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Posted to issues@spark.apache.org by "Felix Cheung (JIRA)" <ji...@apache.org> on 2017/02/13 17:54:41 UTC

[jira] [Resolved] (SPARK-19342) Datatype tImestamp is converted to numeric in collect method

     [ https://issues.apache.org/jira/browse/SPARK-19342?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Felix Cheung resolved SPARK-19342.
----------------------------------
    Resolution: Fixed

> Datatype tImestamp is converted to numeric in collect method 
> -------------------------------------------------------------
>
>                 Key: SPARK-19342
>                 URL: https://issues.apache.org/jira/browse/SPARK-19342
>             Project: Spark
>          Issue Type: Bug
>          Components: SparkR
>    Affects Versions: 2.1.0
>            Reporter: Fangzhou Yang
>            Assignee: Fangzhou Yang
>             Fix For: 2.1.1, 2.2.0
>
>
> Get double instead of POSIX in collect method for timestamp column datatype, when NA exists at the top of the column.
> The following codes and outputs show that, how the bug can be reproduced:
> {code}
> > sparkR.session(master = "local")
> Spark package found in SPARK_HOME: /home/titicaca/spark-2.1
> Launching java with spark-submit command /home/titicaca/spark-2.1/bin/spark-submit   sparkr-shell /tmp/RtmpqmpZUg/backend_port363a898be92 
> Java ref type org.apache.spark.sql.SparkSession id 1 
> > df <- data.frame(col1 = c(0, 1, 2), 
> +                  col2 = c(as.POSIXct("2017-01-01 00:00:01"), NA, as.POSIXct("2017-01-01 12:00:01")))
> > sdf1 <- createDataFrame(df)
> > print(dtypes(sdf1))
> [[1]]
> [1] "col1"   "double"
> [[2]]
> [1] "col2"      "timestamp"
> > df1 <- collect(sdf1)
> > print(lapply(df1, class))
> $col1
> [1] "numeric"
> $col2
> [1] "POSIXct" "POSIXt" 
> > sdf2 <- filter(sdf1, "col1 > 0")
> > print(dtypes(sdf2))
> [[1]]
> [1] "col1"   "double"
> [[2]]
> [1] "col2"      "timestamp"
> > df2 <- collect(sdf2)
> > print(lapply(df2, class))
> $col1
> [1] "numeric"
> $col2
> [1] "numeric"
> {code}
> As we can see, the data type of col2 is converted to numberic unexpectedly in the collected local data frame df2



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