What Sean said. You should also definitely turn on Kryo serialization. The default Java serialization is really really slow if you're gonna move around lots of data. Also make sure you use a cluster with high network bandwidth on.
On Thu, Sep 18, 2014 at 3:06 AM, Sean Owen <so...@cloudera.com> wrote: > Base 64 is an inefficient encoding for binary data by about 2.6x. You could > use byte[] directly. > > But you would still be storing and potentially shuffling lots of data in > your RDDs. > > If the files exist separately on HDFS perhaps you can just send around the > file location and load it directly using HDFS APIs in the function that > needs it. > > On Sep 18, 2014 9:51 AM, "Jaonary Rabarisoa" <jaon...@gmail.com> wrote: >> >> Hi all, >> >> I'm trying to process a large image data set and need some way to optimize >> my implementation since it's very slow from now. In my current >> implementation I store my images in an object file with the following fields >> >> case class Image(groupId: String, imageId: String, buffer: String) >> >> Images belong to groups and have an id, the buffer is the image file (jpg, >> png) encode in base 64 string. >> >> Before running an image processing algorithm on the image buffer, I have a >> lot of jobs that filter, group, join images in my data set based on groupId >> or imageId and theses steps are relatively slow. I suspect that spark moves >> around my image buffer even if it's not necessary for these specific jobs >> and then there's a lot of communication times waste. >> >> Is there a better way to optimize my implementation ? >> >> Regards, >> >> Jaonary --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
