Re: question about pyarrow.Table to pyspark.DataFrame conversion

[Bryan Cutler <cu...@gmail.com>]

Hi Artem,

I don't believe this is currently possible, but it could be a great
addition to PySpark since this would offer a convenient and efficient way
to parallelize nested column data. I created the JIRA
https://issues.apache.org/jira/browse/SPARK-29040 for this.

On Tue, Aug 27, 2019 at 7:55 PM Artem Kozhevnikov <
kozhevnikov.artem@gmail.com> wrote:

> I wonder if there's some recommended method to convert in memory
> pyarrow.Table (or pyarrow.BatchRecord) to pyspark.Dataframe without using
> pandas ?
> My motivation is about converting nested data (like List[int]) that have
> an efficient representation in pyarrow which is not possible with Pandas (I
> don't want to pass by python list of int ...).
>
> Thanks in advance !
> Artem
>
>
>

Re: question about pyarrow.Table to pyspark.DataFrame conversion

[shouheng <sh...@gmail.com>]

Hi Bryan,

I came across  SPARK-29040
<https://issues.apache.org/jira/browse/SPARK-29040>   and I'm very excited
that others are looking for such feature as well. It will be tremendously
useful if we can implement this feature.

Currently, my workaround is to serialize `pyarrow.Table` to a parquet file,
then let Spark to read that parquet file. I avoided using `pd.Dataframe`,
same as what Artem mentioned above.

Do you think this ticket has a chance to get prioritized?

Thank you very much.

Best,
Shouheng



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