Can you add your description of the problem as a comment to that ticket and we'll make sure to test both cases and break it out if the root cause ends up being different.
On Tue, Jul 28, 2015 at 2:48 PM, Justin Uang <justin.u...@gmail.com> wrote: > Sweet! Does this cover DataFrame#rdd also using the cached query from > DataFrame#cache? I think the ticket 9141 is mainly concerned with whether a > derived DataFrame (B) of a cached DataFrame (A) uses the cached query of A, > not whether the rdd from A.rdd or B.rdd uses the cached query of A. > > On Tue, Jul 28, 2015 at 11:33 PM Joseph Bradley <jos...@databricks.com> > wrote: > >> Thanks for bringing this up! I talked with Michael Armbrust, and it >> sounds like this is a from a bug in DataFrame caching: >> https://issues.apache.org/jira/browse/SPARK-9141 >> It's marked as a blocker for 1.5. >> Joseph >> >> On Tue, Jul 28, 2015 at 2:36 AM, Justin Uang <justin.u...@gmail.com> >> wrote: >> >>> 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 >>> >> >>
