Aggregations and Operators Considerations

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$geonear

$merge
$out
Frequently Used Operations
Examples
Calculate Average Price per Month
Calculate a Rolling Average Over 30 Days

Some aggregation stages and operators require special considerations when you use them with time series collections.

$geonear

Time series collections only support the $geoNear aggregation stage for sorting geospatial data from queries against 2dsphere indexes. You can't use the $near and $nearSphere operators on time series collections.

$merge

You cannot use the $merge aggregation stage to add data from another collection to a time series collection.

$out

Starting in MongoDB 7.0, you can use the $out aggregation stage to write documents to a time series collection. For more information, see Migrate Data into a Time Series Collection.

Frequently Used Operations

The following aggregation pipeline operators and stages are often used to analyze time series data:

$dateAdd: Adds a specified amount of time to a Date object.
$dateDiff: Returns the time difference between two dates.
$dateTrunc: Returns a date that has been truncated to the specific unit.
$setWindowFields: Runs calculations on documents in a given window.

Examples

Calculate Average Price per Month

Consider a dowJonesTickerData collection that contains documents with the following structure:

{
   date: ISODate("2020-01-03T05:00:00.000Z"),
   symbol: 'AAPL',
   volume: 146322800,
   open: 74.287498,
   adjClose: 73.486023,
   high: 75.144997,
   low: 74.125,
   close: 74.357498
}

This aggregation pipeline performs the following actions:

Uses $dateTrunc to truncate each document's date to the appropriate month.
Uses $group to group the documents by month and symbol.
Uses $avg to calculate the average price per month.

db.dowJonesTickerData.aggregate( [ {
   $group: {
      _id: {
         firstDayOfMonth: {
            $dateTrunc: {
               date: "$date",
               unit: "month"
            }
         },
         symbol: "$symbol"
      },
      avgMonthClose: {
         $avg: "$close"
      }
   }
} ] )

The pipeline returns a set of documents where each document contains the average closing price per month for a particular stock.

{
   _id: {
      firstDayOfMonth: ISODate("2020-06-01T00:00:00.000Z"),
      symbol: 'GOOG'
   },
   avgMonthClose: 1431.0477184545455
},
{
   _id: {
      firstDayOfMonth: ISODate("2021-07-01T00:00:00.000Z"),
      symbol: 'MDB'
   },
   avgMonthClose: 352.7314293333333
},
{
   _id: {
      firstDayOfMonth: ISODate("2021-06-01T00:00:00.000Z"),
      symbol: 'MSFT'
   },
   avgMonthClose: 259.01818086363636
}

Calculate a Rolling Average Over 30 Days

Consider a dowJonesTickerData collection that contains documents with the following structure:

{
   date: ISODate("2020-01-03T05:00:00.000Z"),
   symbol: 'AAPL',
   volume: 146322800,
   open: 74.287498,
   adjClose: 73.486023,
   high: 75.144997,
   low: 74.125,
   close: 74.357498
}

This aggregation pipeline performs the following operations:

Uses $setWindowFields to specify a window of 30 days.
Calculates a rolling average of the closing price over the last 30 days for each stock.

db.dowJonesTickerData.aggregate( [
   { $setWindowFields: {
      partitionBy: { symbol : "$symbol" } ,
      sortBy: { date: 1 },
      output: {
         averageMonthClosingPrice: {
            $avg : "$close",
            window : { range : [-1, "current"], unit : "month" }
         }
      }
   } }
] )

The pipeline returns a set of documents where each document includes a $averageMonthClosingPrice field that contains the average of the previous month's closing price for that stock symbol.

{
   date: ISODate("2020-01-29T05:00:00.000Z"),
   symbol: 'AAPL',
   volume: 216229200,
   adjClose: 80.014801,
   low: 80.345001,
   high: 81.962502,
   open: 81.112503,
   close: 81.084999,
   averageMonthClosingPrice: 77.63137520000001
}

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Querying Time Series Data

Create a Time Series Collection