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