Parse nested json for building footprints

[Innov]

Looking for some help. If anyone has worked with nested json file in Databricks notebook. I am trying to parse nested json file to get coordinates and use that to create polygon for footprint. Do I need to read it as txt? How can I use the Databricks notebook to do transformation on the file and use that to create polygon, centroids and group the other columns. 

[Kaniz_Fatma]

Hi @Innov, Working with nested JSON files in Databricks Notebooks is a common task, and I can guide you through the process.

Let’s break it down step by step:

1. Reading the Nested JSON File:

   • You don’t need to read the JSON file as plain text (.txt). Instead, use Databricks’ built-in capabilities to read JSON directly into a DataFrame.
   • Start by passing your sample JSON string to the reader. You can use the spark.read.json method to read the nested JSON data.

2. Flattening the Nested Structure:

   • Nested JSON structures can be flattened using the $"column.*" and explode methods.
   • For example, if your JSON contains nested coordinates, you can explode them to create separate rows for each coordinate.
   • Here’s an example using Scala:

     // Read the JSON file
     val source_df = spark.read.json("path/to/your/nested.json")
     
     // Explode the nested coordinates
     val exploded_df = source_df.select($"column_name.*").explode("coordinates", "coordinate") // Adjust column names as needed
     

3. Creating Polygons and Centroids:

   • Once you have the flattened DataFrame, you can create polygons and centroids.
   • For polygons, you’ll need to group the coordinates appropriately. You can use the ST_PolygonFromEnvelope function (if your coordinates represent bounding boxes) or other geometry functions.
   • For centroids, calculate the average of the latitude and longitude within each polygon.

4. Grouping Other Columns:

   • You can group other columns as needed. Use aggregation functions like groupBy and apply relevant operations (e.g., sum, avg, etc.) to those columns.

5. Persisting the Results:

   • Finally, persist the transformed data into a new DataFrame or write it to a storage location (e.g., Delta table, Parquet files, etc.).

Remember to adjust the column names and data types according to your specific JSON structure. If you’re working with PySpark, similar steps apply, but the syntax will be slightly different.

Feel free to ask if you need further assistance! 🚀

For more detailed examples, you can refer to the official Databricks documentation on nested JSON to DataFrame and flattening nested columns¹².