
```
var group = { $group: {_id: "$device", average: { "$avg": "$value"} } }
```

###Results

```
mongos> db.logs.aggregate( project, group )
{ "_id" : ObjectId("556172a53004b760dde8a42c"), "average" : 5545.707070707071 }
{ "_id" : ObjectId("556172a53004b760dde8a474"), "average" : 4978.903225806452 }
{ "_id" : ObjectId("556172a53004b760dde8a441"), "average" : 4680.555555555556 }
{ "_id" : ObjectId("556172a53004b760dde8a464"), "average" : 4449.392523364486 }
{ "_id" : ObjectId("556172a53004b760dde8a461"), "average" : 4996.213483146067 }
{ "_id" : ObjectId("556172a53004b760dde8a456"), "average" : 5068.40206185567 }
{ "_id" : ObjectId("556172a53004b760dde8a446"), "average" : 4885.193548387097 }
{ "_id" : ObjectId("556172a53004b760dde8a424"), "average" : 5428.291262135922 }
{ "_id" : ObjectId("556172a53004b760dde8a439"), "average" : 4490.298850574713 }
{ "_id" : ObjectId("556172a53004b760dde8a429"), "average" : 5121.046728971963 }
{ "_id" : ObjectId("556172a53004b760dde8a44b"), "average" : 4806.734042553191 }
{ "_id" : ObjectId("556172a53004b760dde8a462"), "average" : 4761.1612903225805 }
{ "_id" : ObjectId("556172a53004b760dde8a469"), "average" : 4435.75 }
{ "_id" : ObjectId("556172a53004b760dde8a47b"), "average" : 5881.725490196079 }
{ "_id" : ObjectId("556172a53004b760dde8a470"), "average" : 5089.471910112359 }
{ "_id" : ObjectId("556172a53004b760dde8a47f"), "average" : 5509.420168067227 }
{ "_id" : ObjectId("556172a53004b760dde8a458"), "average" : 4940.489795918367 }
{ "_id" : ObjectId("556172a53004b760dde8a455"), "average" : 4331.29347826087 }
{ "_id" : ObjectId("556172a53004b760dde8a476"), "average" : 4835.9795918367345 }
{ "_id" : ObjectId("556172a53004b760dde8a465"), "average" : 5084.881818181818 }
Type "it" for more
```

##3
### Add To Set 
```
var projectTypes = { 
 $project: { 
  device: "$d_id", 
  value: "$v", 
  _id : 0,  
  type: "$type"
 } 
}

var add2Set = { 
 $group: {
  _id: "", 
  sensorType: {
   $addToSet: "$type" 
  } 
 } 
} 
```

###Result
```
> db.devices.aggregate( projectTypes, add2Set );
{ "_id" : "",
  "sensorType" : [
   "temp",
   "humidity",
   "sound",
   "light", 
   "pressure" 
   ] 
} 
```

##4 Match by Geo Location
Return all sensors within 5 kilometers of my position

``` 
mongos> var loc = { type: "Point", coordinates: [ -174.9559, 40.6544 ] } 
mongos> var geoMatch = { 
	location: {
	 "$geoNear": { "$geometry": point, maxDistance: 500000 } 
	} 
}
mongos> var match = { $match: geoMatch } 
mongos> db.logs.aggregate( match )
```
###Result
```
too much to show here
```

##5 Output To A Collection
```
mongos> var iCommandTheeOutDemon = { $out: "paths" }; 
mongos> db.logs.aggregate( groupByDevice, projectGeoJson, iCommandTheeOutDemon )
```
### Results
Find one sample document.

```
mongos> db.paths.findOne()
{
	"_id" : ObjectId("556172a53004b760dde8a42c"),
	"coordinates" : [
		[
			-14.460004595311432,
			53.96892154407959
		],
		[
			-127.88437751912434,
			-13.702923069362384
		],
		...I

```


## Bonus Aggregation: Calculate Variance!
```
{ "$project" : { 
   mean: "$meanSpd",
   spdDiffSqrd : {
    "$map" : {
     "input": {
      "$map" : {
        "input" : "$speeds",
        "as" : "samp",
        "in" : { "$subtract" : [ "$$samp", "$meanSpd" ] }
       }
     },
     as: "df", in: { $multiply: [ "$$df", "$$df" ] }
} } } },
{ $unwind: "$spdDiffSqrd" },
{ $group: { _id: mean: "$mean", variance: { $avg: "$spdDiffSqrd" } } }

```
##Fun Fact: 
Each pipeline operator is an Abstract Syntax Tree (AST) represented in JSON!