When we try to read multi line json we have to provide .option("multiLine","true"), otherwise it fails with AnalysisException. Why is this not needed for nested json. it works with out this "multiline" option. Can you please tell why?
Hi Manish, Thank you for the videos , its really helpful, One small question, for csv file reading corrupt data we had to create our schema with _corrupt_record column, however for json , how come it is not needed
One of the reasons: JSON is a semi-structured data format, meaning it allows for nested data structures and varied schemas. When reading JSON files, Spark uses a schema inference mechanism that can accommodate this flexibility. If it encounters a record that doesn't conform to the expected structure, it can easily isolate the entire record as a corrupt entry and store it in the _corrupt_record column. CSV files are structured data formats that expect a uniform schema across all rows. If a CSV record deviates from this structure (e.g., missing fields, extra fields, or improperly formatted data), Spark cannot automatically infer how to handle the corruption without an explicit schema definition. This is why you need to define your schema, including a _corrupt_record column if you want to catch those corrupt records.
I have to ingect json file or CSV file in adf then we have to create dataflow means we have to use different transformation after that we to write to databricks but The databricks part not seen any vedio. Either they are using only one databricks or adf to ingect CSV or json file , i need how to connect json file from adf and write into databricks
Notebook detached ×Exception when creating execution context: java.util.concurrent.TimeoutException: Timed out after 15 seconds Getting this error while executing after creating a new cluster.
Bahut mehnat lag rhi kya bhai. Kaam me to aur jada lagega fir. Thora mehnat kar lijiye, it will help you only. Many people still get confused when I ask them to find an error in the file, Thora copy paste kijiyega to dekhiyega data and structure ko v. May be aapko pata hoga lekin sab ek level par nhi honge na.
I think you got confused with the spark fundamental playlist. There are two playlist and each has it's own numbering. Please check playlist and let me know if there is some mistakes in lecture numbering
corrupted record didn't gave me the the _corrupt_record. It is only giving 1 line record of age 20 df_corrupted_json = spark.read.format("json").option("inferSchema","true").option("mode","FAILFAST").option("multiline","true").load("/FileStore/tables/corrupted_json.json") df_corrupted_json.show()
Same i have also not getting _corrupt_record. df_emp_create_scehma=spark.read.format("csv")\ .option("header","true")\ .option("inferschema","true")\ .schema(my_scehma)\ .option("badRecordsPath","/FileStore/tables/gh/bad_records")\ .load("/FileStore/tables/EMP.csv") df_emp_create_scehma.show()
