Hello Tobias,

>From my understanding the introduction of chunk_size doest't help here.

The fundamental reason why iterator() cannot be used with 
prefetch_related() is that the latter requires a set of model instance to 
be materialized to work appropriately which chunk_size doesn't control at 

In other words chunk_size only controls how many rows should be fetched 
from the database cursor and kept into memory at a time. Even when this 
parameter is used, iterator() will only materialize a single model instance 
per yield.

Now, something you could do if you really want to be using prefetch_related 
with iterator() is to materialize chunks and use the now public 
prefetch_related_objects[0] helper to prefetch relationships.

A somewhat complete implementation would look like

def prefetch_related_iterator(queryset, *related_lookups, chunk_size=100):
    iterator = queryset.iterator(chunk_size=chunk_size)
    while True:
        chunk = list(itertools.islice(iterator, chunk_size))
        if not chunk:
        prefetch_related_objects(chunk, *related_lookups)
        yield from chunk

Given that iterator() always ignored prefetch related lookups instead of 
erroring out when they were specified make me feel like turning such a 
feature on by default could be problematic as it could balloon the memory 
usage which is the main reason why iterator is useful anyway.



Le jeudi 11 octobre 2018 15:44:06 UTC-4, tobias....@truffls.com a écrit :
> Hi everyone!
> The docs (
> https://docs.djangoproject.com/en/2.1/ref/models/querysets/#iterator) 
> state that the
>  use of iterator() causes previous prefetch_related() calls to be ignored 
>> since these two optimizations do not make sense together.
> I am wondering, if this is still true with the introduction of the 
> *chunk_size* parameter. Having only one additional query per chunk could 
> heavily reduce the amount of queries per batch.
> Best regards
> Tobi
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