Hi all, I have to following use case: I have around 10k of jsons that I want to use for learning. The jsons are all stored in one file.
For learning a ML model, however, I only need around 30% of the jsons (the rest is not needed at all). So, my idea was to load all data into a RDD and then use the rdd.sample method to get my fraction of the data. I implemented this, and in the end it took as long as loading the whole data set. So I was wondering if Spark is still loading the whole dataset from disk and does the filtering afterwards? If this is the case, why does Spark not push down the filtering and load only a fraction of data from the disk? Cheers, Patrick -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Behaviour-of-RDD-sampling-tp27051.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
