If you don’t cache the RDD, the computation will happen over and over each time we scan through it. This is done to save memory in that case and because Spark can’t know at the beginning whether you plan to access a dataset multiple times. If you’d like to prevent this, use cache(), or maybe persist(StorageLevel.DISK_ONLY) if you don’t want to keep it in memory.
Matei On Jan 21, 2014, at 11:32 AM, DB Tsai <[email protected]> wrote: > Hi, > > When the data is read from HDFS using textFile, and then map function is > performed as the following code to make the format right in order to feed it > into mllib training algorithms. > > rddFile = sc.textFile("Some file on HDFS") > > rddData = rddFile.map(line => { > val temp = line.toString.split(",") > val y = temp(3) match { > case "1" => 0.0 > case "2" => 1.0 > case _ => 2.0 > } > val x = temp.slice(1, 3).map(_.toDouble) > LabeledPoint(y, x) > }) > > My question is that when the map function is performed? Is it lazy evaluated > when we use rddData first time and generate another new dataset called > rddData since RDD is immutable? Does it mean the second time we use rddData, > the transformation isn't computed? > > Or the transformation is computed in real time, so we don't need extra memory > for this? > > The motivation for asking this question is that I found in mllib library, > there are lots of extra transformation is done. For example, the intercept is > added by map( point -> new LabeledPoint(point.y, Array( 1, point.feature)) > > If the new dataset is generated every time when the map is performed, for a > really big dataset, it will waste lots of memory and IO. Also, it will be > less efficiency, when we chain several map function to RDD since all of them > can be done in one place. > > Thanks. > > Sincerely, > > DB Tsai > Machine Learning Engineer > Alpine Data Labs > -------------------------------------- > Web: http://alpinenow.com/
