Sorry but I wasn't able to code my stuff using accumulators as you
suggested :(
In my use case I have to to add elements to an array/list and then, every
100 element commit the batch to a solr index and then clear it.
In the cleanup code I have to commit the uncommited (remainder) elements.
In the example you showed me I can't see how to append element to a list
and commit the remainder elements of my RDD.
Following the advice of Sean this is more o less what I have now, but still
I don't know how to return empty iterators (and if I really have to) and if
I still need to use accumulators to add element to the list (in the code
below I haven't specified what to use..):
.mapPartitions { partition =>
if (!partition.isEmpty) {
// Some setup code here
println("Initialize batch");
partition.map(x => {
var batch = ???
batch.add(someClassFactory.fromString(x._2, x._3))
if (batch.size % 100 == 0) {
println("Flush legacy entities");
batch.clear
}
if (!partition.hasNext) {
// Some cleanup code here
println("Flush legacy entities");
batch.clear
}
Iterator.empty
})
} else {
// return an empty Iterator of your return type
Iterator.empty
}
Best,
Flavio
On Tue, Oct 28, 2014 at 1:26 PM, Kamal Banga <ka...@sigmoidanalytics.com>
wrote:
> Hi Flavio,
>
> Doing batch += ... shouldn't work. It will create new batch for each
> element in the myRDD (also val initializes an immutable variable, var is
> for mutable variables). You can use something like accumulators
> <http://spark.apache.org/docs/latest/programming-guide.html#accumulators>
> .
>
> val accum = sc.accumulator(0, "Some accumulator")
> val NUM = 100
> val SIZE = myRDD.count()
> val LAST = SIZE % NUM
> val MULTIPLE = (SIZE / NUM) * NUM // should return 300 when SIZE is 350
> myRDD.map(x => {
> accum++
> if (accum++ < MULTIPLE)
> null
> else x
> }).
>
> So if there are 350 elements, it should return null for first 300 elements
> and actual values for last 50 elements. Then we can apply a filter for null
> and get remainder elements (and then finally flush the new RDD containing
> the remainder elements).
>
> On map vs mapPartitions, mapPartitions is used mainly for efficiency (as
> you can see here
> <http://spark-summit.org/wp-content/uploads/2013/10/Wendell-Spark-Performance.pdf>,
> here
> <http://stackoverflow.com/questions/21185092/apache-spark-map-vs-mappartitions>
> and here
> <http://bzhangusc.wordpress.com/2014/06/19/optimize-map-performamce-with-mappartitions/>).
> So for simpler code, you can go with map, and for efficiency, you can go
> with mapPartitions.
>
> Regards,
> Kamal
>
--
Flavio Pompermaier
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