Adding an example (very raw), to see if my understanding is correct:
val repartitioned = bidRdd.partitionBy(new Partitioner {
def numPartitions: Int = 100
def getPartition(clientId: Any): Int = hash(clientId) % 100
}
val cachedRdd = repartitioned.cache()
val client1Rdd = cachedRdd.filter({case (clientId:String,v:String) =>
clientId.equals(“Client 1")})
val client2Rdd = cachedRdd.filter({case (clientId:String,v:String) =>
clientId.equals(“Client 2")})
MyModel.train(client1Rdd)
MyModel.train(client2Rdd)
When the first MyModel.train() runs it will trigger an action and will cause
the repartition of the original bigRdd and its caching.
Can someone confirm if the following statements are true?
1) When I execute an action on client2Rdd, it will only read from the partition
that corresponds to Client 2 (it won’t iterate over ALL items originally in
bigRdd)
2) The caching happens in a way that preserves the partitioning by client Id
(and the locality)
Thanks,
Maximo
PD: I am aware that this might cause imbalance of data, but I can probably
mitigate that with a smarter partitioner.
On Sep 9, 2015, at 9:30 AM, Maximo Gurmendez
<[email protected]<mailto:[email protected]>> wrote:
Thanks Ben for your answer. I’ll explore what happens under the hoods in a
data frame.
Regarding the ability to split a large RDD into n RDDs without requiring n
passes to the large RDD. Can partitionBy() help? If I partition by a key that
corresponds to the the split criteria (i..e client id) and then cache each of
those RDDs. Will that lessen the effect of repeated large traversals (since
Spark will figure out that for each smaller RDD it just needs to traverse a
subset of the partitions)?
Thanks!
Máximo
On Sep 8, 2015, at 11:32 AM, Ben Tucker
<[email protected]<mailto:[email protected]>> wrote:
Hi Maximo —
This is a relatively naive answer, but I would consider structuring the RDD
into a DataFrame, then saving the 'splits' using something like
DataFrame.write.parquet(hdfs_path, byPartition=('client')). You could then read
a DataFrame from each resulting parquet directory and do your per-client work
from these. You mention re-using the splits, so this solution might be worth
the file-writing time.
Does anyone know of a method that gets a collection of DataFrames — one for
each partition, in the byPartition=('client') sense — from a 'big' DataFrame?
Basically, the equivalent of writing by partition and creating a DataFrame for
each result, but skipping the HDFS step.
On Tue, Sep 8, 2015 at 10:47 AM, Maximo Gurmendez
<[email protected]<mailto:[email protected]>> wrote:
Hi,
I have a RDD that needs to be split (say, by client) in order to train n
models (i.e. one for each client). Since most of the classifiers that come with
ml-lib only can accept an RDD as input (and cannot build multiple models in one
pass - as I understand it), the only way to train n separate models is to
create n RDDs (by filtering the original one).
Conceptually:
rdd1,rdd2,rdd3 = splitRdds(bigRdd)
the function splitRdd would use the standard filter mechanism . I would then
need to submit n training spark jobs. When I do this, will it mean that it will
traverse the bigRdd n times? Is there a better way to persist the splitted rdd
(i.e. save the split RDD in a cache)?
I could cache the bigRdd, but not sure that would be ver efficient either since
it will require the same number of passes anyway (I think - but I’m relatively
new to Spark). Also I’m planning on reusing the individual splits (rdd1, rdd2,
etc so would be convenient to have them individually cached).
Another problem is that the splits are could be very skewed (i.e. one split
could represent a large percentage of the original bigRdd ). So saving the
split RDDs to disk (at least, naively) could be a challenge.
Is there any better way of doing this?
Thanks!
Máximo
