Mito Is a JupyterLab Extension to Make Python Data Analysis Easy

Running in Production

In this episode of Running in Production, Nate Rush goes over building a JupyterLab extension with Python. It runs locally in a Jupyter Notebook but they also host JupyterHub instances on a Kubernetes cluster running in EKS. It’s been running in production since late 2020.

Nate talks about building a cross platform Python installer, building and publishing a JupyterLab extension, leveraging Pandas, building the front-end with React / Typescript, hosting a version of their product on a Kubernetes cluster, the value in automating the hard stuff and more.

Topics Include

• 1:15 – What is a Jupyter Notebook and how do JupyterLab extensions work?
• 3:35 – 2 developers are currently building it
• 6:50 – You can run it in an offline notebook or through their hosted service
• 8:48 – Handling local installations across Windows, macOS and Linux
• 15:58 – How users launch their Jupyter Notebook locally with this extension
• 17:16 – How do you create a JupyterLab extension?
• 21:10 – The back-end is built upon Pandas
• 23:31 – How do front-end assets make their way into the extension?
• 28:50 – Webpack is being used to bundle up the front-end assets
• 29:36 – Using GitHub Actions to handle the deploy process for the JupyterLab extension
• 35:20 – Front-end packages to help build a spreadsheet driven UI
• 37:37 – Discovering front-end performance issues and errors through logging
• 45:12 – Hosting JupyterHub instances on Kubernetes with EKS on AWS
• 47:32 – The hosted version is free, but it’s on its way to being phased out
• 51:57 – The workflow for deploying the Kubernetes set up and running 3-10 worker nodes
• 54:56 – Backups of the Kubernetes cluster are handled with Velero
• 56:13 – Route53 is hosting their DNS records
• 57:29 – Best tips? They really love Typescript and also automate as much as possible
• 1:00:58 – Don’t hesitate to ask for help and focus on what your users want
• 1:06:34 – Check out https://trymito.io

Links

📄 References

• http://paulgraham.com/ds.html
• https://docs.anaconda.com/anaconda/navigator/
• https://jupyter.org/hub
• The Phoenix Project book

⚙️ Tech Stack

• jupyter →
• python →
• react →
• aws →
• kubernetes →
• webpack →
• github-actions →
• s3 →
• route53 →
• segment →
• mixpanel →

🛠 Libraries Used

• https://pandas.pydata.org/
• https://github.com/pytest-dev/pytest
• https://github.com/DevExpress/testcafe
• https://www.ag-grid.com/
• https://velero.io/

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