Please try
val parsedData3 =
data3.repartition(12).map(_.split("\t")).map(_.toDouble).cache()
and check the storage and driver/executor memory in the WebUI. Make
sure the data is fully cached.
-Xiangrui
On Thu, Jul 17, 2014 at 5:09 AM, Ravishankar Rajagopalan
<viora...@gmail.com> wrote:
> Hi Xiangrui,
>
> Yes I am using Spark v0.9 and am not running it in local mode.
>
> I did the memory setting using "export SPARK_MEM=4G" before starting the
> Spark instance.
>
> Also previously, I was starting it with -c 1 but changed it to -c 12 since
> it is a 12 core machine. It did bring down the time taken to less than 200
> seconds from over 700 seconds.
>
> I am not sure how to repartition the data to match the CPU cores. How do I
> do it?
>
> Thank you.
>
> Ravi
>
>
> On Thu, Jul 17, 2014 at 3:17 PM, Xiangrui Meng <men...@gmail.com> wrote:
>>
>> Is it v0.9? Did you run in local mode? Try to set --driver-memory 4g
>> and repartition your data to match number of CPU cores such that the
>> data is evenly distributed. You need 1m * 50 * 8 ~ 400MB to storage
>> the data. Make sure there are enough memory for caching. -Xiangrui
>>
>> On Thu, Jul 17, 2014 at 1:48 AM, Ravishankar Rajagopalan
>> <viora...@gmail.com> wrote:
>> > I am trying to use MLlib for K-Means clustering on a data set with 1
>> > million
>> > rows and 50 columns (all columns have double values) which is on HDFS
>> > (raw
>> > txt file is 28 MB)
>> >
>> > I initially tried the following:
>> >
>> > val data3 = sc.textFile("hdfs://...inputData.txt")
>> > val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
>> > val numIterations = 10
>> > val numClusters = 200
>> > val clusters = KMeans.train(parsedData3, numClusters, numIterations)
>> >
>> > This took me nearly 850 seconds.
>> >
>> > I tried using persist with MEMORY_ONLY option hoping that this would
>> > significantly speed up the algorithm:
>> >
>> > val data3 = sc.textFile("hdfs://...inputData.txt")
>> > val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
>> > parsedData3.persist(MEMORY_ONLY)
>> > val numIterations = 10
>> > val numClusters = 200
>> > val clusters = KMeans.train(parsedData3, numClusters, numIterations)
>> >
>> > This resulted in only a marginal improvement and took around 720
>> > seconds.
>> >
>> > Is there any other way to speed up the algorithm further?
>> >
>> > Thank you.
>> >
>> > Regards,
>> > Ravi
>
>
