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https://issues.apache.org/jira/browse/SPARK-32479?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Weichen Xu resolved SPARK-32479.
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Resolution: Not A Bug
> Fix the slicing logic in createDataFrame when converting pandas dataframe to
> arrow table
> ----------------------------------------------------------------------------------------
>
> Key: SPARK-32479
> URL: https://issues.apache.org/jira/browse/SPARK-32479
> Project: Spark
> Issue Type: Story
> Components: PySpark
> Affects Versions: 3.1.0
> Reporter: Liang Zhang
> Assignee: Liang Zhang
> Priority: Major
>
> h1. Problem:
> In
> [https://github.com/databricks/runtime/blob/84a952313ae73e3df32f065eb00cc0bcb024af14/python/pyspark/sql/pandas/conversion.py#L418|https://github.com/databricks/runtime/blob/84a952313ae73e3df32f065eb00cc0bcb024af14/python/pyspark/sql/pandas/conversion.py#L418,]
> , the slicing logic may result in less partitions than specified.
> h1. Example:
> Assume:
> {noformat}
> length = 100 -> [0, 1, ..., 99]
> num_slices = 99 = self.sparkContext.defaultParallelism{noformat}
> Old method:
> step = math.ceil(length / num_slices) = 2
> start = i * step, end = (i + 1) * step:
> output: [0,1] [2,3] [4,5] ... [98,99] -> 50 slices != num_slices
>
> h1. Solution:
> We can use a silimar logic as in
> [https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala#L125]
> {code:python}
> # replace conversion.py#L418
> pdf_slices = (pdf.iloc[i * length // num_slices: (i + 1) * length //
> num_slices] for i in xrange(0, num_slices))
> {code}
> New method:
> start = i * length // num_slices, end = (i + 1) * length // num_slices:
> output: [0] [1] [2] ... [98,99] -> 99 slices
>
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