Migrate Data into a Time Series Collection¶

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Create a New Time Series Collection
Transform Data (Optional)
Migrate Data into a Time Series Collection

To migrate data from an existing collection into a time series collection:

Create a New Time Series Collection
Transform Data (Optional)
Migrate Data into a Time Series Collection

Create a New Time Series Collection¶

To create a new time series collection, issue the following command in the mongosh:

db.createCollection(
    "weathernew", {
      timeseries: {
         timeField: "ts",
         metaField: "metaData",
         granularity: "hours"
       }
     }
)

For more information on the preceeding command, see Create a Time Series Collection.

Transform Data (Optional)¶

Time series collections support secondary indexes on the field specified as the metaField. If the data model of your time series data does not have a designated field for your metadata, you can transform your data to create one. To transform the data in your existing collection, use $merge or $out to create a temporary collection with your time series data.

Consider a collection with weather data of the following format:

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 {
    "_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 }
}

To transform this data, we issue the following command:

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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: "temporarytimeseries"
  }
])

After you run this command, you have an intermediary temporarytimeseries collection:

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db.temporarytimeseries.findOne()
{
   "_id" : ObjectId("5553a998e4b02cf7151190b8"),
   "ts" : ISODate("1984-03-05T13:00:00Z"),
   "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 },
   "metaData" : {
     "st" : "x+47600-047900",
     "position" : {
       "type" : "Point",
       "coordinates" : [ -47.9, 47.6 ]
     },
     "elevation" : 9999,
     "callLetters" : "VCSZ",
     "qualityControlProcess" : "V020",
     "type" : "FM-13"
   }
}

Migrate Data into a Time Series Collection¶

To migrate your data from an existing collection that is not of type timeseries into a time series collection, use mongodump and mongorestore.

Warning

When migrating or backfilling into a time series collection you should always insert the documents in order, from oldest to newest. In this case mongodump exports documents in natural order and the --maintainInsertionOrder option for mongorestore guarantees the same insertion order for documents.

For example, to export the temporarytimeseries collection, issue the following command:

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mongodump
    --uri="mongodb://mongodb0.example.com:27017,mongodb1.example.com:27017,mongodb2.example.com:27017/weather" \
    --collection=temporarytimeseries --out=timeseries

The command returns the following output:

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2021-06-01T16:48:39.980+0200  writing weather.temporarytimeseries to timeseries/weather/temporarytimeseries.bson
2021-06-01T16:48:40.056+0200  done dumping weather.temporarytimeseries (10000 documents)

To import timeseries/weather/temporarytimeseries.bson into the new collection weathernew, issue the following command:

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mongorestore
    --uri="mongodb://mongodb0.example.com:27017,mongodb1.example.com:27017,mongodb2.example.com:27017/weather" \
    --collection=weathernew --noIndexRestore \
    --maintainInsertionOrder \
    timeseries/weather/temporarytimeseries.bson

The command returns the following output:

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2021-06-01T16:50:56.639+0200  checking for collection data in timeseries/weather/temporarytimeseries.bson
2021-06-01T16:50:56.640+0200  restoring to existing collection weather.weathernew without dropping
2021-06-01T16:50:56.640+0200  reading metadata for weather.weathernew from timeseries/weather/temporarytimeseries.metadata.json
2021-06-01T16:50:56.640+0200  restoring weather.weathernew from timeseries/weather/temporarytimeseries.bson
2021-06-01T16:51:01.229+0200  no indexes to restore
2021-06-01T16:51:01.229+0200  finished restoring weather.weathernew (10000 documents, 0 failures)
2021-06-01T16:51:01.229+0200  10000 document(s) restored successfully. 0 document(s) failed to restore.

Note

Ensure that you run the preceeding command with the --noIndexRestore option. mongorestore cannot create indexes on time series collections.

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