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JSON string object with nested Array and Struct column to dataframe in pyspark

New Contributor

I am trying to convert JSON string stored in variable into spark dataframe without specifying schema, because I have a big number of different tables, so it has to be dynamically. I managed to do it with sc.parallelize, but since we are moving to Unity Catalog, I had to create a Shared Compute cluster, so now sc.parallelize and some other libraries are not working.

I have prepared 3 different JSON strings stored in variable that looks something like this, but originally it has much more rows. I need it to work for all 3 examples.

Onedrive file: JSON conversion sample.dbc

Here is the example of code that is working with Single user cluster, but not with Shared Compute:

import json

data_df = sc.parallelize(value_json).map(lambda x: json.dumps(x))
data_final_df =
data_final_df = data_final_df.toDF(*(c.replace('@odata.', '_odata_').replace('.', '_') for c in data_final_df.columns))






Community Manager
Community Manager

Hi @filipjankovic, Since you have multiple tables and need dynamic schema inference, I recommend using the following approach:

  1. Schema Inference from JSON String: You can infer the schema from the JSON string and then create a DataFrame.

  2. Schema Inference from First Row: If you want to infer the schema from the first row of your JSON data, you can follow this approach:

    ## Assuming you have a DataFrame with a column 'temp_json_string
    # Get the first row's JSON string
    val json ="temp_json_string").collect()(0).getString(0)
    # Infer schema from the JSON string
    val ddlSchema: String = spark.sql(s"select schema_of_json('${json}')").collect()(0).getString(0)
    # Create DataFrame with inferred schema
    val data_final_df =

Remember to replace df with your actual DataFrame containing the JSON strings. These approaches should work regardless of whether you’re using a Single User cluster or a Shared Compute cluster. Let me know if you need further assistance!

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