sqlalchemy dynamic filtering

Question

I\'m trying to implement dynamic filtering using SQLAlchemy ORM.

I was looking through StackOverflow and found very similar question:SQLALchemy dynamic filter_by

Answer 1

class Place(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    search_id = db.Column(db.Integer, db.ForeignKey('search.id'), nullable=False)

    @classmethod
    def dinamic_filter(model_class, filter_condition):
        '''
        Return filtered queryset based on condition.
        :param query: takes query
        :param filter_condition: Its a list, ie: [(key,operator,value)]
        operator list:
            eq for ==
            lt for <
            ge for >=
            in for in_
            like for like
            value could be list or a string
        :return: queryset
        '''
        __query = db.session.query(model_class)
        for raw in filter_condition:
            try:
                key, op, value = raw
            except ValueError:
                raise Exception('Invalid filter: %s' % raw)
            column = getattr(model_class, key, None)
            if not column:
                raise Exception('Invalid filter column: %s' % key)
            if op == 'in':
                if isinstance(value, list):
                    filt = column.in_(value)
                else:
                    filt = column.in_(value.split(','))
            else:
                try:
                    attr = list(filter(lambda e: hasattr(column, e % op), ['%s', '%s_', '__%s__']))[0] % op
                except IndexError:
                    raise Exception('Invalid filter operator: %s' % op)
                if value == 'null':
                    value = None
                filt = getattr(column, attr)(value)
            __query = __query.filter(filt)
        return __query

Execute like:

places = Place.dinamic_filter([('search_id', 'eq', 1)]).all()

Answer 2

For the people using FastAPI and SQLAlchemy, here is a example of dynamic filtering:

api/app/app/crud/order.py

from typing import Optional

from pydantic import UUID4
from sqlalchemy.orm import Session

from app.crud.base import CRUDBase
from app.models.order import Order
from app.schemas.order import OrderCreate, OrderUpdate


class CRUDOrder(CRUDBase[Order, OrderCreate, OrderUpdate]):

    def get_orders(
        self,
        db: Session,
        owner_id: UUID4,
        status: str,
        trading_type: str,
        pair: str,
        skip: int = 0,
        limit: int = 100,
    ) -> Optional[Order]:
        filters = {
            arg: value
            for arg, value in locals().items()
            if arg != "self" and arg != "db" and arg != "skip" and arg != "limit" and value is not None
        }
        query = db.query(self.model)
        for attr, value in filters.items():
            query = query.filter(getattr(self.model, attr) == value)
        return (
            query
            .offset(skip)
            .limit(limit)
            .all()
        )


order = CRUDOrder(Order)

Answer 3

I have a similar issue, tried to filter from a dictionary:

filters = {"field": "value"}

Wrong:

...query(MyModel).filter(**filters).all()

Good:

...query(MyModel).filter_by(**filters).all()

Answer 4

Your problem is that filter_by takes keyword arguments, but filter takes expressions. So expanding a dict for filter_by **mydict will work. With filter, you normally pass it one argument, which happens to be an expression. So when you expand your **filters dict to filter, you pass filter a bunch of keyword arguments that it doesn't understand.

If you want to build up a set of filters from a dict of stored filter args, you can use the generative nature of the query to keep applying filters. For example:

# assuming a model class, User, with attributes, name_last, name_first
my_filters = {'name_last':'Duncan', 'name_first':'Iain'}
query = session.query(User)
for attr,value in my_filters.iteritems():
    query = query.filter( getattr(User,attr)==value )
# now we can run the query
results = query.all()

The great thing about the above pattern is you can use it across multiple joined columns, you can construct 'ands' and 'ors' with and_ and or_, you can do <= or date comparisons, whatever. It's much more flexible than using filter_by with keywords. The only caveat is that for joins you have to be a bit careful you don't accidentally try to join a table twice, and you might have to specify the join condition for complex filtering. I use this in some very complex filtering over a pretty involved domain model and it works like a charm, I just keep a dict going of entities_joined to keep track of the joins.