Re: Strange behavior of RDD.cartesian
You can find here a gist that illustrates this issue
https://gist.github.com/jrabary/9953562
I got this with spark from master branch.
On Sat, Mar 29, 2014 at 7:12 PM, Andrew Ash wrote:
> Is this spark 0.9.0? Try setting spark.shuffle.spill=false There was a
> hash collision bug that's fixed in 0.9.1 that might cause you to have too
> few results in that join.
>
> Sent from my mobile phone
> On Mar 28, 2014 8:04 PM, "Matei Zaharia" wrote:
>
>> Weird, how exactly are you pulling out the sample? Do you have a small
>> program that reproduces this?
>>
>> Matei
>>
>> On Mar 28, 2014, at 3:09 AM, Jaonary Rabarisoa wrote:
>>
>> I forgot to mention that I don't really use all of my data. Instead I use
>> a sample extracted with randomSample.
>>
>>
>> On Fri, Mar 28, 2014 at 10:58 AM, Jaonary Rabarisoa wrote:
>>
>>> Hi all,
>>>
>>> I notice that RDD.cartesian has a strange behavior with cached and
>>> uncached data. More precisely, I have a set of data that I load with
>>> objectFile
>>>
>>> *val data: RDD[(Int,String,Array[Double])] = sc.objectFile("data")*
>>>
>>> Then I split it in two set depending on some criteria
>>>
>>>
>>> *val part1 = data.filter(_._2 matches "view1")*
>>> *val part2 = data.filter(_._2 matches "view2")*
>>>
>>>
>>> Finally, I compute the cartesian product of part1 and part2
>>>
>>> *val pair = part1.cartesian(part2)*
>>>
>>>
>>> If every thing goes well I should have
>>>
>>> *pair.count == part1.count * part2.count*
>>>
>>> But this is not the case if I don't cache part1 and part2.
>>>
>>> What I was missing ? Does caching data mandatory in Spark ?
>>>
>>> Cheers,
>>>
>>> Jaonary
>>>
>>>
>>>
>>>
>>
>>
Re: Strange behavior of RDD.cartesian
Is this spark 0.9.0? Try setting spark.shuffle.spill=false There was a hash
collision bug that's fixed in 0.9.1 that might cause you to have too few
results in that join.
Sent from my mobile phone
On Mar 28, 2014 8:04 PM, "Matei Zaharia" wrote:
> Weird, how exactly are you pulling out the sample? Do you have a small
> program that reproduces this?
>
> Matei
>
> On Mar 28, 2014, at 3:09 AM, Jaonary Rabarisoa wrote:
>
> I forgot to mention that I don't really use all of my data. Instead I use
> a sample extracted with randomSample.
>
>
> On Fri, Mar 28, 2014 at 10:58 AM, Jaonary Rabarisoa wrote:
>
>> Hi all,
>>
>> I notice that RDD.cartesian has a strange behavior with cached and
>> uncached data. More precisely, I have a set of data that I load with
>> objectFile
>>
>> *val data: RDD[(Int,String,Array[Double])] = sc.objectFile("data")*
>>
>> Then I split it in two set depending on some criteria
>>
>>
>> *val part1 = data.filter(_._2 matches "view1")*
>> *val part2 = data.filter(_._2 matches "view2")*
>>
>>
>> Finally, I compute the cartesian product of part1 and part2
>>
>> *val pair = part1.cartesian(part2)*
>>
>>
>> If every thing goes well I should have
>>
>> *pair.count == part1.count * part2.count*
>>
>> But this is not the case if I don't cache part1 and part2.
>>
>> What I was missing ? Does caching data mandatory in Spark ?
>>
>> Cheers,
>>
>> Jaonary
>>
>>
>>
>>
>
>
Re: Strange behavior of RDD.cartesian
Weird, how exactly are you pulling out the sample? Do you have a small program
that reproduces this?
Matei
On Mar 28, 2014, at 3:09 AM, Jaonary Rabarisoa wrote:
> I forgot to mention that I don't really use all of my data. Instead I use a
> sample extracted with randomSample.
>
>
> On Fri, Mar 28, 2014 at 10:58 AM, Jaonary Rabarisoa wrote:
> Hi all,
>
> I notice that RDD.cartesian has a strange behavior with cached and uncached
> data. More precisely, I have a set of data that I load with objectFile
>
> val data: RDD[(Int,String,Array[Double])] = sc.objectFile("data")
>
> Then I split it in two set depending on some criteria
>
>
> val part1 = data.filter(_._2 matches "view1")
> val part2 = data.filter(_._2 matches "view2")
>
>
> Finally, I compute the cartesian product of part1 and part2
>
> val pair = part1.cartesian(part2)
>
>
> If every thing goes well I should have
>
> pair.count == part1.count * part2.count
>
> But this is not the case if I don't cache part1 and part2.
>
> What I was missing ? Does caching data mandatory in Spark ?
>
> Cheers,
>
> Jaonary
>
>
>
>
Re: Strange behavior of RDD.cartesian
I forgot to mention that I don't really use all of my data. Instead I use a
sample extracted with randomSample.
On Fri, Mar 28, 2014 at 10:58 AM, Jaonary Rabarisoa wrote:
> Hi all,
>
> I notice that RDD.cartesian has a strange behavior with cached and
> uncached data. More precisely, I have a set of data that I load with
> objectFile
>
> *val data: RDD[(Int,String,Array[Double])] = sc.objectFile("data")*
>
> Then I split it in two set depending on some criteria
>
>
> *val part1 = data.filter(_._2 matches "view1")*
> *val part2 = data.filter(_._2 matches "view2")*
>
>
> Finally, I compute the cartesian product of part1 and part2
>
> *val pair = part1.cartesian(part2)*
>
>
> If every thing goes well I should have
>
> *pair.count == part1.count * part2.count*
>
> But this is not the case if I don't cache part1 and part2.
>
> What I was missing ? Does caching data mandatory in Spark ?
>
> Cheers,
>
> Jaonary
>
>
>
>
Strange behavior of RDD.cartesian
Hi all,
I notice that RDD.cartesian has a strange behavior with cached and uncached
data. More precisely, I have a set of data that I load with objectFile
*val data: RDD[(Int,String,Array[Double])] = sc.objectFile("data")*
Then I split it in two set depending on some criteria
*val part1 = data.filter(_._2 matches "view1")*
*val part2 = data.filter(_._2 matches "view2")*
Finally, I compute the cartesian product of part1 and part2
*val pair = part1.cartesian(part2)*
If every thing goes well I should have
*pair.count == part1.count * part2.count*
But this is not the case if I don't cache part1 and part2.
What I was missing ? Does caching data mandatory in Spark ?
Cheers,
Jaonary
