Github user tgravescs commented on the issue:
https://github.com/apache/spark/pull/15297
Ok so are you saying this skewed join implementation doesn't apply to other
dataframe operations, something like:
val df_pixels = sqlContext.read.parquet("somefile")
val df_pixels_renamed = df_pixels.withColumnRenamed("photo_id",
"pixels_photo_id")
val df_meta = sqlContext.read.parquet("somemeta")
val df = df_meta.as("meta").join(df_pixels_renamed, $"meta.photo_id"
=== $"pixels_photo_id", "inner").drop("pixels_photo_id")
df.write.parquet("someoutputfile")
Where normally spark.sql.shuffle.partitions=X would configure the number of
output files. So in my example if I set spark.sql.shuffle.partitions=200 but
skewed join use 210, what happens, how many output files would I get?
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