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From Justin Uang <justin.u...@gmail.com>
Subject DataFrame#rdd doesn't respect DataFrame#cache, slowing down CrossValidator
Date Tue, 28 Jul 2015 09:36:35 GMT
Hey guys,

I'm running into some pretty bad performance issues when it comes to using
a CrossValidator, because of caching behavior of DataFrames.

The root of the problem is that while I have cached my DataFrame
representing the features and labels, it is caching at the DataFrame level,
while CrossValidator/LogisticRegression both drop down to the dataset.rdd
level, which ignores the caching that I have previously done. This is
worsened by the fact that for each combination of a fold and a param set
from the grid, it recomputes my entire input dataset because the caching
was lost.

My current solution is to force the input DataFrame to be based off of a
cached RDD, which I did with this horrible hack (had to drop down to java
from the pyspark because of something to do with vectors not be inferred
correctly):

def checkpoint_dataframe_caching(df):
    return
DataFrame(sqlContext._ssql_ctx.createDataFrame(df._jdf.rdd().cache(),
train_data._jdf.schema()), sqlContext)

before I pass it into the CrossValidator.fit(). If I do this, I still have
to cache the underlying rdd once more than necessary (in addition to
DataFrame#cache()), but at least in cross validation, it doesn't recompute
the RDD graph anymore.

Note, that input_df.rdd.cache() doesn't work because the python
CrossValidator implementation applies some more dataframe transformations
like filter, which then causes filtered_df.rdd to return a completely
different rdd that recomputes the entire graph.

Is it the intention of Spark SQL that calling DataFrame#rdd removes any
caching that was done for the query? Is the fix as simple as getting the
DataFrame#rdd to reference the cached query, or is there something more
subtle going on.

Best,

Justin

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