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https://issues.apache.org/jira/browse/SPARK-18853?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-18853:
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Assignee: (was: Apache Spark)
> Project (UnaryNode) is way too aggressive in estimating statistics
> -------------------------------------------------------------------
>
> Key: SPARK-18853
> URL: https://issues.apache.org/jira/browse/SPARK-18853
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Reporter: Reynold Xin
>
> We currently define statistics in UnaryNode:
> {code}
> override def statistics: Statistics = {
> // There should be some overhead in Row object, the size should not be
> zero when there is
> // no columns, this help to prevent divide-by-zero error.
> val childRowSize = child.output.map(_.dataType.defaultSize).sum + 8
> val outputRowSize = output.map(_.dataType.defaultSize).sum + 8
> // Assume there will be the same number of rows as child has.
> var sizeInBytes = (child.statistics.sizeInBytes * outputRowSize) /
> childRowSize
> if (sizeInBytes == 0) {
> // sizeInBytes can't be zero, or sizeInBytes of BinaryNode will also be
> zero
> // (product of children).
> sizeInBytes = 1
> }
> child.statistics.copy(sizeInBytes = sizeInBytes)
> }
> {code}
> This has a few issues:
> 1. This can aggressively underestimate the size for Project. We assume each
> array/map has 100 elements, which is an overestimate. If the user projects a
> single field out of a deeply nested field, this would lead to huge
> underestimation. A safer sane default is probably 2.
> 2. It is not a property of UnaryNode to propagate statistics this way. It
> should be a property of Project.
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