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https://issues.apache.org/jira/browse/SPARK-21187?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16821849#comment-16821849
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Florian Wilhelm commented on SPARK-21187:
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I know that this actually does not help with resolving this issue, but for the
time being I wrote up a little workaround how to still use Spark's `pandas_udf`
and Arrow with Spark dataframes containing complex types. I hope it's of some
use for PySpark users until this issue is fixed.
[https://florianwilhelm.info/2019/04/more_efficient_udfs_with_pyspark/]
> Complete support for remaining Spark data types in Arrow Converters
> -------------------------------------------------------------------
>
> Key: SPARK-21187
> URL: https://issues.apache.org/jira/browse/SPARK-21187
> Project: Spark
> Issue Type: Umbrella
> Components: PySpark, SQL
> Affects Versions: 2.3.0
> Reporter: Bryan Cutler
> Assignee: Bryan Cutler
> Priority: Major
>
> This is to track adding the remaining type support in Arrow Converters.
> Currently, only primitive data types are supported. '
> Remaining types:
> * -*Date*-
> * -*Timestamp*-
> * *Complex*: Struct, -Array-, Arrays of Date/Timestamps, Map
> * -*Decimal*-
> * -*Binary*-
> * Categorical when converting from Pandas
> Some things to do before closing this out:
> * -Look to upgrading to Arrow 0.7 for better Decimal support (can now write
> values as BigDecimal)-
> * -Need to add some user docs-
> * -Make sure Python tests are thorough-
> * Check into complex type support mentioned in comments by [~leif], should
> we support mulit-indexing?
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