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Re: [jira] [Commented] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

2013-07-23 Thread Peng Cheng 
That's exactly what I'm trying to do right now :) (I'm testing 
FastByIDArrayMap), but we probably have more problems than just HashMap, 
based on the heap dump analysis result, PreferenceArray probably will be 
our next target. This is awesome, as your FactorizablePreferences didn't 
use it in the first place.


Yours Peng

On 13-07-23 05:46 PM, Sebastian Schelter wrote:

IMHO you will always have memory issues if you try to provide constant time
random access. Thats why I proposed to created a special memory efficient
DataModel for sequential access.


2013/7/23 Peng Cheng (JIRA) 


 [
https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13717659#comment-13717659]

Peng Cheng commented on MAHOUT-1286:


Aye aye, I just did, turns out that instances of
PreferenceArray$PreferenceView has taken 1.7G. Quite unexpected right?
Thanks a lot for the advice.
My next experiment will just use GenericPreference [] directly, there will
be no more PreferenceArray.

Class Name
 |Objects |  Shallow Heap |Retained Heap

---
org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray$PreferenceView|
72,237,632 | 1,733,703,168 | >= 1,733,703,168
long[]
 |480,199 |   818,209,680 |   >= 818,209,680
float[]
  |480,190 |   410,563,592 |   >= 410,563,592
java.lang.Object[]
 | 18,230 |   361,525,488 | >= 2,443,647,088
org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray
 |480,189 |15,366,048 | >= 1,237,456,672
java.util.ArrayList
  | 17,811 |   427,464 | >= 2,092,416,104
char[]
 |  2,150 |   272,632 |   >= 272,632
byte[]
 |141 |54,048 |>= 54,048
java.lang.String
 |  2,119 |50,856 |   >= 271,920
java.util.concurrent.ConcurrentHashMap$HashEntry
 |673 |21,536 |>= 38,104
java.net.URL
 |229 |14,656 |>= 40,720
java.util.HashMap$Entry
  |344 |11,008 |>= 68,760

---



Memory-efficient DataModel, supporting fast online updates and

element-wise iteration
-

 Key: MAHOUT-1286
 URL: https://issues.apache.org/jira/browse/MAHOUT-1286
 Project: Mahout
  Issue Type: Improvement
  Components: Collaborative Filtering
Affects Versions: 0.9
Reporter: Peng Cheng
Assignee: Sean Owen
   Original Estimate: 336h
  Remaining Estimate: 336h

Most DataModel implementation in current CF component use hash map to

enable fast 2d indexing and update. This is not memory-efficient for big
data set. e.g. Netflix prize dataset takes 11G heap space as a
FileDataModel.

Improved implementation of DataModel should use more compact data

structure (like arrays), this can trade a little of time complexity in 2d
indexing for vast improvement in memory efficiency. In addition, any online
recommender or online-to-batch converted recommender will not be affected
by this in training process.

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Re: [jira] [Commented] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

2013-07-23 Thread Sebastian Schelter 
IMHO you will always have memory issues if you try to provide constant time
random access. Thats why I proposed to created a special memory efficient
DataModel for sequential access.


2013/7/23 Peng Cheng (JIRA) 

>
> [
> https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13717659#comment-13717659]
>
> Peng Cheng commented on MAHOUT-1286:
> 
>
> Aye aye, I just did, turns out that instances of
> PreferenceArray$PreferenceView has taken 1.7G. Quite unexpected right?
> Thanks a lot for the advice.
> My next experiment will just use GenericPreference [] directly, there will
> be no more PreferenceArray.
>
> Class Name
> |Objects |  Shallow Heap |Retained Heap
>
> ---
> org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray$PreferenceView|
> 72,237,632 | 1,733,703,168 | >= 1,733,703,168
> long[]
> |480,199 |   818,209,680 |   >= 818,209,680
> float[]
>  |480,190 |   410,563,592 |   >= 410,563,592
> java.lang.Object[]
> | 18,230 |   361,525,488 | >= 2,443,647,088
> org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray
> |480,189 |15,366,048 | >= 1,237,456,672
> java.util.ArrayList
>  | 17,811 |   427,464 | >= 2,092,416,104
> char[]
> |  2,150 |   272,632 |   >= 272,632
> byte[]
> |141 |54,048 |>= 54,048
> java.lang.String
> |  2,119 |50,856 |   >= 271,920
> java.util.concurrent.ConcurrentHashMap$HashEntry
> |673 |21,536 |>= 38,104
> java.net.URL
> |229 |14,656 |>= 40,720
> java.util.HashMap$Entry
>  |344 |11,008 |>= 68,760
>
> ---
>
>
> > Memory-efficient DataModel, supporting fast online updates and
> element-wise iteration
> >
> -
> >
> > Key: MAHOUT-1286
> > URL: https://issues.apache.org/jira/browse/MAHOUT-1286
> > Project: Mahout
> >  Issue Type: Improvement
> >  Components: Collaborative Filtering
> >Affects Versions: 0.9
> >Reporter: Peng Cheng
> >Assignee: Sean Owen
> >   Original Estimate: 336h
> >  Remaining Estimate: 336h
> >
> > Most DataModel implementation in current CF component use hash map to
> enable fast 2d indexing and update. This is not memory-efficient for big
> data set. e.g. Netflix prize dataset takes 11G heap space as a
> FileDataModel.
> > Improved implementation of DataModel should use more compact data
> structure (like arrays), this can trade a little of time complexity in 2d
> indexing for vast improvement in memory efficiency. In addition, any online
> recommender or online-to-batch converted recommender will not be affected
> by this in training process.
>
> --
> This message is automatically generated by JIRA.
> If you think it was sent incorrectly, please contact your JIRA
> administrators
> For more information on JIRA, see: http://www.atlassian.com/software/jira
>

[jira] [Commented] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

2013-07-23 Thread Peng Cheng (JIRA) 

[ 
https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13717659#comment-13717659
 ] 

Peng Cheng commented on MAHOUT-1286:


Aye aye, I just did, turns out that instances of PreferenceArray$PreferenceView 
has taken 1.7G. Quite unexpected right? Thanks a lot for the advice.
My next experiment will just use GenericPreference [] directly, there will be 
no more PreferenceArray.

Class Name 
|Objects |  Shallow Heap |Retained Heap
---
org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray$PreferenceView|
 72,237,632 | 1,733,703,168 | >= 1,733,703,168
long[] 
|480,199 |   818,209,680 |   >= 818,209,680
float[]
|480,190 |   410,563,592 |   >= 410,563,592
java.lang.Object[] 
| 18,230 |   361,525,488 | >= 2,443,647,088
org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray   
|480,189 |15,366,048 | >= 1,237,456,672
java.util.ArrayList
| 17,811 |   427,464 | >= 2,092,416,104
char[] 
|  2,150 |   272,632 |   >= 272,632
byte[] 
|141 |54,048 |>= 54,048
java.lang.String   
|  2,119 |50,856 |   >= 271,920
java.util.concurrent.ConcurrentHashMap$HashEntry   
|673 |21,536 |>= 38,104
java.net.URL   
|229 |14,656 |>= 40,720
java.util.HashMap$Entry
|344 |11,008 |>= 68,760
---


> Memory-efficient DataModel, supporting fast online updates and element-wise 
> iteration
> -
>
> Key: MAHOUT-1286
> URL: https://issues.apache.org/jira/browse/MAHOUT-1286
> Project: Mahout
>  Issue Type: Improvement
>  Components: Collaborative Filtering
>Affects Versions: 0.9
>Reporter: Peng Cheng
>Assignee: Sean Owen
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Most DataModel implementation in current CF component use hash map to enable 
> fast 2d indexing and update. This is not memory-efficient for big data set. 
> e.g. Netflix prize dataset takes 11G heap space as a FileDataModel.
> Improved implementation of DataModel should use more compact data structure 
> (like arrays), this can trade a little of time complexity in 2d indexing for 
> vast improvement in memory efficiency. In addition, any online recommender or 
> online-to-batch converted recommender will not be affected by this in 
> training process.

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Jenkins build is back to normal : Mahout-Examples-Cluster-Reuters-II #551

2013-07-23 Thread Apache Jenkins Server 
See

Re: [VOTE] Release Mahout 0.8

2013-07-23 Thread Grant Ingersoll 
Working on this today.  LDAP at ASF seems to be mucked up at the moment, so 
when it becomes available, I'll do the necessary copying, etc.

-Grant

On Jul 19, 2013, at 9:55 AM, Grant Ingersoll  wrote:

> This passes.  I will finish off the release either tonight or tomorrow AM.
> 
> On Jul 19, 2013, at 3:06 AM, Jake Mannix  wrote:
> 
>> +1 from me, I used the jars to run some LDA (on a couple hundred million
>> documents) on the work cluster (1.0.something small), and it worked fine.
>> Other clustering example (with reuters) also worked as expected.
>> 
>> 
>> 
>> On Thu, Jul 18, 2013 at 11:27 AM, Suneel Marthi 
>> wrote:
>> 
>>> +1 from me.
>>> 
>>> 
>>> 
>>> 
>>> 
>>> From: Sebastian Schelter 
>>> To: dev@mahout.apache.org
>>> Sent: Thursday, July 18, 2013 1:22 PM
>>> Subject: Re: [VOTE] Release Mahout 0.8
>>> 
>>> 
>>> +1 from me, recommender stuff worked fine in my tests
>>> 
>>> 
>>> 2013/7/18 Grant Ingersoll 
>>> 
 +1 from me.
 
 On Jul 16, 2013, at 4:52 PM, Grant Ingersoll 
>>> wrote:
 
> Applying a forcing function:
> 
> Please vote on releasing the 0.8 artifacts at
 
>>> https://repository.apache.org/content/repositories/orgapachemahout-113/org/apache/mahout/
 .
> 
> Release notes are at
 https://cwiki.apache.org/confluence/display/MAHOUT/Release+0.8
> 
> [] +1 Looks good
> [] 0 - No opinion
> [] -1 Don't release
> 
> Vote criteria from https://www.apache.org/dev/release.html
> 
> What are the ASF requirements on approving a release?
> Votes on whether a package is ready to be released use majority
>>> approval
 -- i.e., at least three PMC members must vote affirmatively for release,
 and there must be more positive than negative votes. Releases may not be
 vetoed. Before voting +1 PMC members are required to download the signed
 source code package, compile it as provided, and test the resulting
 executable on their own platform, along with also verifying that the
 package meets the requirements of the ASF policy on releases.
> 
> Thanks,
> Grant
 
 
 
>>> 
>> 
>> 
>> 
>> -- 
>> 
>>  -jake
> 
> 
> Grant Ingersoll | @gsingers
> http://www.lucidworks.com
> 
> 
> 
> 
> 


Grant Ingersoll | @gsingers
http://www.lucidworks.com