Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/18902
+1 for using Dataframe-based version code.
@zhengruifeng One thing I want to confirm is that, I check your testing
code, both RDD-based version and Dataframe-based version code will both cost on
deserialization:
```
...
val df = spark.createDataFrame(rows, struct)
df.persist()
df.count()
...
// do `imputer.fit`
```
when running `imputer.fit`, it will extract the required columns from the
cached input dataframe, and then you compare the perf between `RDD.aggregate`
and `dataframe avg`, they both need to deserialize data from input and then do
computation, and `dataframe avg` will take advantage of codegen and should be
faster. But here the test show that RDD version is slower than Dataframe
version, it is not very reasonable, so I want to confirm:
in your RDD version testing, do you cache again when get `RDD` from the
input `Dataframe`?
If not, your testing has no problem, I will guess there exists other
performance issue in SQL layer and cc @cloud-fan to take a look.
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