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Migrate Data into a Time Series Collection

On this page

  • About this Task
  • Before you Begin
  • Steps
  • Optional. Create a metadata field.
  • Create your time series collection and insert your data.
  • Next Steps

This page describes how to migrate data into a time series collection using an aggregation pipeline. Alternatively, to learn how to migrate data into a time series collection using MongoDB Relational Migrator, see Configure a Time Series Collection with Relational Migrator.

About this Task

Starting in MongoDB version 7.0, you can use the $out aggregation stage to migrate data from an existing collection into a time series collection. If your collection stores data that you want to compare across time intervals, use a time series collection to improve performance and storage. For more information on the benefits of time series collections, see Time Series Collections.

Before you Begin

Consider a weatherdata collection that contains time and metadata information:

db.weatherdata.insertOne(
   {
      _id: ObjectId("5553a998e4b02cf7151190b8"),
      st: "x+47600-047900",
      ts: ISODate("1984-03-05T13:00:00Z"),
      position: {
         type: "Point",
         coordinates: [ -47.9, 47.6 ]
      },
      elevation: 9999,
      callLetters: "VCSZ",
      qualityControlProcess: "V020",
      dataSource: "4",
      type: "FM-13",
      airTemperature: { value: -3.1, quality: "1" },
      dewPoint: { value: 999.9, quality : "9" },
      pressure: { value: 1015.3, quality: "1" },
      wind: {
         direction: { angle: 999, quality: "9" },
         type: "9",
         speed: { rate: 999.9, quality: "9" }
      },
      visibility: {
         distance: { value: 999999, quality : "9" },
         variability: { value: "N", quality: "9" }
      },
      skyCondition: {
         ceilingHeight: { value: 99999, quality: "9", determination: "9" },
         cavok: "N"
      },
      sections: [ "AG1" ],
      precipitationEstimatedObservation: {
         discrepancy: "2",
         estimatedWaterDepth: 999
      }
   }
)

Steps

1

Optional. Create a metadata field.

If your collection does not include a specific field that contains all metadata, transform your data to define one.

The following pipeline stages add a metaData field and use $project to include or exclude the remaining fields in the document. In this example, the metaData field becomes the metaField of the time series collection that you create.

Note

Choosing the right field as your time series metaField optimizes both storage and query performance. For more information on metaField selection and best practices, see metaFields.

{ $addFields: {
      metaData: {
      st: "$st",
      position: "$position",
      elevation: "$elevation",
      callLetters: "$callLetters",
      qualityControlProcess: "$qualityControlProcess",
      type: "$type"
      }
   },
},
{ $project: {
      _id: 1,
      ts: 1,
      metaData: 1,
      dataSource: 1,
      airTemperature: 1,
      dewPoint: 1,
      pressure: 1,
      wind: 1,
      visibility: 1,
      skyCondition: 1,
      sections: 1,
      precipitationEstimatedObservation: 1
   }
}
2

Create your time series collection and insert your data.

The example below performs the following operations:

  • Uses the $addFields aggregation stage to add a metaData field to your weather_data collection.

  • Uses the $out aggregation stage with the timeseries option to create a weathernew time series collection in the mydatabase database.

  • Defines the metaField of your weathernew collection as the metaData field.

  • Defines the timeField of your weathernew collection as the ts field.

Note

The timeField of a time series collection must be a date type.

db.weather_data.aggregate( [
  {
     $addFields: {
       metaData: {
         st: "$st",
         position: "$position",
         elevation: "$elevation",
         callLetters: "$callLetters",
         qualityControlProcess: "$qualityControlProcess",
         type: "$type"
       }
     },
  }, {
     $project: {
        _id: 1,
        ts: 1,
        metaData: 1,
        dataSource: 1,
        airTemperature: 1,
        dewPoint: 1,
        pressure: 1,
        wind: 1,
        visibility: 1,
        skyCondition: 1,
        sections: 1,
        precipitationEstimatedObservation: 1
     }
  }, {
     $out: {
       db: "mydatabase",
       coll: "weathernew",
       timeseries: {
         timeField: "ts",
         metaField: "metaData"
       }
     }
  }
] )

For the aggregation stage syntax, see $out. For a full explanation of the time series options, see the Time Series Field Reference.

After you run this aggregation pipeline, you can use findOne() to view a document in your weathernew time series collection:

db.weathernew.findOne()

The operation returns the following document:

   {
      _id: ObjectId("5553a998e4b02cf7151190b8"),
      ts: ISODate("1984-03-05T13:00:00Z"),
      metaData: {
         st: "x+47600-047900",
         position: {
            type: "Point",
            coordinates: [ -47.9, 47.6 ]
         },
         elevation: 9999,
         callLetters: "VCSZ",
         qualityControlProcess: "V020",
         type: "FM-13"
      },
      dataSource: "4",
      airTemperature: { value: -3.1, quality: "1" },
      dewPoint: { value: 999.9, quality: "9" },
      pressure: { value: 1015.3, quality: "1" },
      wind: {
         direction: { angle: 999, quality: "9" },
         type: "9",
         speed: { rate: 999.9, quality: "9" }
      },
      visibility: {
         distance: { value: 999999, quality: "9" },
         variability: { value: "N", quality: "9" }
      },
      skyCondition: {
         ceilingHeight: { value: 99999, quality: "9", determination: "9" },
         cavok: "N"
      },
      sections: [ "AG1" ],
      precipitationEstimatedObservation: { discrepancy: "2", estimatedWaterDepth: 999 }
   }

Next Steps

If your original collection had secondary indexes, manually recreate them now.

If your time series collection includes timeField values before 1970-01-01T00:00:00.000Z or after 2038-01-19T03:14:07.000Z, MongoDB logs a warning and disables some query optimizations that make use of the internal clustered index. To regain query performance and resolve the log warning, create a secondary index on the timeField.

Tip

See also:

Add Secondary Indexes to Time Series Collections

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