[jira] [Resolved] (SPARK-16641) Add an Option to Create a Dataset With a Case Class, Ignoring Column Names (Using ordinal instead)

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

     [ https://issues.apache.org/jira/browse/SPARK-16641?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-16641.
----------------------------------
    Resolution: Incomplete

> 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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