Hi Imk,
I think iterator and for-comprehension may help here. I wrote a snippet
that implements your first 2 requirements:
def distance(a: (Double, Double), b: (Double, Double)): Double = ???
// Defines some total ordering among locations.
def lessThan(a: (Double, Double), b: (Double, Double)): Boolean = ???
sc.textFile("input")
.map { line =>
val Array(_, latitude, longitude, ip, _, _) = line.split(",")
ip -> (latitude.toDouble, longitude.toDouble)
}
.groupByKey()
.mapValues { positions =>
for {
a <- positions.iterator
b <- positions.iterator
if lessThan(a, b) && distance(a, b) < 100
} yield {
(a, b)
}
}
The key point is that iterators are lazy evaluated, so that you don’t need
to store the whole cartesian product.
I didn’t quite get your 3rd requirement, but I think you can implement that
following similar approach.
Cheng
On Thu, Jun 5, 2014 at 1:11 PM, lmk <lakshmi.muralikrish...@gmail.com>
wrote:
> Hi Oleg/Andrew,
> Thanks much for the prompt response.
>
> We expect thousands of lat/lon pairs for each IP address. And that is my
> concern with the Cartesian product approach.
> Currently for a small sample of this data (5000 rows) I am grouping by IP
> address and then computing the distance between lat/lon coordinates using
> array manipulation techniques.
> But I understand this approach is not right when the data volume goes up.
> My code is as follows:
>
> val dataset:RDD[String] = sc.textFile("x.csv")
> val data = dataset.map(l=>l.split(","))
> val grpData = data.map(r =>
> (r(3),((r(1).toDouble),r(2).toDouble))).groupByKey()
>
> Now, I have the data grouped by ipaddress as Array[(String,
> Iterable[(Double, Double)])]
> ex..
> Array((ip1,ArrayBuffer((lat1,lon1), (lat2,lon2), (lat3,lon3)))
>
> Now I have to find the distance between (lat1,lon1) and (lat2,lon2) and
> then
> between (lat1,lon1) and (lat3,lon3) and so on for all combinations.
>
> This is where I get stuck. Please guide me on this.
>
> Thanks Again.
>
>
>
>
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