[jira] [Resolved] (SPARK-29316) CLONE - 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-29316?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-29316.
----------------------------------
    Resolution: Won't Fix

> CLONE - schemaInference option not to convert strings with leading zeros to int/long 
> -------------------------------------------------------------------------------------
>
>                 Key: SPARK-29316
>                 URL: https://issues.apache.org/jira/browse/SPARK-29316
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.1.0, 2.1.1, 2.2.0, 2.3.0
>            Reporter: Ambar Raghuvanshi
>            Priority: Critical
>              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:java}
> 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')
>             )
> The general usage of data with trailing 0 is for Identifiers. If they are converted to int/long defeats the purpose of inferSchema. The conversion should be provided on the basis of a flag whether the data should be converted to int/long or not. {code}



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