Spark 3.3.0 - guide with Schema Mismatch in Nested JSON Data when Using DataFrames
I need some guidance on I'm trying to configure I recently switched to I'm getting frustrated with I’m currently working with Spark 3.3.0 and trying to load a nested JSON file into a DataFrame. However, I keep working with a schema mismatch behavior when accessing certain nested fields. The JSON structure I'm dealing with looks like this: ```json { "user": { "id": "12345", "name": "John Doe", "address": { "city": "New York", "zip": "10001" } }, "transactions": [ { "amount": 250.0, "date": "2023-01-01" } ] } ``` When I try to read this JSON file with the following code: ```scala val df = spark.read.json("path/to/json") df.printSchema() ``` The schema prints out correctly, but when I attempt to access the nested fields like this: ```scala val userCity = df.select("user.address.city").collect() ``` I get the following behavior: ``` org.apache.spark.sql.AnalysisException: want to resolve 'user.address.city' given input columns: [user, transactions]; ``` I’ve verified that the JSON structure matches what I'm trying to access, and the field names appear to be correct. I also tried using `df.selectExpr("user.address.city")` but received a similar behavior. To troubleshoot, I have printed the DataFrame contents and confirmed the nested structure is intact. Additionally, I am using the latest version of Spark SQL and have ensured that the JSON file is properly formatted. Here’s how I attempted to check the contents: ```scala df.show(false) ``` The output looks good, but accessing nested fields still leads to errors. Could this be an scenario with how Spark interprets the JSON schema, or am I missing something specific about accessing nested fields in Spark DataFrames? Any insights or suggestions would be greatly appreciated! For context: I'm using Scala on Windows. What am I doing wrong? I'm working in a Ubuntu 20.04 environment. Thanks for any help you can provide! What's the best practice here?