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Cache intermediate dfs in memory #31

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daniel-thom
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This improves performance and prevents Spark memory errors from happening in certain situations. It fixes the issues seen by @mooneyme yesterday while converting distributedpv_sectoral.dsg.

@mooneyme The following command took ~2 minutes on my laptop. Run in local mode. There was nothing unusual about this dataset. I don't know why things were failing now when they didn't fail before. In any case, this solution is much better.

spark-submit --driver-memory 16G convert_dsg.py data/distributedpv_sectoral.dsg output -s sector -n 2

This improves performance and prevents Spark memory errors from
happening in certain situations.
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Looks good to me

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