As alternative: checkpoint the dataframe, collect days, and then delete
corresponding directories using hadoop FileUtils, then write the dataframe
On Fri, Sep 29, 2017 at 10:31 AM, peay <p...@protonmail.com> wrote:
> Hello,
>
> I am trying to use data_frame.write.partitionBy("day").save("dataset.parquet")
> to write a dataset while splitting by day.
>
> I would like to run a Spark job to process, e.g., a month:
> dataset.parquet/day=2017-01-01/...
> ...
>
> and then run another Spark job to add another month using the same folder
> structure, getting me
> dataset.parquet/day=2017-01-01/
> ...
> dataset.parquet/day=2017-02-01/ <http://dataset.parquet/day=2017-01-01/>
> ...
>
> However:
> - with save mode "overwrite", when I process the second month, all of
> dataset.parquet/ gets removed and I lose whatever was already computed for
> the previous month.
> - with save mode "append", then I can't get idempotence: if I run the job
> to process a given month twice, I'll get duplicate data in all the
> subfolders for that month.
>
> Is there a way to do "append in terms of the subfolders from partitionBy,
> but overwrite within each such partitions? Any help would be appreciated.
>
> Thanks!
>
