[jira] [Commented] (SPARK-17426) Current TreeNode.toJSON may trigger OOM under some corner cases

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15469557#comment-15469557 ] 

Apache Spark commented on SPARK-17426:
--------------------------------------

User 'clockfly' has created a pull request for this issue:
https://github.com/apache/spark/pull/14990

> Current TreeNode.toJSON may trigger OOM under some corner cases
> ---------------------------------------------------------------
>
>                 Key: SPARK-17426
>                 URL: https://issues.apache.org/jira/browse/SPARK-17426
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Sean Zhong
>
> In SPARK-17356, we fix the OOM issue when Metadata is super big. There are other cases that may also trigger OOM. Current implementation of TreeNode.toJSON will recursively search and print all fields of current TreeNode, even if the field's type is of type Seq or type Map. 
> This is not safe because:
> 1. the Seq or Map can be very big. Converting them to JSON make take huge memory, which may trigger out of memory error.
> 2. Some user space input may also be propagated to the Plan. The user space input can be of arbitrary type, and may also be self-referencing. Trying to print user space input to JSON is very risky.
> The following example triggers a StackOverflowError when calling toJSON on a plan with user defined UDF.
> {code}
> case class SelfReferenceUDF(
>     var config: Map[String, Any] = Map.empty[String, Any]) extends Function1[String, Boolean] {
>   config += "self" -> this
>   def apply(key: String): Boolean = config.contains(key)
> }
> test("toJSON should not throws java.lang.StackOverflowError") {
>   val udf = ScalaUDF(SelfReferenceUDF(), BooleanType, Seq("col1".attr))
>   // triggers java.lang.StackOverflowError
>   udf.toJSON
> }
> {code}



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