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.dropDuplicates(['varId'])
# find variants in new datasets that aren't already in existing datasets
df = broadcast(df).join(existing_variants_df, dataset_df.varId == existing_variants.varId,
"leftanti")
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Just as a curious note: is there a limit to the size that can be broadcast and if so what happens if you exceed it? Also did you test without broadcast and what happened / what was the difference?

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I did a tests at 1gb, 50gb, and 80gb, and they all worked and fairly fast too. I don't think I tried without broadcast.

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I can do more structured testing or we can just "do it live" with the release.

.csv(outdir, sep='\t')
# if we find new variants, put them in the new_variants path for additional processing
# and append them to the existing deduped variants path
if not df.rdd.isEmpty():
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What would happen if this were removed? If an empty df is appended to something would it just have an extra empty file?

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Yeah, it will just add an empty file(s) to both places.

# just specify, potentially with a list only new datasets
dataset_srcdir = f'{S3DIR}/variants/ExSeq/*/*'
existing_variants = f'{S3DIR}/out/varianteffect/variants'
new_variants = f'{S3DIR}/out/new-variants'
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This should probably still be in out/varianteffect/new-variants not in the first level of out, I imagine this is just an error carried from when you were writing to the test bucket

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Sounds good. I mean to call out for your opinion on the directories.

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2 participants