[
https://issues.apache.org/jira/browse/SPARK-10520?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Reynold Xin updated SPARK-10520:
--------------------------------
Description:
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$$anonfun$5.apply(TreeNode.scala:290)
at org.apache.spark.sql.
{code}
This is a rather annoying bug since the SparkR documentation currently suggests
that dates are now supported in SparkR.
was:
I create a simple dataframe in R and call the summary function on it (standard
R, not SparkR).
```
> library(magrittr)
> df <- data.frame(
date = as.Date("2015-01-01") + 0:99,
r = runif(100)
)
> df %>% summary
date r
Min. :2015-01-01 Min. :0.01221
1st Qu.:2015-01-25 1st Qu.:0.30003
Median :2015-02-19 Median :0.46416
Mean :2015-02-19 Mean :0.50350
3rd Qu.:2015-03-16 3rd Qu.:0.73361
Max. :2015-04-10 Max. :0.99618
```
Notice that the date can be summarised here. In SparkR; this will give an error.
```
> ddf <- createDataFrame(sqlContext, df)
> ddf %>% summary
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: cannot resolve 'avg(date)' due to
data type mismatch: function average requires numeric types, not DateType;
at
org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$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$5.apply(TreeNode.scala:290)
at org.apache.spark.sql.
```
This is a rather annoying bug since the SparkR documentation currently suggests
that dates are now supported in SparkR.
> dates cannot be summarised in SparkR
> ------------------------------------
>
> Key: SPARK-10520
> URL: https://issues.apache.org/jira/browse/SPARK-10520
> Project: Spark
> Issue Type: Bug
> Components: SparkR, SQL
> Affects Versions: 1.5.0
> Reporter: Vincent Warmerdam
>
> I create a simple dataframe in R and call the summary function on it
> (standard R, not SparkR).
> {code}
> > library(magrittr)
> > df <- data.frame(
> date = as.Date("2015-01-01") + 0:99,
> r = runif(100)
> )
> > df %>% summary
> date r
> Min. :2015-01-01 Min. :0.01221
> 1st Qu.:2015-01-25 1st Qu.:0.30003
> Median :2015-02-19 Median :0.46416
> Mean :2015-02-19 Mean :0.50350
> 3rd Qu.:2015-03-16 3rd Qu.:0.73361
> Max. :2015-04-10 Max. :0.99618
> {code}
> Notice that the date can be summarised here. In SparkR; this will give an
> error.
> {code}
> > ddf <- createDataFrame(sqlContext, df)
> > ddf %>% summary
> Error in invokeJava(isStatic = FALSE, objId$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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