Definition
New in version 5.0.
Returns the approximation of the area under a curve, which is calculated using the trapezoidal rule where each set of adjacent documents form a trapezoid using the:
sortBy field values in the
$setWindowFieldsstage for the integration intervals.input field expression result values in
$integralfor the y axis values.
$integral is only available in the
$setWindowFields stage.
$integral syntax:
{ $integral: { input: <expression>, unit: <time unit> } }
$integral takes a document with these fields:
Field | Description |
|---|---|
Specifies the expression to evaluate. You must provide an expression that returns a number. | |
Behavior
If you omit a window, a default window with unbounded upper and lower limits is used.
Example
Create a powerConsumption collection that contains electrical power
usage in kilowatts measured by meter devices at 30 second intervals:
db.powerConsumption.insertMany( [ { powerMeterID: "1", timeStamp: new Date( "2020-05-18T14:10:30Z" ), kilowatts: 2.95 }, { powerMeterID: "1", timeStamp: new Date( "2020-05-18T14:11:00Z" ), kilowatts: 2.7 }, { powerMeterID: "1", timeStamp: new Date( "2020-05-18T14:11:30Z" ), kilowatts: 2.6 }, { powerMeterID: "1", timeStamp: new Date( "2020-05-18T14:12:00Z" ), kilowatts: 2.98 }, { powerMeterID: "2", timeStamp: new Date( "2020-05-18T14:10:30Z" ), kilowatts: 2.5 }, { powerMeterID: "2", timeStamp: new Date( "2020-05-18T14:11:00Z" ), kilowatts: 2.25 }, { powerMeterID: "2", timeStamp: new Date( "2020-05-18T14:11:30Z" ), kilowatts: 2.75 }, { powerMeterID: "2", timeStamp: new Date( "2020-05-18T14:12:00Z" ), kilowatts: 2.82 } ] )
This example uses $integral in the $setWindowFields
stage to output the energy consumption in kilowatt-hours measured
by each meter device:
db.powerConsumption.aggregate( [ { $setWindowFields: { partitionBy: "$powerMeterID", sortBy: { timeStamp: 1 }, output: { powerMeterKilowattHours: { $integral: { input: "$kilowatts", unit: "hour" }, window: { range: [ "unbounded", "current" ], unit: "hour" } } } } } ] )
In the example:
partitionBy: "$powerMeterID"partitions the documents in the collection bypowerMeterID.sortBy: { timeStamp: 1 }sorts the documents in each partition bytimeStampin ascending order (1), so the earliesttimeStampis first.outputsets thekilowattsintegral value in a new field calledpowerMeterKilowattHoursusing$integralthat is run in a range window.The input expression is set to
"$kilowatts", which is used for the y axis values in the integral calculation.The
$integralunit is set to"hour"for thetimeStampfield, which means$integralreturns the kilowatt-hours energy consumption.The window contains documents between an
unboundedlower limit and thecurrentdocument in the output. This means$integralreturns the total kilowatt-hours energy consumption for the documents from the beginning of the partition, which is the first data point in the partition for each power meter, to the timestamp of the current document in the output.
In this example output, the energy consumption measured by meters 1 and
2 are shown in the powerMeterKilowattHours field:
{ "_id" : ObjectId("60cbdc3f833dfeadc8e62863"), "powerMeterID" : "1", "timeStamp" : ISODate("2020-05-18T14:10:30Z"), "kilowatts" : 2.95, "powerMeterKilowattHours" : 0 } { "_id" : ObjectId("60cbdc3f833dfeadc8e62864"), "powerMeterID" : "1", "timeStamp" : ISODate("2020-05-18T14:11:00Z"), "kilowatts" : 2.7, "powerMeterKilowattHours" : 0.023541666666666666 } { "_id" : ObjectId("60cbdc3f833dfeadc8e62865"), "powerMeterID" : "1", "timeStamp" : ISODate("2020-05-18T14:11:30Z"), "kilowatts" : 2.6, "powerMeterKilowattHours" : 0.045625 } { "_id" : ObjectId("60cbdc3f833dfeadc8e62866"), "powerMeterID" : "1", "timeStamp" : ISODate("2020-05-18T14:12:00Z"), "kilowatts" : 2.98, "powerMeterKilowattHours" : 0.068875 } { "_id" : ObjectId("60cbdc3f833dfeadc8e62867"), "powerMeterID" : "2", "timeStamp" : ISODate("2020-05-18T14:10:30Z"), "kilowatts" : 2.5, "powerMeterKilowattHours" : 0 } { "_id" : ObjectId("60cbdc3f833dfeadc8e62868"), "powerMeterID" : "2", "timeStamp" : ISODate("2020-05-18T14:11:00Z"), "kilowatts" : 2.25, "powerMeterKilowattHours" : 0.019791666666666666 } { "_id" : ObjectId("60cbdc3f833dfeadc8e62869"), "powerMeterID" : "2", "timeStamp" : ISODate("2020-05-18T14:11:30Z"), "kilowatts" : 2.75, "powerMeterKilowattHours" : 0.040625 } { "_id" : ObjectId("60cbdc3f833dfeadc8e6286a"), "powerMeterID" : "2", "timeStamp" : ISODate("2020-05-18T14:12:00Z"), "kilowatts" : 2.82, "powerMeterKilowattHours" : 0.06383333333333334 }
Tip
For an additional example about IOT Power Consumption, see the Practical MongoDB Aggregations e-book.