MongoDB : Is there a way to detect a value trend using aggregation?

Question

I\'m trying to detect the \"trend\" of a value in a collection.

Let\'s say I have the following:

{ created_at: 2014-12-01, value:1015 }
{ created_at: 201         


        

Answer 1

Rough outline: I would calculate the average for the ten minute period:

> var avgCursor = db.sensor_readings.aggregate([
    { "$match" : { "created_at" : { "$gt" : ten_minutes_ago, "$lte" : now } } }
    { "$group" : { "_id" : 0, "average" : { "$avg" : "$value" } } }
]}
> var avgDoc = avgCursor.toArray()[0]
> avgDoc
{ "_id" : 0, "average" : 23 }

Then I would store it in another collection:

> db.sensor_averages.insert({ "start" : ten_minutes_ago, "end" : now, "average" : avgDoc.average })

Finally, recall the two averages you need to compute the difference, and compute it:

> var diffCursor = db.sensor_averages.find({ "start" : { "$gte" : twenty_minutes_ago } }).sort({ "start" : -1 })
> var diffArray = diffCursor.toArray()
> var difference = diffArray[0].average - diffArray[1].average

You could also skip the periodic aggregations and instead keep a running average updated in sensor_averages, jumping to a new doc every 10 minutes. At the beginning of each 10 minute period, insert into sensor_averages a doc

{
    "start" : now,
    "svalues" : 0,
    "nvalues" : 0
}

then on each insert of a sensor_reading document for the next ten minutes, also update the sensor_averages doc:

db.sensor_averages.update(
    { "start" : now_rounded_to_the_ten_minute_boundary },
    { "$inc" : { "svalues" : value, "nvalues" : 1 } }
)

Then, when you want the difference between averages, recall the appropriate two docs, divide svalues by nvalues to get the average, and subtract.