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https://issues.apache.org/jira/browse/SPARK-10520?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14737757#comment-14737757
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Vincent Warmerdam edited comment on SPARK-10520 at 9/9/15 10:58 PM:
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It just occured to me that there is a very similar error with machine learning.
In R you can pass a date/timestamp into a model and it will treat it as if it
were a numeric.
{code}
> df <- data.frame(d = as.Date('2014-01-01') + 1:100, r = runif(100) + 0.5 *
> 1:100)
> lm(r ~ d, data = df)
Call:
lm(formula = r ~ d, data = df)
Coefficients:
(Intercept) d
-7994.9971 0.4975
{code}
I'm not sure if Spark wants to have similar support but it may be something to
keep in mind; the problem seems similar.
was (Author: cantdutchthis):
It just occured to me that there is a very similar error with machine learning.
In R you can pass a date/timestamp into a model and it will treat it as if it
were a numeric.
```
> df <- data.frame(d = as.Date('2014-01-01') + 1:100, r = runif(100) + 0.5 *
> 1:100)
> lm(r ~ d, data = df)
Call:
lm(formula = r ~ d, data = df)
Coefficients:
(Intercept) d
-7994.9971 0.4975
```
I'm not sure if Spark wants to have similar support but it may be something to
keep in mind; the problem seems similar.
> dates cannot be summarised in SparkR
> ------------------------------------
>
> Key: SPARK-10520
> URL: https://issues.apache.org/jira/browse/SPARK-10520
> Project: Spark
> Issue Type: Bug
> Components: SparkR, SQL
> Affects Versions: 1.5.0
> Reporter: Vincent Warmerdam
>
> I create a simple dataframe in R and call the summary function on it
> (standard R, not SparkR).
> {code}
> > library(magrittr)
> > df <- data.frame(
> date = as.Date("2015-01-01") + 0:99,
> r = runif(100)
> )
> > df %>% summary
> date r
> Min. :2015-01-01 Min. :0.01221
> 1st Qu.:2015-01-25 1st Qu.:0.30003
> Median :2015-02-19 Median :0.46416
> Mean :2015-02-19 Mean :0.50350
> 3rd Qu.:2015-03-16 3rd Qu.:0.73361
> Max. :2015-04-10 Max. :0.99618
> {code}
> Notice that the date can be summarised here. In SparkR; this will give an
> error.
> {code}
> > ddf <- createDataFrame(sqlContext, df)
> > ddf %>% summary
> Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
> org.apache.spark.sql.AnalysisException: cannot resolve 'avg(date)' due to
> data type mismatch: function average requires numeric types, not DateType;
> at
> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:61)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:53)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:293)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:293)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:292)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:290)
> at org.apache.spark.sql.
> {code}
> This is a rather annoying bug since the SparkR documentation currently
> suggests that dates are now supported in SparkR.
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