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How to Write a Schema for Nested JSON in PySpark

Learn how to effectively write a schema for nested JSON structures in PySpark, avoiding null values and ensuring accurate data types. --- This video is based on the question https://stackoverflow.com/q/74350945/ asked by the user 'Xi12' ( https://stackoverflow.com/u/17867413/ ) and on the answer https://stackoverflow.com/a/74352759/ provided by the user 'Zafar' ( https://stackoverflow.com/u/4373061/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to write a schema for below nested Json pyspark Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Understanding the Problem: Writing Schema for Nested JSON in PySpark When working with PySpark, one common challenge developers face is defining the schema for nested JSON structures. This can be particularly frustrating when the resulting DataFrame contains null values due to incorrect schema definitions. In this guide, we will explore how to write a schema for a given nested JSON structure, ensuring that we correctly define each field according to its datatype, which prevents nulls from appearing in our DataFrame. The Sample JSON Structure Let's consider a sample JSON structure that contains several fields, including nested objects. For instance, we have: [[See Video to Reveal this Text or Code Snippet]] Common Mistakes When Writing Schema A frequent mistake when writing schemas for such nested JSON objects is incorrectly defining the data types, particularly for nested fields. For instance, in an attempt to define a schema for the above JSON, one might encounter null values due to mistakes like: Using StringType for fields that are numeric, such as latitude or longitude. Forgetting to define nested structures properly, leading to misinterpretation of the JSON structure. Defining the Correct Schema To successfully write the schema for the above JSON, we should include the appropriate data types for all fields, particularly ensuring that nested fields are defined correctly. Here's an example of a correctly structured schema in PySpark: [[See Video to Reveal this Text or Code Snippet]] Breakdown of the Schema Top-Level Object: The schema begins with place_results, which is itself an object containing various fields. Data Types: Each field is defined with its corresponding data type: Strings for text fields (e.g. data_cid, title) Double for numeric fields (e.g. rating, latitude, and longitude) Nested Structure: The gps_coordinates field is also defined as a StructType, holding both latitude and longitude together. Implementing the Schema in PySpark To implement this schema, you may read your JSON data as follows: [[See Video to Reveal this Text or Code Snippet]] Conclusion Defining a proper schema in PySpark for nested JSON structures is crucial to avoiding issues like null values. By clearly understanding the structure of your JSON and assigning the appropriate data types, you can efficiently process your data and leverage Spark’s full potential. Give this schema writing a try next time you encounter a nested JSON in PySpark, and see the improvement in your DataFrame without nulls!