Serving Jupyter Notebooks to Thousands of Users
In our organization, Colaberry Inc, we provide professionals from various backgrounds and various levels of experience, with the platform and the opportunity to learn Data Analytics and Data Science. In order to teach Data Science, the Jupyter Notebook platform is one of the most important tools. A Jupyter Notebook is a document within an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
In this blog, we will learn the basic architecture of JupyterHub, the multi-user jupyter notebook platform, its working mechanism, and finally how to set up jupyter notebooks to serve a large user base.
Why Jupyter Notebooks?
In our platform, refactored.ai we provide users an opportunity to learn Data Science and AI by providing courses and lessons on Data Science and machine learning algorithms, the basics of the python programming language, and topics such as data handling and data manipulation.
Our approach to teaching these topics is to provide an option to “Learn by doing”. In order to provide practical hands-on learning, the content is delivered using the Jupyter Notebooks technology.
Jupyter notebooks allow users to combine code, text, images, and videos in a single document. This also makes it easy for students to share their work with peers and instructors. Jupyter notebook also gives users access to computational environments and resources without burdening the users with installation and maintenance tasks.
Limitations
One of the limitations of the Jupyter Notebook server is that it is a single-user environment. When you are teaching a group of students learning data science, the basic Jupyter Notebook server falls short of serving all the users.
JupyterHub comes to our rescue when it comes to serving multiple users, with their own separate Jupyter Notebook servers seamlessly. This makes JupyterHub equivalent to a web application that could be integrated into any web-based platform, unlike the regular jupyter notebooks.
JupyterHub Architecture
The below diagram is a visual explanation of the various components of the JupyterHub platform. In the subsequent sections, we shall see what each component is and how the various components work together to serve multiple users with jupyter notebooks.
Components of JupyterHub
Notebooks
At the core of this platform are the Jupyter Notebooks. These are live documents that contain user code, write-up or documentation, and results of code execution in a single document. The contents of the notebook are rendered in the browser directly. They come with a file extension .ipynb. The figure below depicts how a jupyter notebook looks:
Notebook Server
As mentioned above, the notebook servers serve jupyter notebooks as .ipynb files. The browser loads the notebooks and then interacts with the notebook server via sockets. The code in the notebook is executed in the notebook server. These are single-user servers by design.
Hub
Hub is the architecture that supports serving jupyter notebooks to multiple users. In order to support multiple users, the Hub uses several components such as Authenticator, User Database, and Spawner.
Authenticator
This component is responsible for authenticating the user via one of the several authentication mechanisms. It supports OAuth, GitHub, and Google to name a few of the several available options. This component is responsible for providing an Auth Token after the user is successfully authenticated. This token is used to provide access for the corresponding user.
Refer to JupyterHub documentation for an exhaustive list of options. One of the notable options is using an identity aggregator platform such as Auth0 that supports several other options.
User Database
Internally, Jupyter Hub uses a user database to store the user information to spawn separate user pods for the logged-in user and then serve notebooks contained within the user pods for individual users.
Spawner
A spawner is a worker component that creates individual servers or user pods for each user allowed to access JupyterHub. This mechanism ensures multiple users are served simultaneously. It is to be noted that there is a predefined limitation on the number of the simultaneous first-time spawn of user pods, which is roughly about 80 simultaneous users. However, this does not impact the regular usage of the individual servers after initial user pod creation.
How It All Works Together
The mechanism used by JupyterHub to authenticate multiple users and provide them with their own Jupyter Notebook servers is described below.
The user requests access to the Jupyter notebook via the JupyterHub (JH) server.
The JupyterHub then authenticates the user using one of the configured authentication mechanisms such as OAuth. This returns an auth token to the user to access the user pod.
A separate Jupyter Notebook server is created and the user is provided access to it.
The requested notebook in that server is returned to the user in the browser.
The user then writes code (or documentation text) in the notebook.
The code is then executed in the notebook server and the response is returned to the user’s browser.
Deployment and Scalability
The JupyterHub servers could be deployed in two different approaches:
Deployed on the cloud platforms such as AWS or Google Cloud platform. This uses Docker and Kubernetes clusters in order to scale the servers to support thousands of users.
A lightweight deployment on a single virtual instance to support a small set of users.
Scalability
In order to support a few thousand users and more, we use the Kubernetes cluster deployment on the Google Cloud platform. Alternatively, this could also have been done on the Amazon AWS platform to support a similar number of users.
This uses a Hub instance and multiple user instances each of which is known as a pod. (Refer to the architecture diagram above). This deployment architecture scales well to support a few thousand users seamlessly.
To learn more about how to set up your own JupyterHub instance, refer to the Zero to JupyterHub documentation.
Conclusion
JupyterHub is a scalable architecture of Jupyter Notebook servers that supports thousands of users in a maintainable cluster environment on popular cloud platforms.
This architecture suits several use cases with thousands of users and a large number of simultaneous users, for example, an online Data Science learning platform such as refactored.ai