On Jun 4, 2013, at 11:41 AM, Ladislav Lenart <lenart...@volny.cz> wrote:
> Hello.
>
>> You will then get the wrong results. The docstring tries to explain this -
>> a joinedload uses a JOIN. For each "cls" instance, there are many rows, one
>> for each "bar". If you cut off the results in the middle of populating that
>> collection, the collection is incomplete, you'll see the wrong collection on
>> your cls.bars. On the next load, cls.bars will be wiped out and populated
>> with the remaining "bar" objects.
>
> Ok, I think I understand this too.
>
> I've tried WindowedRangeQuery. It looked promising at first but it is (much)
> slower than yield_per() with all its quirks, at least for my usecase.
OK, but with yield_per() you want to use eagerloading also, so yield_per() not
fast enough either, I guess....
> If I
> understand the WindowedRangeQuery recipe, it does a full scan of the target
> table first to read all the ids and calculate the bounds of all the windows. I
> don't want to it like this. I am working with relatively large datasets but it
> is still far less than all rows in the table. Something like 10-50000 rows
> from
> a table with 1-2 million rows. The windowed query iterates over many
> completely
> empty windows.
>
> Can I modify the recipe so it preserves the filtering and creates windows only
> for the interesting subset of the table?
Absolutely, you should do whatever you have to in order to get the range you
want, in fact the recipe even says this:
Enhance this yourself ! Add a "where" argument
so that windows of just a subset of rows can
be computed.
if your situation is even simpler than that, such as just querying from PKs
50-1000, you could just make up your own integer ranges within those two
endpoints.
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