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https://issues.apache.org/jira/browse/FLINK-2186?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15859691#comment-15859691
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ASF GitHub Bot commented on FLINK-2186:
---------------------------------------
Github user tonycox commented on a diff in the pull request:
https://github.com/apache/flink/pull/3012#discussion_r100337777
--- Diff:
flink-scala/src/main/scala/org/apache/flink/api/scala/ExecutionEnvironment.scala
---
@@ -348,6 +349,47 @@ class ExecutionEnvironment(javaEnv: JavaEnv) {
wrap(new DataSource[T](javaEnv, inputFormat, typeInfo,
getCallLocationName()))
}
+ def readCsvFileAsRow[T : ClassTag : TypeInformation](
+ filePath: String,
+ rowSize: Int,
+ additionalTypes: Map[Int, Class[_]] = null,
+ lineDelimiter: String = "\n",
+ fieldDelimiter: String = ",",
+ quoteCharacter: Character = null,
+ ignoreFirstLine: Boolean = false,
+ ignoreComments: String = null,
+ lenient: Boolean = false,
+ includedFields: Array[Int] = null): DataSet[Row] = {
--- End diff --
Or 3 tabs :)
> Rework CSV import to support very wide files
> --------------------------------------------
>
> Key: FLINK-2186
> URL: https://issues.apache.org/jira/browse/FLINK-2186
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library, Scala API
> Reporter: Theodore Vasiloudis
> Assignee: Anton Solovev
>
> In the current readVcsFile implementation, importing CSV files with many
> columns can become from cumbersome to impossible.
> For example to import an 11 column file we need to write:
> {code}
> val cancer = env.readCsvFile[(String, String, String, String, String, String,
> String, String, String, String,
> String)]("/path/to/breast-cancer-wisconsin.data")
> {code}
> For many use cases in Machine Learning we might have CSV files with thousands
> or millions of columns that we want to import as vectors.
> In that case using the current readCsvFile method becomes impossible.
> We therefore need to rework the current function, or create a new one that
> will allow us to import CSV files with an arbitrary number of columns.
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