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Code Issues 32 Releases Wiki Activity

Fixed #36526 -- Doc'd QuerySet.bulk_update() memory usage when batching.

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Thanks Simon Charette for the review.
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Natalia 2025-08-28 17:19:20 -03:00 committed by nessita
parent 96a7a65216
commit 608d3ebc88
1 changed files with 15 additions and 0 deletions
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15
docs/ref/models/querysets.txt
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@ -2506,6 +2506,21 @@ them, but it has a few caveats:
* If updating a large number of columns in a large number of rows, the SQL
generated can be very large. Avoid this by specifying a suitable
``batch_size``.
* When updating a large number of objects, be aware that ``bulk_update()``
prepares all of the ``WHEN`` clauses for every object across all batches
before executing any queries. This can require more memory than expected. To
reduce memory usage, you can use an approach like this::
from itertools import islice
batch_size = 100
ids_iter = range(1000)
while ids := list(islice(ids_iter, batch_size)):
batch = Entry.objects.filter(ids__in=ids)
for entry in batch:
entry.headline = f"Updated headline {entry.pk}"
Entry.objects.bulk_update(batch, ["headline"], batch_size=batch_size)
* Updating fields defined on multi-table inheritance ancestors will incur an
extra query per ancestor.
* When an individual batch contains duplicates, only the first instance in that