1. Data Protection Strategies with MongoDB Atlas Snapshots


    Data protection is a crucial aspect of any database management strategy. MongoDB Atlas, the fully-managed cloud service offered by MongoDB, provides features that allow you to efficiently back up your data using snapshots. These snapshots can be used to restore your data to a previous state in the event of data corruption, loss, or a need for data analysis from a point in time.

    In a Pulumi program, you would use the MongoDB Atlas provider to manage this process. Resources you might interact with include CloudBackupSnapshot to take regular snapshots, CloudBackupSnapshotRestoreJob to restore data from a snapshot, and CloudBackupSnapshotExportJob to export snapshots for analysis or offline storage.

    In this Python program using Pulumi, we'll assume that you are already authenticated with MongoDB Atlas and have the appropriate permissions to manage resources within your project. We're also assuming that you have a MongoDB Atlas cluster deployed and wish to work with snapshots on that cluster.

    This program will show you how to:

    • Create a regular snapshot of your MongoDB Atlas cluster.
    • Automate the restoration of a snapshot backup.
    • Export a snapshot for storage or analysis.

    We are going to use mongodbatlas.CloudBackupSnapshot to create a snapshot, mongodbatlas.CloudBackupSnapshotRestoreJob to restore a snapshot, and mongodbatlas.CloudBackupSnapshotExportJob to export a snapshot.

    Here is how you'd write the program:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Set your MongoDB Atlas Project ID and your cluster name. project_id = "your-atlas-project-id" cluster_name = "your-cluster-name" # Create a backup snapshot of the cluster. snapshot = mongodbatlas.CloudBackupSnapshot("mySnapshot", project_id=project_id, cluster_name=cluster_name, description="My manual snapshot", retention_in_days=7 ) # Restore the cluster from a backup snapshot. # Please provide your own snapshot ID. restore_job = mongodbatlas.CloudBackupSnapshotRestoreJob("mySnapshotRestoreJob", project_id=project_id, cluster_name=cluster_name, snapshot_id=snapshot.id ) # Export the cloud backup snapshot. # Please provide your own bucket information where the snapshots will be stored. export_job = mongodbatlas.CloudBackupSnapshotExportJob("mySnapshotExportJob", project_id=project_id, snapshot_id=snapshot.id, cluster_name=cluster_name, delivery_type_config={ "delivery_type": "download", "bucket_name": "your-bucket-name", "bucket_prefix": "your-bucket-prefix" } ) # Running `pulumi up` will apply the above settings and begin the snapshotting process. pulumi.export("snapshot_id", snapshot.id) pulumi.export("restore_job_id", restore_job.id) pulumi.export("export_job_id", export_job.id)

    In the code above:

    • CloudBackupSnapshot creates a new backup snapshot for the specified cluster.
    • CloudBackupSnapshotRestoreJob initiates a restore process using a specific snapshot ID, which you must replace with one from an actual snapshot in your project. This process creates a job that will restore the snapshot.
    • CloudBackupSnapshotExportJob starts a job to export the specified snapshot into an S3 bucket. You will need to provide an existing S3 bucket name and prefix where the snapshot will be stored.

    Running this Pulumi program will configure the backup, restoration, and export jobs on your MongoDB Atlas cluster based on the identifiers you provide.

    Each time you want to create a new snapshot or restore from a snapshot, you can update the values as needed and rerun the Pulumi program. The pulumi.export lines at the end of the program are there to output the IDs of the created resources, which can be useful for tracking and confirmation purposes.

    For more information about the resources used, you can visit the following links: