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Diffstat (limited to 'docs/ref/models')
| -rw-r--r-- | docs/ref/models/querysets.txt | 15 |
1 files changed, 15 insertions, 0 deletions
diff --git a/docs/ref/models/querysets.txt b/docs/ref/models/querysets.txt index df9da9b1c7..f509e7d616 100644 --- a/docs/ref/models/querysets.txt +++ b/docs/ref/models/querysets.txt @@ -2510,6 +2510,21 @@ 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 |