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Restore Archived Data

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  • Required Access
  • Procedure

Important

Feature unavailable in Serverless Instances

Serverless instances don't support this feature at this time. To learn more, see Serverless Instance Limitations.

You can restore archived data to your Atlas cluster. You can use the alternate syntax that Atlas Data Federation provides for the $merge pipeline stage to move the data back into the same or different Atlas cluster, database, or collection within the same Atlas project.

Note

Ensure that your cluster is adequately provisioned for the amount of data that will be restored from your archive so that it doesn't run out of space during or after restoration of archived data. Contact Support for additional technical guidance on setting up the size of the oplog or for troubleshooting any space issues on your Atlas cluster.

This page describes how to restore archived data using the $merge pipeline stage or MongoDB Tools.

Required Access

To follow this procedure, you must have Project Data Access Admin access or higher to the project.

Procedure

If your dataset is small, you can use the $merge stage to move your archived data back to you Atlas cluster. This approach is not recommended for large datasets (around 1TB of data) with large number of partitions.

1

Pause the Online Archive associated with the collection whose archived data you wish to restore.

See Pause and Resume Archiving for more information.

2

Connect to Online Archive using the connection string.

You must use the Archive Only connection string to connect to the Online Archive. To learn more, see Connect to Online Archive.

3

Use $merge to move the data from your archive to your Atlas cluster.

To learn more about the $merge pipeline stage syntax and usage for moving data back into your Atlas cluster, see the $merge pipeline stage.

Example

Consider the following documents in an S3 archive:

{
  "_id" : 1,
  "item": "cucumber",
  "source": "nepal",
  "released": ISODate("2016-05-18T16:00:00Z")
}
{
  "_id" : 2,
  "item": "miso",
  "source": "canada",
  "released": ISODate("2016-05-18T16:00:00Z")
}
{
  "_id" : 3,
  "item": "oyster",
  "source": "luxembourg",
  "released": ISODate("2016-05-18T16:00:00Z")
}
{
  "_id" : 4,
  "item": "mushroom",
  "source": "ghana",
  "released": ISODate("2016-05-18T16:00:00Z")
}

Suppose the $merge syntax for restoring these documents into the Atlas cluster identifies documents based on the item and source fields during the $merge stage.

db.<collection>.aggregate([
  {
    "$merge": {
      "into": {
        "atlas": {
          "clusterName": "<atlas-cluster-name>",
          "db": "<db-name>",
          "coll": "<collection-name>"
        }
      },
      "on": [ "item", "source" ],
      "whenMatched": "keepExisting",
      "whenNotMatched": "insert"
    }
  }
])

In this example, when an archived document matches a document on the Atlas cluster on those two fields, Atlas keeps the existing document in the cluster because the copy of the document on the Atlas cluster is more recent than the copy of the document in the archive. When an archived document doesn't match any document in the Atlas cluster, Atlas inserts the document into the specified collection on the Atlas cluster.

When restoring data back into the Atlas cluster, the archived data might have duplicate _id fields. For this example, we can include a $sort stage for sorting on the _id and released fields before the $merge stage to ensure that Atlas chooses the documents with the recent date if there are duplicates to resolve.

Note

If there are multiple on fields, you must create a compound unique index on the on identifier fields:

db.<collection>.createIndex( { item: 1, source: 1 }, {
unique: true } )

Alternatively, specify merges sequentially, one for each on identifier field, to a temporary collection. Then merge the data in the temporary collection to the target collection using the cluster's connection string. You must still create a unique index for each on identifier field.

The aggregation stage can be run in the background by setting the background flag to true. To run this command in mongosh, use the db.runCommand.

db.runCommand(
  "aggregate": "<collection>",
  "pipeline": [
    {
      $sort: {
        "_id": 1,
        "released": 1,
      }
    },
    {
      "$merge": {
        "into": {
          "atlas": {
            "clusterName": "<atlas-cluster-name>",
            "db": "<db-name>",
            "coll": "<collection-name>"
          }
        },
        "on": [ "item", "source" ],
        "whenMatched": "keepExisting",
        "whenNotMatched": "insert"
      }
    }
  ], {"background": true}
)

To learn more about resolving duplicate fields, see the $merge considerations.

4

Verify data in the Atlas cluster and delete the online archive.

See Delete an Online Archive for more information.

Note

If you run into issues while migrating data back to your Atlas cluster, contact Support.

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