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Posted to issues@spark.apache.org by "Milad Khajavi (JIRA)" <ji...@apache.org> on 2016/01/17 11:11:39 UTC
[jira] [Updated] (SPARK-12863) missing api for renaming and mapping
result of operations on GroupedDataset to case classes
[ https://issues.apache.org/jira/browse/SPARK-12863?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Milad Khajavi updated SPARK-12863:
----------------------------------
Description:
Here I struggle with spark api to convert the result of count to convert that to KeyValue case class. I think there is no api for changing the column of Dataset and mapping them to new case class.
case class LogRow(id: String, location: String, time: Long)
case class KeyValue(key: (String, String), value: Long)
val log = LogRow("1", "a", 1) :: and so on
log.toDS().groupBy(l => {(l.id, l.location)}).count().toDF().toDF("key", "value").as[KeyValue].printSchema()
was:
Here I struggle with spark api to convert the result of count to convert that to KeyValue case class. I think there is no api for changing the column of Dataset and mapping them to new case class.
case class LogRow(id: String, location: String, time: Long)
case class KeyValue(key: (String, String), value: Long)
val log = LogRow("1", "a", 1) :: and so on
log.toDS().groupBy(l => {
(l.id, l.location)
}).count().toDF().toDF("key", "value").as[KeyValue].printSchema()
> missing api for renaming and mapping result of operations on GroupedDataset to case classes
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-12863
> URL: https://issues.apache.org/jira/browse/SPARK-12863
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Reporter: Milad Khajavi
>
> Here I struggle with spark api to convert the result of count to convert that to KeyValue case class. I think there is no api for changing the column of Dataset and mapping them to new case class.
> case class LogRow(id: String, location: String, time: Long)
> case class KeyValue(key: (String, String), value: Long)
> val log = LogRow("1", "a", 1) :: and so on
> log.toDS().groupBy(l => {(l.id, l.location)}).count().toDF().toDF("key", "value").as[KeyValue].printSchema()
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