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There does not appear to be a way to write spark jobs to disk using a set partition scheme. This is normally done via dataframe.write.parquet(<path>, partitionBy=['year']), if one is to partition the data by year, for example. I am looking at the API page here: https://doc.dataiku.com/dss/latest/python-api/pyspark.html, specifically the function: write_with_schema

What are my options here? Since this is an important requirement for us, what's to stop me from simply using the sqlContext to write to a fixed path in HDFS, using the command I gave above? Can this be hacked somehow, or by using a plugin?

I can't seem to look up how to override the write_with_schema call. Following the instructions here: https://doc.dataiku.com/dss/latest/python-api/outside-usage.html - 'spark' does not appear to be a module in the tarball (dataiku-internal-client-5.1.0). Any reason why you are trying to hide that part of the API?

1 Answer

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In order to use partitioning in Dataiku, you need to specify it on the output (and possibly input) dataset. You can find more details on this page: https://doc.dataiku.com/dss/latest/partitions/fs_datasets.html.

If you set it up accordingly, this file system partitioning setup will be applied to all recipes, including those running on Spark.

Hope it helps,

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