[jira] [Commented] (SPARK-21978) schemaInference option not to convert strings with leading zeros to int/long

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-21978?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16162749#comment-16162749 ] 

Hyukjin Kwon commented on SPARK-21978:
--------------------------------------

Not sure. It sounds rather a niche use case. As a workaround, we could just disable {{inferSchema}} or manually change it after only getting the schema, manually changing it and setting it. For example:

{code}
schema = spark.read.csv("...", inferSchema=True).schema
# Update `schema`
spark.read.schema(schema).csv("...").show()
{code}

Do you maybe have a reference to support this idea, for example, in {{read.csv}} at R or other CSV parsing libraries?

> schemaInference option not to convert strings with leading zeros to int/long 
> -----------------------------------------------------------------------------
>
>                 Key: SPARK-21978
>                 URL: https://issues.apache.org/jira/browse/SPARK-21978
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 2.1.0, 2.1.1, 2.2.0, 2.3.0
>            Reporter: Ruslan Dautkhanov
>              Labels: csv, csvparser, easy-fix, inference, ramp-up, schema
>
> It would be great to have an option in Spark's schema inference to *not* to convert to int/long datatype a column that has leading zeros. Think zip codes, for example.
> {code}
> df = (sqlc.read.format('csv')
>               .option('inferSchema', True)
>               .option('header', True)
>               .option('delimiter', '|')
>               .option('leadingZeros', 'KEEP')       # this is the new proposed option
>               .option('mode', 'FAILFAST')
>               .load('csvfile_withzipcodes_to_ingest.csv')
>             )
> {code}



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