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Migrate Data into a Time Series Collection

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  • Migrate Data to a Time Series Collection
  • Create a new time series collection
  • (Optional) Transform your data and specify a metadata field

To migrate data from an existing collection into a time series collection, use an $out stage in your aggregation pipeline. If your documents do not have a suitable metadata field, use a $addFields stage before the $out stage to add one.

1

Create a new time series collection by issuing the following command in the mongosh, changing the timeField, metaField, and granularity values as needed to match the data you're migrating:

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

For detailed information on creating a new time series collection, see Create and Query a Time Series Collection.

2

If your original collection doesn't have a metadata field, use the $addFields aggregation stage to add it. For the example in this procedure, documents require a metaData field.

Consider a collection with weather data that uses the format:

{
"_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 }
}

The following pipeline stages add a metaData field as specified in Step 1 and use $project to include or exclude the remaining fields in the document:

{ $addFields: {
metaData: {
"st": "$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
}
}
3

Run the aggregation pipeline with $out as the final stage

The example below uses the db.collection.aggregate() helper method:

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: "weathernew"
}
])

After you run this command, you have the weathernew collection below:

db.weathernew.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"
}
}

If your original collection had secondary indexes, manually recreate them now. If your 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. Create a secondary index on the timeField to regain query performance and resolve the log warning.

Tip

See also:

←  Add Secondary Indexes to Time Series CollectionsBuild Materialized Views on Top of Time Series Data →
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