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I've had pretty good results with SQLAlchemy on large datasets, that
might be a painless way to solve the problem..
Ben

Jarek Zgoda wrote:
> Jacob Kaplan-Moss napisaƂ(a):
> 
>>>> Can you share any hints on how to reduce the memory usage in such
>>>> situation? The underlying database structure is rather complicated and I
>>>> would like to not do all queries manually.
>>> At this level -- hundreds of thousands of objects per query -- I doubt
>>> that any ORM solution is going to deliver decent performance.
>> That may be true, but in this case it's actually a bug in Django:
>> under some (many?) circumstances QuerySets load the entire result set
>> into memory even when used with an iterator. The good news is that
>> this has been fixed; the bad news is that the fix is on the
>> queryset-refactor branch, which likely won't merge to trunk for at
>> least a few more weeks, if not longer. Also note that values() won't
>> really help you, either: the overhead for a model instance isn't all
>> that big; the problem you're running into is simply the shear number
>> of results.
>>
>> In your situation, I'd do one of two things: either switch to the qsrf
>> branch if you're a living-on-the-edge kind of guy, or else look into
>> using an ObjectPaginator to churn through results in chunks.
> 
> Ah, thanks. The application is in production state, so I cann't use any
> other Django version we have installed on the machines. I'll try with
> ObjectPaginator, leaving raw SQL as last resort. Thank you all for the
> hints.
> 
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