MongoEngine / MongoEngine/mongoengine
update(add_to_set) on a ListField(CachedReferenceField) only includes the _id
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- Python
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Description
Modifying existing CachedReferenceField test code to test a ListField(CachedReferenceField()) using doc.update(add_to_set__animal=animal_instance) results in the _id being in the list. None of the cached fields made it.
from mongoengine import *
from mongoengine.connection import get_db
from mongoengine.base import (BaseDict, BaseField, EmbeddedDocumentList,
_document_registry)
class Owner(EmbeddedDocument):
name = StringField()
tags = ListField(StringField())
class Animal(Document):
name = StringField()
tag = StringField()
owner = EmbeddedDocumentField(Owner)
class Ocorrence(Document):
person = StringField()
animal = ListField(CachedReferenceField(
Animal, fields=['tag', 'owner.tags']))
Animal.drop_collection()
Ocorrence.drop_collection()
a = Animal(name="Leopard", tag="heavy",
owner=Owner(tags=['cool', 'funny'],
name="Wilson Júnior")
)
a2 = Animal(name="Tiger", tag="too heavy",
owner=Owner(tags=['cool2', 'funny2'],
name="Wilson")
)
a.save()
a2.save()
o = Ocorrence(person="teste 2")
o.save()
o.update(add_to_set__animal=a)
o.update(add_to_set__animal=a2)
# doing it this way works as documented
#o = Ocorrence(person="teste 2", animal=[a,a2])
#o.save()
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by running the reproduction using the Owner, Animal, and Ocorrence definitions shown in the issue, comparing update(add_to_set__animal=animal_instance) with saving a populated list. Trace how the CachedReferenceField values are serialized during the add_to_set update. Done means both cached fields, including nested owner.tags, remain available in the stored list and the regression is covered by a test.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- mongodb, python
- Domain
- databases
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100