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Archiving Datasets

Overview

Archiving gives you the ability to safely store the datasets that you do not use frequently, without consuming your organization's active storage space quota. Depending on the Storage Class used, a dataset may be archived automatically. When you decide that you want to use the dataset again, you can quickly and easily restore it.

Archived datasets currently can not be queried.

Permissions

A user can archive or restore any dataset that they have write permissions for.

Examples

You can easily archive and restore a dataset through the UI or through the API (via Python or R).

Archiving

Dataset can be archived using the archive() function which will run the archiving task

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import solvebio as sb

# Retrieve the dataset by dataset_id
dataset = sb.Object.retrieve('dataset_id')
dataset.archive()

# Archive all datasets in a folder, recursively
folder = Object.retrieve('folder_id')
for dataset in folder.datasets(recursive=True):
    dataset.archive()
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require(solvebio)

# Set Storage class to archive
Object.update("DATASET ID", storage_class="Archive")

Restore

Restore of the archived dataset can be done using the restore() function on the archived dataset.

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import solvebio as sb

dataset = sb.Object.retrieve('dataset_id')
dataset.restore()
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require(solvebio)

# Restore the dataset by setting the Storage Class to standard
Object.update("DATASET ID", storage_class="Standard")

Switching the Storage Class

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import solvebio as sb

dataset = sb.Object.retrieve('dataset_id')

# Change the Storage Class to Essential
dataset.storage_class = "Essential"
dataset.save()
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require(solvebio)

# Set Storage class to archive
Object.update("DATASET ID", storage_class="Archive")

# Set the Storage Class to essential
Object.update("DATASET ID", storage_class="Essential")