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The Wherobots Notebook Environment is the main development interface for developers and data scientists working with WherobotsDB in Wherobots Cloud. In this post we’ll take a look at configuring and starting a notebook environment then see how to work with WherobotsDB via Python and Spatial SQL.
This is the second post in a series that will introduce Wherobots Cloud, covering how to get started with cloud-native geospatial analytics at scale.
After signing in to Wherobots Cloud you’ll be prompted to configure and start a notebook runtime. Anyone can create a Wherobots Cloud account and it’s free to get started.
Free tier users have access to the “Tiny” runtime with a default resource configuration suitable for development and testing. Professional tier users are able to select preconfigured runtimes with more resources or create custom runtimes.
We can also add additional configuration such as AWS S3 bucket credentials or additional Spark configuration before starting the runtime and also select additional Python libraries to be installed into our environment. Many PyData and geospatial Python packages are installed by default so we can typically get started with just the default runtime configuration by clicking the “Start” button.
Starting the runtime creates a Jupyter notebook environment specific to our Wherobots Cloud user. For free tier users this notebook environment will run for 2 hours then shutdown after which we’ll need to restart the runtime, while professional tier users don’t have this restriction.
Once our notebook runtime is available we can enter the Jupyter environment by clicking “Open Notebook”. By default we’ll land on a sample notebook which introduces some basic features of SedonaDB. If you’re not familiar with Jupyter it’s an interactive development environment organized around notebooks. Notebooks are made up of cells which can be code or also include text, images, or other interactive widgets. We can run cells individually or from the menu by selecting Run >> Run All Cells to run all cells in the notebook sequentially.
This initial notebook is a simple example meant to introduce some concepts and familiarize new users with working with SedonaDB. It covers:
SedonaContext
This initial notebook is just a starting point and you can also find a number of additional example notebooks in the notebook_example directory in Jupyter. Specifically,
notebook_example
sedona
sedona-example-python.ipynb
sedona-overture-maps.ipynb
havasu
havasu-iceberg-geometry-etl.ipynb
havasu-iceberg-raster-etl.ipynb
havasu-iceberg-outdb-raster-etl.ipynb
sedonamaps
sedonamaps_example.ipynb
Note: Only 1 notebook can be run at a time. If you want to run another notebook, please shut down the kernel of the current notebook first (See instructions here).
Now you can of course create your own notebooks to work with spatial data in Wherobots Cloud. Once you’ve created a notebook there are a few ways to export it, for example you can download the notebook locally but a common workflow is to check notebook changes into version control and then push changes to a system like GitHub or GitLab.
Our Jupyter environment has git installed so we can check our notebooks into version control from the terminal. This is also a good way to import new notebooks. For example, let’s bring in some notebooks from this repository by opening the terminal in Jupyter and running the following command:
git
git clone https://github.com/johnymontana/30-day-map-challenge
And now these notebooks are available in our Wherobots notebook environment, so as we make changes to them we can check them in to version control and push those back to our GitHub repository.
As you’re working with Wherobots Cloud be sure to join the Wherobots Community site where you can ask questions if you get stuck and also share your projects with the community.
Here are some other resources you might find useful as you explore SedonaDB and Wherobots Cloud:
This was a quick introduction to the Wherobots Notebook environment. I hope you’ll enjoy working with Wherobots Cloud and I hope to see you around the Wherobots Community Site!
Key takeaways
Sign in to Wherobots Cloud, choose a runtime (Tiny on the free tier), optionally add S3 credentials, Spark config, or extra Python libraries, click Start, then Open Notebook.
Free-tier Tiny runtimes run for 2 hours then shut down and must be restarted. Professional-tier runtimes are not described as having that limit.
In the Jupyter notebook_example directory: sedona (Shapefiles, Overture), havasu (vector/raster Iceberg ETL, including out-DB rasters), and sedonamaps (map matching GPS to OSM).
Yes. git is available in the Jupyter terminal for cloning projects in and committing notebooks out to GitHub or GitLab.
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