I have a lot of model classes with ralations between them with a CRUD interface to edit. The problem is that some objects can't be deleted since there are other objects refering to them. Sometimes I can setup ON DELETE rule to handle this case, but in most cases I don't want automatic deletion of related objects till they are unbound manually. Anyway, I'd like to present editor a list of objects refering to currently viewed one and highlight those that prevent its deletion due to FOREIGN KEY constraint. Is there a ready solution to automatically discover referers?
Update
The task seems to be quite common (e.g. django ORM shows all dependencies), so I wonder that there is no solution to it yet.
There are two directions suggested:
Enumerate all relations of current object and go through their backref. But there is no guarantee that all relations have backref defined. Moreover, there are some cases when backref is meaningless. Although I can define it everywhere I don't like doing this way and it's not reliable.
(Suggested by van and stephan) Check all tables of MetaData object and collect dependencies from their foreign_keys property (the code of sqlalchemy_schemadisplay can be used as example, thanks to stephan's comments). This will allow to catch all dependencies between tables, but what I need is dependencies between model classes. Some foreign keys are defined in intermediate tables and have no models corresponding to them (used as secondary in relations). Sure, I can go farther and find related model (have to find a way to do it yet), but it looks too complicated.
Solution
Below is a method of base model class (designed for declarative extention) that I use as solution. It is not perfect and doesn't meet all my requirements, but it works for current state of my project. The result is collected as dictionary of dictionaries, so I can show them groupped by objects and their properties. I havn't decided yet whether it's good idea, since the list of referers sometimes is huge and I'm forced to limit it to some reasonable number.
def _get_referers(self):
db = object_session(self)
cls, ident = identity_key(instance=self)
medatada = cls.__table__.metadata
result = {}
# _mapped_models is my extension. It is collected by metaclass, so I didn't
# look for other ways to find all model classes.
for other_class in medatada._mapped_models:
queries = {}
for prop in class_mapper(other_class).iterate_properties:
if not (isinstance(prop, PropertyLoader) and \
issubclass(cls, prop.mapper.class_)):
continue
query = db.query(prop.parent)
comp = prop.comparator
if prop.uselist:
query = query.filter(comp.contains(self))
else:
query = query.filter(comp==self)
count = query.count()
if count:
queries[prop] = (count, query)
if queries:
result[other_class] = queries
return result
Thanks to all who helped me, especially stephan and van.
SQL: I have to absolutely disagree with S.Lott' answer.
I am not aware of out-of-the-box solution, but it is definitely possible to discover all the tables that have ForeignKey constraints to a given table. One needs to use properly the INFORMATION_SCHEMA views such as REFERENTIAL_CONSTRAINTS, KEY_COLUMN_USAGE, TABLE_CONSTRAINTS, etc. See SQL Server example. With some limitations and extensions, most versions of new relational databases support INFORMATION_SCHEMA standard. When you have all the FK information and the object (row) in the table, it is a matter of running few SELECT statements to get all other rows in other tables that refer to given row and prevent it from being deleted.
SqlAlchemy: As noted by stephan in his comment, if you use orm with backref for relations, then it should be quite easy for you to get the list of parent objects that keep reference to the object you are trying to delete, because those objects are basically mapped properties of your object (child1.Parent).
If you work with Table objects of sql alchemy (or not always use backref for relations), then you would have to get values of foreign_keys for all the tables, and then for all those ForeignKeys call references(...) method, providing your table as a parameter. In this way you will find all the FKs (and tables) that have reference to the table your object maps to. Then you can query all the objects that keep reference to your object by constructing the query for each of those FKs.
In general, there's no way to "discover" all of the references in a relational database.
In some databases, they may use declarative referential integrity in the form of explicit Foreign Key or Check constraints.
But there's no requirement to do this. It can be incomplete or inconsistent.
Any query can include a FK relationship that is not declared. Without the universe of all queries, you can't know the relationships which are used but not declared.
To find "referers" in general, you must actually know the database design and have all queries.
For each model class, you can easily see if all its one-to-many relations are empty simply by asking for the list in each case and seeing how many entries it contains. (There is probably a more efficient way implemented in terms of COUNT, too.) If there are any foreign keys relating to the object, and you have your object relations set up correctly, then at least one of these lists will be non-zero in length.
Related
It seems to me that MetaData.reflect() and sqlalchemy.ext.automap.prepare() tables should be able to be used interchangeably for many use cases, but they can't be.
The metadata.tables['mytable'] into conn.execute(select(...)) returns a sqlalchemy.engine.cursor.CursorResult and your iterator gets the columns directly (eg x.columnA).
But automap_base().classes.mytable into the same conn.execute(select(...)) returns a sqlalchemy.engine.result.ChunkedIteratorResult and you need x.mytable.columnA to get at the column.
The sqlalchemy.engine.Result() documention says as much:
New in version 1.4: The Result object provides a completely updated
usage model and calling facade for SQLAlchemy Core and SQLAlchemy ORM.
In Core, it forms the basis of the CursorResult object which replaces
the previous ResultProxy interface. When using the ORM, a higher level
object called ChunkedIteratorResult is normally used.
Can I generically convert one to the other? That is, some wrapper that works for every table without needing the table name?
What's the best futureproof way to do this? I want my code to be forward-looking to sqlalchemy 2.0. Does that mean I should move away from either automap or MetaData?
sqlalchemy 1.4.35
This is the difference between the Core and the ORM.
select() from a Table vs. ORM class
While the SQL generated in these examples looks the same whether we
invoke select(user_table) or select(User), in the more general case
they do not necessarily render the same thing, as an ORM-mapped class
may be mapped to other kinds of “selectables” besides tables. The
select() that’s against an ORM entity also indicates that ORM-mapped
instances should be returned in a result, which is not the case when
SELECTing from a Table object.
Don't hesitate to use the ORM. It's higher level, pythonic, cool, and automap is ORM.
I like how Django ORM lazy loads related objects in the queryset, but I guess it's quite unpredictable as it is.
The queryset API doesn't keep the related objects when they are used to make a queryset, thereby fetching them again when accessed later.
Suppose I have a ModelA instance (say instance_a) which is a foreign key (say for_a) of some N instances of ModelB. Now I want to perform query on ModelB which has the given ModelA instance as the foreign key.
Django ORM provides two ways:
Using .filter() on ModelB:
b_qs = ModelB.objects.filter(for_a=instance_a)
for instance_b in b_qs:
instance_b.for_a # <-- fetches the same row for ModelA again
Results in 1 + N queries here.
Using reverse relations on ModelA instance:
b_qs = instance_a.for_a_set.all()
for instance_b in b_qs:
instance_b.for_a # <-- this uses the instance_a from memory
Results in 1 query only here.
While the second way can be used to achieve the result, it's not part of the standard API and not useable for every scenario. For example, if I have instances of 2 foreign keys of ModelB (say, ModelA and ModelC) and I want to get related objects to both of them.
Something like the following works:
ModelB.objects.filter(for_a=instance_a, for_c=instance_c)
I guess it's possible to use .intersection() for this scenario, but I would like a way to achieve this via the standard API. After all, covering such cases would require more code with non-standard queryset functions which may not make sense to the next developer.
So, the first question, is it possible to optimize such scenarios with the the standard API itself?
The second question, if it's not possible right now, can it be added with some tweaks with the QuerySet?
PS: It's my first time asking a question here, so forgive me if I made any mistake.
You could improve the query by using select_related():
b_qs = ModelB.objects.select_related('for_a').filter(for_a=instance_a)
or
b_qs = instance_a.for_a_set.select_related('for_a')
Does that help?
You use .select_related(..) [Django-doc] for ForeignKeys, or .prefetch_related(..) [Django-doc] for something-to-many relations.
With .select_related(..) you will make a LEFT OUTER JOIN at the database side, and fetch records for the two objects, and thus do the deserialization to the proper objects.
ModelB.objects.select_related('for_a').filter(for_a=instance_a)
For relations that are one-to-many (so a reversed ForeignKey), or ManyToManyFields, this is not a good idea, since it could result in a large amount of duplicate objects that are retrieved. This would result in a large answer from the database, and a lot of work at the Python end to deserialize these objects. .prefetch_related will make individual queries, and then do the linking itself.
So I have two table in a one-to-many relationship. When I make a new row of Table1, I want to populate Table2 with the related rows. However, this population actually involves computing the Table2 rows, using data in other related tables.
What's a good way to do that using the ORM layer? That is, assuming that that the Table1 mappings are created through the ORM, where/how should I call the code to populate Table2?
I thought about using the after_insert hook, but i want to have a session to pass to the population method.
Thanks.
You can use the before_flush or after_flush hook, it provides a session. You then check session.new objects for newly created models (tip: use isinstance(object, ModelClass)) and do your work here.
In fact, SQLAlchemy recommends before_flush for general on flush changes.
Mapper-level flush events only allow very limited operations, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given Connection. Please read fully the notes at Mapper-level Events for guidelines on using these methods; generally, the SessionEvents.before_flush() method should be preferred for general on-flush changes.
After asking around in #sqlalchemy IRC, it was pointed out that this could be done using ORM-level relationships in an before_flush event listener.
It was explained that when you add a mapping through a relationship, the foreign key is automatically filled on flush, and the appropriate insert statement generated by the ORM.
Say I have RootEntity, AEntity(child of RootEntity), BEntity(child of AEntity).
class RootEntity(ndb.Model):
rtp = ndb.StringProperty()
class AEntity(ndb.Model):
ap = ndb.IntegerProperty()
class BEntity(ndb.Model):
bp = ndb.StringProperty()
So in different handlers I need to get instances of BEntity with specific ancestor(instance of AEntity).
There is a my query: BEntity.query(ancestor = ndb.Key("RootEntity", 1, "AEntity", AEntity.query(ancestor = ndb.Key("RootEntity", 1)).filter(AEntity.ap == int(some_value)).get().key.integer_id()))
How I can to optimize this query? Make it better, may be less sophisticated?
Upd:
This query is a part of function with #ndb.transactional decorator.
You should not use Entity Groups to represent entity relationships.
Entity groups have a special purpose: to define the scope of transactions. They give you ability to update multiple entities transactionally, as long as they are a part of the same entity group (this limitation has been somewhat relaxed with the new XG transactions). They also allow you to use queries within transactions (not available via XG transactions).
The downside of entity groups is that they have an update limitation of 1 write/second.
In your case my suggestion would be to use separate entities and make references between them. The reference should be a Key of the referenced entity as this is type-safe.
Regarding query simplicity: GAE unfortunately does not support JOINs or reference (multi-entity) queries, so you would still need to combine multiple queries together (as you do now).
There is a give and take with ancestor queries. They are a more verbose and messy to deal with but you get a better structure to your data and consistency in your queries.
To simplify this, if your handler knows the BEntity you want to get, just pass around the key.urlsafe() encoded key, it already has all of your ancestor information encoded.
If this is not possible, try possibly restructuring your data. Since these objects are all of the same ancestor, they belong to the same entity group, thus at most you can insert/update ~1 time per second for objects in that entity group. If you require higher throughput or do not require consistent ancestral queries, then try using ndb.KeyProperty to link entities with a reference to a parent rather than as an ancestor. Then you'd only need to get a single parent to query on rather than the parent and the parent's parent.
You should also try and use IDs whenever possible, so you can avoid having to filter for entities in your datastore by properties and just reference them by ID:
BEntity.query(ancestor = ndb.Key("RootEntity", 1, "AEntity", int(some_value)))
Here, int(some_value) is the integer ID of the AEntity you used when you created that object. Just be sure that you can ensure the IDs you manually create/use will be unique across all instances of that Model that share the same parent.
EDIT:
To clarify, my last example should have been made more clear in that I was suggesting to restructure the data such that int(some_value) be used as the integer ID of the AEntity rather than storing is as a separate property of the Entity - if possible of course. From the example given, a query is performed for the AEntity objects that have a given integer field value of int(some_value) and executed with a get() - implying that you will always expect a single value return for that integer ID making it a good candidate to use as the integer ID for the key of that object eliminating the need for a query.
I've just started learning Python Django and have a lot of experience building high traffic websites using PHP and MySQL. What worries me so far is Python's overly optimistic approach that you will never need to write custom SQL and that it automatically creates all these Foreign Key relationships in your database. The one thing I've learned in the last few years of building Chess.com is that its impossible to NOT write custom SQL when you're dealing with something like MySQL that frequently needs to be told what indexes it should use (or avoid), and that Foreign Keys are a death sentence. Percona's strongest recommendation was for us to remove all FKs for optimal performance.
Is there a way in Django to do this in the models file? create relationships without creating actual DB FKs? Or is there a way to start at the database level, design/create my database, and then have Django reverse engineer the models file?
If you don't want foreign keys, then avoid using
models.ForeignKey(),
models.ManyToManyField(), and
models.OneToOneField().
Django will automatically create an auto-increment int field named id that you can use to refer to individual records, or you can override that by marking a field as primary_key=True.
There is also documentation on running raw SQL queries on the database.
Raw SQL is as easy as this :
for obj in MyModel.objects.raw('SELECT * FROM myapp_mymodel'):
print obj
Denormalizing a database is up to you at model definition time.
You can use non-relational databases (MongoDB, ...) too with Django NonRel
django-admin inspectdb allows you to reverse engineer a models file from existing tables. That is only a very partial response to your question ;)
You can just create the model.py and avoid having SQL Alchemy automatically create the tables leaving it up to you to define the actual tables as you please. So although there are foreign key relationships in the model.py this does not mean that they must exist in the actual tables. This is a very good thing considering how ludicrously foreign key constraints are implemented in MySQL - MyISAM just ignores them and InnoDB creates a non-optional index on every single one regardless of whether it makes sense.
I concur with the 'no foreign keys' advice (with the disclaimer: I also work for Percona).
The reason why it is is recommended is for concurrency / reducing locking internally.
It can be a difficult "optimization" to sell, but if you consider that the database has transactions (and is more or less ACID compliant) then it should only be application-logic errors that cause foreign-key violations. Not to say they don't exist, but if you enable foreign keys in development hopefully you should find at least a few bugs.
In terms of whether or not you need to write custom SQL:
The explanation I usually give is that "optimization rarely decreases complexity". I think it is okay to stick with an ORM by default, but if in a profiler it looks like one particular piece of functionality is taking a lot more time than you suspect it would when written by hand, then you need to be prepared to fix it (assuming the code is called often enough).
The real secret here is that you need good instrumentation / profiling in order to be frugal with your complexity adding optimization(s).