Minor Description
Hello,
I'm newbie in spark, but I think that I found a small problem that can affect spark Kmeans performances.
Before starting to explain the problem, I want to explain the warning that I faced.
I tried to use Spark Kmeans with Dataframes to cluster my data
df_Part = assembler.transform(df_Part)
df_Part.cache()
while (k<=max_cluster) and (wssse > seuilStop):
kmeans = KMeans().setK(k)
model = kmeans.fit(df_Part)
wssse = model.computeCost(df_Part)
k=k+1
but when I run the code I receive the warning :
WARN KMeans: The input data is not directly cached, which may hurt performance if its parent RDDs are also uncached.
I searched in spark source code to find the source of this problem, then I realized there is two classes responsible for this warning:
(mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala )
(mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala )
When my dataframe is cached, the fit method transform my dataframe into an internally rdd which is not cached.
Dataframe -> rdd -> run Training Kmeans Algo(rdd)
-> The first class (ml package) responsible for converting the dataframe into rdd then call Kmeans Algorithm
->The second class (mllib package) implements Kmeans Algorithm, and here spark verify if the rdd is cached, if not a warning will be generated.
So, the solution of this problem is to cache the rdd before running Kmeans Algorithm.
https://github.com/ZakariaHili/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
All what we need is to add two lines:
Cache rdd just after dataframe transformation, then uncached it after training algorithm.
I hope that I was clear.
If you think that I was wrong, please let me know.
Sincerely,
Zakaria HILI
