How to handle complex json schema

[chrisf_sts]

I have a mounted external directory that is an s3 bucket with multiple subdirectories containing call log files in json format.  The files are irregular and complex, when i try to use spark.read.json or spark.sql (SELECT *) i get the UNABLE_TO_INFER_SCHEMA error.  the files are too complex to try and build a schema manually, plus there are thousands of files.  what is the best approach for creating a dataframe with this data?

[Kaniz_Fatma]

Hi @chrisf_sts, One possible approach is to use the spark.read.option("multiline", "true") method to read multi-line.... This option allows Spark to handle JSON objects that span multiple lines. You can also use the inferSchema option to let Spark infer the schema of the JSON data automatically.

Another possible approach is to use the explode function to flatten the nested arrays and structures.... This function creates a new row for each element in the given array or map column. You can then select the columns you need from the exploded DataFrame.

I hope this helps you with your problem. If you have any other questions, feel free to ask me. 😊