RDD is essentially a distributed *vector*, and is partitioned over multiple
worker nodes at runtime. So HashMap like O(1) key lookup over the whole
RDD is not applicable, but you can turn all the key/value pairs within a
single partition into a HashMap via RDD.mapPartitions:
someRdd.mapPartitions { iter =>
val hashMap = iter.toMap
...
}
On Sat, Jan 25, 2014 at 5:11 AM, Manoj Samel <manojsamelt...@gmail.com>wrote:
> Yes, that works.
>
> But then the hashmap functionality of the fast key lookup etc. is gone and
> the search will be linear using a iterator etc. Not sure if Spark
> internally creates additional optimizations for Seq but otherwise one has
> to assume this becomes a List/Array without a fast key lookup of a hashmap
> or b-tree
>
> Any thoughts ?
>
>
>
>
>
> On Fri, Jan 24, 2014 at 1:00 PM, Frank Austin Nothaft <
> fnoth...@berkeley.edu> wrote:
>
>> Manoj,
>>
>> I assume you’re trying to create an RDD[(String, Double)]? Couldn’t you
>> just do:
>>
>> val cr_rdd = sc.parallelize(cr.toSeq)
>>
>> The toSeq would convert the HashMap[String,Double] into a Seq[(String,
>> Double)] before calling the parallelize function.
>>
>> Regards,
>>
>> Frank Austin Nothaft
>> fnoth...@berkeley.edu
>> fnoth...@eecs.berkeley.edu
>> 202-340-0466
>>
>> On Jan 24, 2014, at 12:56 PM, Manoj Samel <manojsamelt...@gmail.com>
>> wrote:
>>
>> > Is there a way to create RDD over a hashmap ?
>> >
>> > If I have a hash map and try sc.parallelize, it gives
>> >
>> > <console>:17: error: type mismatch;
>> > found : scala.collection.mutable.HashMap[String,Double]
>> > required: Seq[?]
>> > Error occurred in an application involving default arguments.
>> > val cr_rdd = sc.parallelize(cr)
>> > ^
>>
>>
>
