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https://issues.apache.org/jira/browse/SPARK-16641?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-16641:
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Labels: bulk-closed (was: )
> Add an Option to Create a Dataset With a Case Class, Ignoring Column Names
> (Using ordinal instead)
> --------------------------------------------------------------------------------------------------
>
> Key: SPARK-16641
> URL: https://issues.apache.org/jira/browse/SPARK-16641
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Pat McDonough
> Priority: Minor
> Labels: bulk-closed
>
> When working with a CSV that has no header row, there isn't a concise method
> to create a Dataset using a case class. An option to map fields by ordinal
> rather than field name would be great.
> For example, given the following case class:
> {code}
> case class Part(partkey: Int, name: String, mfgr: String, brand: String,
> _type: String, size: Int, container: String, retailprice: Double,
> comments: String)
> {code}
> I'd like to use the following:
> {code}
> val parts = spark.read.option("delimiter", "|").option("header", "false")
> .csv("dbfs:/databricks-datasets/tpch/data-001/part/").as[Part]
> {code}
> But that won't work because the field names (_c0, _c1, _c2...) do not match
> the Case class field names.
> Instead, I end up writing a bunch of extra conversion code in a map function.
> {code}
> val parts = spark.read.option("delimiter", "|").option("header", "false")
> .csv("dbfs:/databricks-datasets/tpch/data-001/part/")
> .map(p =>
> new part(p.getString(0).trim().toInt, p.getString(1), p.getString(2),
> p.getString(3), p.getString(4), p.getString(5).trim().toInt, p.getString(6),
> p.getString(7).trim().toDouble, p.getString(8)))
> {code}
> CC: [~rxin]
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