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Use JuiceFS on Colab with Google CloudSQL and GCS

Colaboratory, or “Colab” for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary Python code through the browser, and is especially well suited to machine learning, data analysis and education.

Colab supports Google Drive for upload files to or download files from Colab instances. However, in some cases, Google Drive might not be that convenient to use with Colab. This is where JuiceFS could be a helpful tool to allow easily sync files between Colab instances, or between Colab instance and a local or on-premises machine.

A demo Colab notebook using JuiceFS can be seen here.

We illustrate the needed steps for using JuiceFS in Colab environment. We use Google CloudSQL as the JuiceFS metadata engine, and Google Cloud Storage (GCS) as the JuiceFS object storage.

For other types of metadata engines or object storages, please refer to the metadata engine reference doc and the object storage reference doc.

Many of the steps mentioned here will be quite similar with the getting started doc which you can also use for reference.

Summary of steps

  1. Format a juicefs file system from any machine/instance which having access to Google cloud resources
  2. Mount juicefs file system in a Colab Notebook
  3. Happily storing sharing files cross machines and platforms


In this demo we will use Google Cloud Platform's CloudSQL and Google Cloud Storage (GCS) as the backbone for creating a massive high performance file storage system via juicefs. It is required that you have a Google Cloud Platform account to follow this demo document.

Otherwise, if you have another cloud vendors' resources (for example AWS's RDBS and S3), you can still use this guide as a reference and with other reference docs provided by juicefs to achieve a similar solution.

You might also want the Colab instance is in the same zone or close to the region where CloudSQL and GCS are deployed, to make JuiceFS reaching the best performance. The tutorial should work for a random hosted Colab instance but you might notice a slow performance due to the latency between the Colab instance and the CloudSQL/GCS regions. To start Colab instances in a specific region, see instructions for starting a GCE VM on Colab via GCP Marketplace .

So in order to follow this guide you will need to have these resources ready:

  • A Google Cloud Platform account ready and also a project created. In our case we will use GCP project as juicefs-learning for the demo
  • A CloudSQL (Postgres) ready to be used. In this demo uses instance juicefs-learning:europe-west1:juicefs-sql-example-1 as the metadata service
  • A GCS bucket created as the object storage service. In this demo we use gs://juicefs-bucket-example-1 as the bucket to store file chunks.
  • An IAM ServiceAccount or an authorized user account that has write access to the Postgres server and GCS buckets

Detailed Steps

Step 1 - Format and mount a JuiceFS file system folder

This step is only need to be done once, and you can choose to do this at any machine/instance where you have good connection and access to your Google Cloud resources.

Firstly you can use gcloud auth application-default login to prepare a local credential, or use GOOGLE_APPLICATION_CREDENTIALS to set up a JSON key file.

Then you can use cloud_sql_proxy to open a port (in this case 5432) locally so to expose your cloud Postgres service to your local machine:

gcloud auth application-default login

# Or set up the json key file via GOOGLE_APPLICATION_CREDENTIALS=/path/to/key

cloud_sql_proxy -instances=juicefs-learning:europe-west1:juicefs-sql-example-1=tcp:

Then use the following command to create a new file system with a name as myvolume by using juicefs format command. Then later we can mount this file system in any other machines/instances where you have access to your cloud resources.

You can download juicefs here.

juicefs format \
--storage gs \
--bucket gs://juicefs-bucket-example-1 \
"postgres://postgres:mushroom1@localhost:5432/juicefs?sslmode=disable" \

Noticing again, this step above is only need to be done once at any machine you feel like to work on.

Step 2 - Mount JuiceFS file system on Colab

After you have finished with Step 1 above, then it means you already have a JuiceFS filesystem (called myvolume in this case) is defined and ready to be used.

So here we open a Colab page and run those command to mount our file system into a folder called mnt.

Firstly we download the juicefs binary and do the same as Step 1 to get GCP credentials and open cloudsql proxy.

Note that the follow commands are run in Colab environment so there is a ! mark in the beginning for running shell command.

  1. Download juicefs to Colab runtime instance

    ! curl -L -o juicefs.tar.gz
    ! tar -xf juicefs.tar.gz
  2. Set up Google Cloud credentials

    ! gcloud auth application-default login
  3. Open cloud_sql_proxy

    ! wget -O cloud_sql_proxy
    ! chmod +x cloud_sql_proxy
    ! GOOGLE_APPLICATION_CREDENTIALS=/content/.config/application_default_credentials.json nohup ./cloud_sql_proxy -instances=juicefs-learning:europe-west1:juicefs-sql-example-1=tcp: >> cloud_sql_proxy.log &
  4. Mount JuiceFS file system myvolumn onto folder mnt

    ! GOOGLE_APPLICATION_CREDENTIALS=/content/.config/application_default_credentials.json nohup ./juicefs mount  "postgres://postgres:mushroom1@localhost:5432/juicefs?sslmode=disable" mnt > juicefs.log &

Now you should be able to use the folder mnt as if it is a local file system folder to write and read folders and files in it.

Step 3 - Loading data at another time or another instance

Now as you have data stored in Step 2 in your JuiceFS file system, you can repeat all the operations mentioned in Step 2 anytime in any other machines so to have access again to the data previously stored or to store more data into it.

Congratulations! Now you have learned how to use JuiceFS and specifically how to use it with Google Colab to conveniently sharing and storing data files in a distributed fashion.

A demo Colab notebook using JuiceFS can be seen here.

Happy coding :)