Data Storage in the NASA Openscapes Hub

Storing large amounts of data in the cloud can incur significant ongoing costs if not done optimally. We are charged daily for data stored in our Hub. We are developing technical strategies and policies to reduce storage costs that will keep the Openscapes 2i2c Hub a shared resource for us all to use, while also providing reusable strategies for other admins.

The Hub uses an EC2 compute instance, with the $HOME directory (/users/jovyan/ in python images and /users/rstudio/ in R images) mounted to AWS Elastic File System (EFS) storage. This drive is really handy because it is persistent across server restarts and is a great place to store your code. However the $HOME directory should not be used to store data, as it is very expensive, and can also be quite slow to read from and write to.

To that end, the Hub provides every user access to two AWS S3 buckets - a “scratch” bucket for short-term storage, and a “persistent” bucket for longer-term storage. AWS S3 buckets are like online storage containers, accessible through the internet, where you can store and retrieve files. S3 buckets have fast read/write, and storage costs are relatively inexpensive compared to storing in your $HOME directory. All major cloud providers provide a similar storage service - S3 is Amazon’s version, while Google provides “Google Cloud Storage”, and Microsoft provides “Azure Blob Storage”.

These buckets are accessible only when you are working inside the Hub; you can access them using the environment variables:

• $SCRATCH_BUCKET pointing to s3://openscapeshub-scratch/[your-username]
  • Scratch buckets are designed for storage of temporary files, e.g. intermediate results. Objects stored in a scratch bucket are removed after 7 days from their creation.
• $PERSISTENT_BUCKET pointing to s3://openscapeshub-persistent/[your-username]
  • Persistent buckets are designed for storing data that is consistently used throughout the lifetime of a project. There is no automatic purging of objects in persistent buckets, so it is the responsibility of the Hub admin and/or Hub users to delete objects when they are no longer needed to minimize cloud billing costs.

Using S3 Bucket Storage

Please see the short tutorial in the Earthdata Cloud Cookbook on Using S3 Bucket Storage in NASA-Openscapes Hub.

Data retention and archiving policy

User $HOME directories will be retained for six months after their last use. After a home directory has been idle for six months, it will be archived to our “archive” S3 bucket, and removed. If a user requests their archive back, an admin can restore it for them.

Once a user’s home directory archive has been sitting in the archive for an additional six months, it will be permanently removed from the archive. After this it can no longer be retrieved.

In addition to these policies, admins will keep an eye on the Home Directory Usage Dashboard in Grafana. When a user’s home directory increases in size to over 100GB, we will contact them and work with them to reduce the size of their home directory - by removing large unnecessary files, and moving the rest to the appropriate S3 bucket (e.g., $PERSISTENT_BUCKET).

The _shared directory

The _shared directory is a place where instructors can put workshop materials for participants to access. It is mounted as /home/jovyan/shared, and is read only for all users. For those with admin access to the Hub, it is also mounted as a writeable directory as /home/jovyan/shared-readwrite.

This directory will follow the same policies as users’ home directories: after six months, contents will be archived to the “archive” S3 bucket (more below). After an additional six months, the archive will be deleted.

How to archive old home directories (admin)

To start, you will need to be an admin of the Openscapes Jupyterhub so that the allusers directory is mounted in your home directory. This will contain all users’ home directories, and you will have full read-write access.

Finding large $HOME directories

Look at the Home Directory Usage Dashboard in Grafana to see the directories that haven’t been used in a long time and/or are very large.

You can also view and sort users’ directories by size in the Hub with the following command, though this takes a while because it has to summarize a lot of files and directories. This will show the 30 largest home directories:

du -h --max-depth=1 /home/jovyan/allusers/ | sort -hr | head -n 30

Authenticate with S3 archive bucket

We have created an AWS IAM user called archive-homedirs with appropriate permissions to write to the openscapeshub-prod-homedirs-archive bucket. Get access keys for this user from the AWS console, and use these keys to authenticate in the Hub:

In the terminal, type:

awsv2 configure

Enter the access key and secret key at the prompts, and set default region to us-west-2.

You will also need to temporarily unset some AWS environment variables that have been configured to authenticate with NASA S3 storage. (These will be reset the next time you log in):

unset AWS_ROLE_ARN
unset AWS_WEB_IDENTITY_TOKEN_FILE

Test to make sure you can access the archive bucket:

# test s3 access:
awsv2 s3 ls s3://openscapeshub-prod-homedirs-archive/archives/
touch test123.txt
awsv2 s3 mv test123.txt s3://openscapeshub-prod-homedirs-archive/archives/
awsv2 s3 rm s3://openscapeshub-prod-homedirs-archive/archives/test123.txt

Setting up and running the archive script

We use a python script, developed by @yuvipanda, that reproducibly archives a list of users’ directories into a specified S3 bucket.

Copy the script into your home directory in the Hub.

In the Hub as of 2024-05-17, a couple of dependencies for the script are missing; you can install them before running the script:

pip install escapism

# I had solver errors with pigz so needed to use the classic solver. 
# Also, the installation of pigz required a machine with >= 3.7GB memory
conda install pigz --solver classic

Create a text file, with one username per line, of users’ home directories you would like to archive to s3. It will look like:

username1
username2
# etc...

Finally, run the script from the terminal, changing the parameter values as required:

python3 archive-home-dirs.py \
    --archive-name="archive-$(date +'%Y-%m-%d')" \
    --basedir=/home/jovyan/allusers/ \
    --bucket-name=openscapeshub-prod-homedirs-archive \
    --object-prefix="archives/" \
    --usernames-file=users-to-archive.txt \
    --temp-path=/home/jovyan/archive-staging/

Omitted in the above example, but available to use, is the --delete flag, which will delete the users’ home directory once the archive is completed.

If you don’t use the --delete flag, first verify that the archive was successfully completed and then remove the user’s home directory manually.

Archiving the shared directory

You can use the same script to archive directories in the shared directory, by modifying the inputs slightly:

• Set --basedir=/home/jovyan/shared/, (or --basedir=/home/jovyan/shared-readwrite/ if you want to be able use the --delete flag).
• Create a file with a list of directories in the shared directory you want to archive, and pass it to the --usernames-file argument.
• Set --object-prefix="archives/_shared/ to put the archives in the _shared subdirectory in the archive bucket.

E.g.:

python3 archive-home-dirs.py \
    --archive-name="archive-$(date +'%Y-%m-%d')" \
    --basedir=/home/jovyan/shared/ \
    --bucket-name=openscapeshub-prod-homedirs-archive \
    --object-prefix="archives/_shared/" \
    --usernames-file=/home/jovyan/shared-to-archive.txt \
    --temp-path=/home/jovyan/archive-staging/

By default, archives (.tar.gz) are created in your /tmp directory before upload to the S3 bucket. The /tmp directory is cleared out when you shut down the Hub. However, /tmp has limited space (80GB shared by up to four users on a single node), so if you are archiving many large directories, you will likely need to specify a location in your $HOME directory by passing a path to the --temp-path argument. The script will endeavour to clean up after itself and remove the tar.gz file after uploading, but double check that directory when you are finished or you may have copies of all of the other user directories in your own $HOME!