Broadcasting huge array or persisting on HDFS to read on executors - both not working

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Broadcasting huge array or persisting on HDFS to read on executors - both not working

surender kumar
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.
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Re: Broadcasting huge array or persisting on HDFS to read on executors - both not working

matteuan
Why broadcasting this list then? You should use an RDD or DataFrame. For example, RDD has a method sample() that returns a random sample from it.

On 11 April 2018 at 22:34, surender kumar <[hidden email]> wrote:
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.

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Re: Broadcasting huge array or persisting on HDFS to read on executors - both not working

surender kumar
right, this is what I did when I said I tried to persist and create an RDD out of it to sample from. But how to do for each user?
You have one rdd of users on one hand and rdd of items on the other. How to go from here? Am I missing something trivial? 


On Thursday, 12 April, 2018, 2:10:51 AM IST, Matteo Cossu <[hidden email]> wrote:


Why broadcasting this list then? You should use an RDD or DataFrame. For example, RDD has a method sample() that returns a random sample from it.

On 11 April 2018 at 22:34, surender kumar <[hidden email]> wrote:
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.

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Re: Broadcasting huge array or persisting on HDFS to read on executors - both not working

matteuan
I don't think it's trivial. Anyway, the naive solution would be a cross join between user x items. But this can be very very expensive. I've encountered once a similar problem, here how I solved it:
  • create a new RDD with (itemID, index) where the index is a unique integer between 0 and the number of items
  • for every user sample n items by generating randomly n distinct integers between 0 and the number of items (e.g. with rand.randint()), so you have a new RDD (userID, [sample_items])
  • flatten all the list in the previously created RDD and join them back with the RDD with (itemID, index) using index as join attribute
You can do the same things with DataFrame using UDFs.

On 11 April 2018 at 23:01, surender kumar <[hidden email]> wrote:
right, this is what I did when I said I tried to persist and create an RDD out of it to sample from. But how to do for each user?
You have one rdd of users on one hand and rdd of items on the other. How to go from here? Am I missing something trivial? 


On Thursday, 12 April, 2018, 2:10:51 AM IST, Matteo Cossu <[hidden email]> wrote:


Why broadcasting this list then? You should use an RDD or DataFrame. For example, RDD has a method sample() that returns a random sample from it.

On 11 April 2018 at 22:34, surender kumar <[hidden email]> wrote:
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.


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Re: Broadcasting huge array or persisting on HDFS to read on executors - both not working

surender kumar
Thanks Matteo, this should work!

-Surender


On Thursday, 12 April, 2018, 1:13:38 PM IST, Matteo Cossu <[hidden email]> wrote:


I don't think it's trivial. Anyway, the naive solution would be a cross join between user x items. But this can be very very expensive. I've encountered once a similar problem, here how I solved it:
  • create a new RDD with (itemID, index) where the index is a unique integer between 0 and the number of items
  • for every user sample n items by generating randomly n distinct integers between 0 and the number of items (e.g. with rand.randint()), so you have a new RDD (userID, [sample_items])
  • flatten all the list in the previously created RDD and join them back with the RDD with (itemID, index) using index as join attribute
You can do the same things with DataFrame using UDFs.

On 11 April 2018 at 23:01, surender kumar <[hidden email]> wrote:
right, this is what I did when I said I tried to persist and create an RDD out of it to sample from. But how to do for each user?
You have one rdd of users on one hand and rdd of items on the other. How to go from here? Am I missing something trivial? 


On Thursday, 12 April, 2018, 2:10:51 AM IST, Matteo Cossu <[hidden email]> wrote:


Why broadcasting this list then? You should use an RDD or DataFrame. For example, RDD has a method sample() that returns a random sample from it.

On 11 April 2018 at 22:34, surender kumar <[hidden email]> wrote:
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.


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Re: Broadcasting huge array or persisting on HDFS to read on executors - both not working

Gourav Sengupta
Hi,

There is an option for Stratified Sampling available in SPARK: https://spark.apache.org/docs/latest/mllib-statistics.html#stratified-sampling

Also there is a method called randomSplit which may be called on dataframes in case we want to split them into training and test data.

Please let me know whether using any of these built in functions helps or not.


Regards,
Gourav 

On Thu, Apr 12, 2018 at 3:25 AM, surender kumar <[hidden email]> wrote:
Thanks Matteo, this should work!

-Surender


On Thursday, 12 April, 2018, 1:13:38 PM IST, Matteo Cossu <[hidden email]> wrote:


I don't think it's trivial. Anyway, the naive solution would be a cross join between user x items. But this can be very very expensive. I've encountered once a similar problem, here how I solved it:
  • create a new RDD with (itemID, index) where the index is a unique integer between 0 and the number of items
  • for every user sample n items by generating randomly n distinct integers between 0 and the number of items (e.g. with rand.randint()), so you have a new RDD (userID, [sample_items])
  • flatten all the list in the previously created RDD and join them back with the RDD with (itemID, index) using index as join attribute
You can do the same things with DataFrame using UDFs.

On 11 April 2018 at 23:01, surender kumar <[hidden email]> wrote:
right, this is what I did when I said I tried to persist and create an RDD out of it to sample from. But how to do for each user?
You have one rdd of users on one hand and rdd of items on the other. How to go from here? Am I missing something trivial? 


On Thursday, 12 April, 2018, 2:10:51 AM IST, Matteo Cossu <[hidden email]> wrote:


Why broadcasting this list then? You should use an RDD or DataFrame. For example, RDD has a method sample() that returns a random sample from it.

On 11 April 2018 at 22:34, surender kumar <[hidden email]> wrote:
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.



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Re: Broadcasting huge array or persisting on HDFS to read on executors - both not working

surender kumar
Question was not what kind of sampling but random sampling per user. There's no value associated with items to create stratas. If you read Matteo's answer, that's the way to go about it.

-Surender


On Thursday, 12 April, 2018, 5:49:43 PM IST, Gourav Sengupta <[hidden email]> wrote:


Hi,

There is an option for Stratified Sampling available in SPARK: https://spark.apache.org/docs/latest/mllib-statistics.html#stratified-sampling

Also there is a method called randomSplit which may be called on dataframes in case we want to split them into training and test data.

Please let me know whether using any of these built in functions helps or not.


Regards,
Gourav 

On Thu, Apr 12, 2018 at 3:25 AM, surender kumar <[hidden email]> wrote:
Thanks Matteo, this should work!

-Surender


On Thursday, 12 April, 2018, 1:13:38 PM IST, Matteo Cossu <[hidden email]> wrote:


I don't think it's trivial. Anyway, the naive solution would be a cross join between user x items. But this can be very very expensive. I've encountered once a similar problem, here how I solved it:
  • create a new RDD with (itemID, index) where the index is a unique integer between 0 and the number of items
  • for every user sample n items by generating randomly n distinct integers between 0 and the number of items (e.g. with rand.randint()), so you have a new RDD (userID, [sample_items])
  • flatten all the list in the previously created RDD and join them back with the RDD with (itemID, index) using index as join attribute
You can do the same things with DataFrame using UDFs.

On 11 April 2018 at 23:01, surender kumar <[hidden email]> wrote:
right, this is what I did when I said I tried to persist and create an RDD out of it to sample from. But how to do for each user?
You have one rdd of users on one hand and rdd of items on the other. How to go from here? Am I missing something trivial? 


On Thursday, 12 April, 2018, 2:10:51 AM IST, Matteo Cossu <[hidden email]> wrote:


Why broadcasting this list then? You should use an RDD or DataFrame. For example, RDD has a method sample() that returns a random sample from it.

On 11 April 2018 at 22:34, surender kumar <[hidden email]> wrote:
I'm using pySpark.
I've list of 1 million items (all float values ) and 1 million users. for each user I want to sample randomly some items from the item list.
Broadcasting the item list results in Outofmemory error on the driver, tried setting driver memory till 10G.  I tried to persist this array on disk but I'm not able to figure out a way to read the same on the workers.

Any suggestion would be appreciated.