Incorrect reading csv format with inferSchema

[Tracy_]

Hi All,

There is a CSV with a column ID (format: 8-digits & "D" at the end).

When trying to read a csv with .option("inferSchema", "true"), it returns the ID as double and trim the "D". Is there any idea (apart from inferSchema=False) to get correct result? Thanks for help!

Below options was tried and also failed.

options(delimiter=",", sep = ",", header=True, inferSchema=True,multiline=True, quote="\"", escape="\"")

[Ajay-Pandey]

Hi @tracy ng​ 

By default, spark treating as a double value for all the numbers ending with D or F

I think you should connect with databricks regarding this.

[Tracy_]

Thanks @Ajay Pandey​.

[Hubert-Dudek]

It can be solved, for example, by specifying the schema with ID as string.

If you don't want to specify schema, better would be autoloader as you can specify hint:

spark.readStream
  .format("cloudFiles")
  .option("cloudFiles.format", "csv")
  .option("cloudFiles.schemaLocation", checkpoint_path)
  .option("cloudFiles.schemaHints", "ID String")
  .option("inferSchema", True)
  .option("mergeSchema", True)
  .load(folder)

[Tracy_]

Thanks @Hubert Dudek​ ,

Becasue the reading file is used to loop different source from different directory with different schema and dynamic column name (sometimes named ID, sometime named SID etc.), it seems the autoloader is not applicable to the case. I wonder is there any option to disable this feature (treating column as double when there is trailing D).

[Anonymous]

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