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https://issues.apache.org/jira/browse/SPARK-11167?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14970304#comment-14970304
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Felix Cheung commented on SPARK-11167:
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Shouldn't it try to infer the tightest type that could accommodate the
heterogeneous values? This is the strategy used by spark csv package, for
example, I'm sure there are others:
https://github.com/databricks/spark-csv/blob/master/src/main/scala/com/databricks/spark/csv/util/InferSchema.scala
> Incorrect type resolution on heterogeneous data structures
> ----------------------------------------------------------
>
> Key: SPARK-11167
> URL: https://issues.apache.org/jira/browse/SPARK-11167
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Affects Versions: 1.6.0
> Reporter: Maciej Szymkiewicz
>
> If structure contains heterogeneous incorrectly assigns type of the
> encountered element as type of a whole structure. This problem affects both
> lists:
> {code}
> SparkR:::infer_type(list(a=1, b="a")
> ## [1] "array<double>"
> SparkR:::infer_type(list(a="a", b=1))
> ## [1] "array<string>"
> {code}
> and environments:
> {code}
> SparkR:::infer_type(as.environment(list(a=1, b="a")))
> ## [1] "map<string,double>"
> SparkR:::infer_type(as.environment(list(a="a", b=1)))
> ## [1] "map<string,string>"
> {code}
> This results in errors during data collection and other operations on
> DataFrames:
> {code}
> ldf <- data.frame(row.names=1:2)
> ldf$foo <- list(list("1", 2), list(3, 4))
> sdf <- createDataFrame(sqlContext, ldf)
> collect(sdf)
> ## 15/10/17 17:58:57 ERROR Executor: Exception in task 0.0 in stage 9.0 (TID
> 9)
> ## scala.MatchError: 2.0 (of class java.lang.Double)
> ## ...
> {code}
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