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Posted to dev@spark.apache.org by Sandeep Giri <sa...@knowbigdata.com> on 2015/08/05 11:34:14 UTC
Re: New Feature Request
Hi Jonathan,
Does that guarantee a result? I do not see that it is really optimized.
Hi Carsten,
How does the following code work:
data.filter(qualifying_function).take(n).count() >= n
Also, as per my understanding, in both the approaches you mentioned the
qualifying function will be executed on whole dataset even if the value was
already found in the first element of RDD:
- data.filter(qualifying_function).take(n).count() >= n
- val contains1MatchingElement = !(data.filter(qualifying_
function).isEmpty())
Isn't it? Am I missing something?
Regards,
Sandeep Giri,
+1 347 781 4573 (US)
+91-953-899-8962 (IN)
www.KnowBigData.com. <http://KnowBigData.com.>
Phone: +1-253-397-1945 (Office)
[image: linkedin icon] <https://linkedin.com/company/knowbigdata> [image:
other site icon] <http://knowbigdata.com> [image: facebook icon]
<https://facebook.com/knowbigdata> [image: twitter icon]
<https://twitter.com/IKnowBigData> <https://twitter.com/IKnowBigData>
On Fri, Jul 31, 2015 at 3:37 PM, Jonathan Winandy <
jonathan.winandy@gmail.com> wrote:
> Hello !
>
> You could try something like that :
>
> def exists[T](rdd:RDD[T])(f:T=>Boolean, n:Int):Boolean = {
> rdd.filter(f).countApprox(timeout = 10000).getFinalValue().low > n
> }
>
> If would work for large datasets and large value of n.
>
> Have a nice day,
>
> Jonathan
>
>
>
> On 31 July 2015 at 11:29, Carsten Schnober <
> schnober@ukp.informatik.tu-darmstadt.de> wrote:
>
>> Hi,
>> the RDD class does not have an exist()-method (in the Scala API), but
>> the functionality you need seems easy to resemble with the existing
>> methods:
>>
>> val containsNMatchingElements =
>> data.filter(qualifying_function).take(n).count() >= n
>>
>> Note: I am not sure whether the intermediate take(n) really increases
>> performance, but the idea is to arbitrarily reduce the number of
>> elements in the RDD before counting because we are not interested in the
>> full count.
>>
>> If you need to check specifically whether there is at least one matching
>> occurrence, it is probably preferable to use isEmpty() instead of
>> count() and check whether the result is false:
>>
>> val contains1MatchingElement =
>> !(data.filter(qualifying_function).isEmpty())
>>
>> Best,
>> Carsten
>>
>>
>>
>> Am 31.07.2015 um 11:11 schrieb Sandeep Giri:
>> > Dear Spark Dev Community,
>> >
>> > I am wondering if there is already a function to solve my problem. If
>> > not, then should I work on this?
>> >
>> > Say you just want to check if a word exists in a huge text file. I could
>> > not find better ways than those mentioned here
>> > <
>> http://www.knowbigdata.com/blog/interview-questions-apache-spark-part-2#q6
>> >.
>> >
>> > So, I was proposing if we have a function called /exists /in RDD with
>> > the following signature:
>> >
>> > #returns the true if n elements exist which qualify our criteria.
>> > #qualifying function would receive the element and its index and return
>> > true or false.
>> > def /exists/(qualifying_function, n):
>> > ....
>> >
>> >
>> > Regards,
>> > Sandeep Giri,
>> > +1 347 781 4573 (US)
>> > +91-953-899-8962 (IN)
>> >
>> > www.KnowBigData.com. <http://KnowBigData.com.>
>> > Phone: +1-253-397-1945 (Office)
>> >
>> > linkedin icon <https://linkedin.com/company/knowbigdata> other site
>> icon
>> > <http://knowbigdata.com> facebook icon
>> > <https://facebook.com/knowbigdata>twitter icon
>> > <https://twitter.com/IKnowBigData><https://twitter.com/IKnowBigData>
>> >
>>
>> --
>> Carsten Schnober
>> Doctoral Researcher
>> Ubiquitous Knowledge Processing (UKP) Lab
>> FB 20 / Computer Science Department
>> Technische Universität Darmstadt
>> Hochschulstr. 10, D-64289 Darmstadt, Germany
>> phone [+49] (0)6151 16-6227, fax -5455, room S2/02/B111
>> schnober@ukp.informatik.tu-darmstadt.de
>> www.ukp.tu-darmstadt.de
>>
>> Web Research at TU Darmstadt (WeRC): www.werc.tu-darmstadt.de
>> GRK 1994: Adaptive Preparation of Information from Heterogeneous Sources
>> (AIPHES): www.aiphes.tu-darmstadt.de
>> PhD program: Knowledge Discovery in Scientific Literature (KDSL)
>> www.kdsl.tu-darmstadt.de
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
>> For additional commands, e-mail: dev-help@spark.apache.org
>>
>>
>
Re: New Feature Request
Posted by Sean Owen <so...@cloudera.com>.
I don't think countApprox is appropriate here unless approximation is OK.
But more generally, counting everything matching a filter requires applying
the filter to the whole data set, which seems like the thing to be avoided
here.
The take approach is better since it would stop after finding n matching
elements (it might do a little extra work given partitioning and
buffering). It would not filter the whole data set.
The only downside there is that it would copy n elements to the driver.
On Wed, Aug 5, 2015 at 10:34 AM, Sandeep Giri <sa...@knowbigdata.com>
wrote:
> Hi Jonathan,
>
> Does that guarantee a result? I do not see that it is really optimized.
>
> Hi Carsten,
>
>
> How does the following code work:
>
> data.filter(qualifying_function).take(n).count() >= n
>
>
> Also, as per my understanding, in both the approaches you mentioned the
> qualifying function will be executed on whole dataset even if the value was
> already found in the first element of RDD:
>
>
> - data.filter(qualifying_function).take(n).count() >= n
> - val contains1MatchingElement = !(data.filter(qualifying_
> function).isEmpty())
>
> Isn't it? Am I missing something?
>
>
> Regards,
> Sandeep Giri,
> +1 347 781 4573 (US)
> +91-953-899-8962 (IN)
>
> www.KnowBigData.com. <http://KnowBigData.com.>
> Phone: +1-253-397-1945 (Office)
>
> [image: linkedin icon] <https://linkedin.com/company/knowbigdata> [image:
> other site icon] <http://knowbigdata.com> [image: facebook icon]
> <https://facebook.com/knowbigdata> [image: twitter icon]
> <https://twitter.com/IKnowBigData> <https://twitter.com/IKnowBigData>
>
>
> On Fri, Jul 31, 2015 at 3:37 PM, Jonathan Winandy <
> jonathan.winandy@gmail.com> wrote:
>
>> Hello !
>>
>> You could try something like that :
>>
>> def exists[T](rdd:RDD[T])(f:T=>Boolean, n:Int):Boolean = {
>> rdd.filter(f).countApprox(timeout = 10000).getFinalValue().low > n
>> }
>>
>> If would work for large datasets and large value of n.
>>
>> Have a nice day,
>>
>> Jonathan
>>
>>
>>
>> On 31 July 2015 at 11:29, Carsten Schnober <
>> schnober@ukp.informatik.tu-darmstadt.de> wrote:
>>
>>> Hi,
>>> the RDD class does not have an exist()-method (in the Scala API), but
>>> the functionality you need seems easy to resemble with the existing
>>> methods:
>>>
>>> val containsNMatchingElements =
>>> data.filter(qualifying_function).take(n).count() >= n
>>>
>>> Note: I am not sure whether the intermediate take(n) really increases
>>> performance, but the idea is to arbitrarily reduce the number of
>>> elements in the RDD before counting because we are not interested in the
>>> full count.
>>>
>>> If you need to check specifically whether there is at least one matching
>>> occurrence, it is probably preferable to use isEmpty() instead of
>>> count() and check whether the result is false:
>>>
>>> val contains1MatchingElement =
>>> !(data.filter(qualifying_function).isEmpty())
>>>
>>> Best,
>>> Carsten
>>>
>>>
>>>
>>> Am 31.07.2015 um 11:11 schrieb Sandeep Giri:
>>> > Dear Spark Dev Community,
>>> >
>>> > I am wondering if there is already a function to solve my problem. If
>>> > not, then should I work on this?
>>> >
>>> > Say you just want to check if a word exists in a huge text file. I
>>> could
>>> > not find better ways than those mentioned here
>>> > <
>>> http://www.knowbigdata.com/blog/interview-questions-apache-spark-part-2#q6
>>> >.
>>> >
>>> > So, I was proposing if we have a function called /exists /in RDD with
>>> > the following signature:
>>> >
>>> > #returns the true if n elements exist which qualify our criteria.
>>> > #qualifying function would receive the element and its index and return
>>> > true or false.
>>> > def /exists/(qualifying_function, n):
>>> > ....
>>> >
>>> >
>>> > Regards,
>>> > Sandeep Giri,
>>> > +1 347 781 4573 (US)
>>> > +91-953-899-8962 (IN)
>>> >
>>> > www.KnowBigData.com. <http://KnowBigData.com.>
>>> > Phone: +1-253-397-1945 (Office)
>>> >
>>> > linkedin icon <https://linkedin.com/company/knowbigdata> other site
>>> icon
>>> > <http://knowbigdata.com> facebook icon
>>> > <https://facebook.com/knowbigdata>twitter icon
>>> > <https://twitter.com/IKnowBigData><https://twitter.com/IKnowBigData>
>>> >
>>>
>>> --
>>> Carsten Schnober
>>> Doctoral Researcher
>>> Ubiquitous Knowledge Processing (UKP) Lab
>>> FB 20 / Computer Science Department
>>> Technische Universität Darmstadt
>>> Hochschulstr. 10, D-64289 Darmstadt, Germany
>>> phone [+49] (0)6151 16-6227, fax -5455, room S2/02/B111
>>> schnober@ukp.informatik.tu-darmstadt.de
>>> www.ukp.tu-darmstadt.de
>>>
>>> Web Research at TU Darmstadt (WeRC): www.werc.tu-darmstadt.de
>>> GRK 1994: Adaptive Preparation of Information from Heterogeneous Sources
>>> (AIPHES): www.aiphes.tu-darmstadt.de
>>> PhD program: Knowledge Discovery in Scientific Literature (KDSL)
>>> www.kdsl.tu-darmstadt.de
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
>>> For additional commands, e-mail: dev-help@spark.apache.org
>>>
>>>
>>
>