![]() ![]() In fact, many developers accidentally use INNER JOIN instead, because INNER JOIN can implement a SEMI JOIN when joining a 1:1 or a M:1 relationship. With all of the above options, SQL would be a much more concise language for those cases where we’d like to quickly semi/anti join two relations. LEFT ANTI JOIN Dept d ON e.DeptName = d.DeptName In SQL, we would write the same relation using IN or EXISTS: “Semi” means that we don’t really join the right hand side, we only check if a join would yield results for any given tuple. The Wikipedia article on relational algebra nicely explains semi join and anti join visually:Īs you can see, the semi join relation Employee ⋉ Dept only contains attributes from the Employee relation, not from the Dept relation. We’ll be looking only at the first two in this article. Division (see also our previous article on division).However, there are three operators in relational algebra, that have no exact representation in SQL, and can only be expressed through “workarounds”. In most cases, SQL is much much more powerful than relational algebra. Relational operators without equivalent SQL syntax The above example “equi-joins” the ACTOR, FILM_ACTOR, and FILM tables from the Sakila database, in order to produce a new relation consisting of all the actors and all their associated films. One of the most common relational JOIN operations is the “equi-join” or SQL INNER JOIN. algebra nicely describes the various operations that we know in SQL as well from a more abstract, formal perspective. If anyone ends up here like me: the workaround to get the efficient query is: ![]() This One2Many inefficient query is still there at least with django 2.2. ('WHERE "demo_child"." parent_id" IS NULL' vs. So the same issue as with many-to-many where this query is orders of magnitude slower than a slightly modified one: ON ("demo_parent"."id" = "demo_child"."parent_id") SELECT "demo_parent"."id", "demo_parent"."name" Parents = models.ForeignKey(to=Parent, db_column="parent_id") I realized over the weekend that the same issue exists for one-to-many relationships. Sidenote: why exactly are we using deferred foreign keys, not foreign keys with DEFERRABLE INITIALLY IMMEDIATE and set them to deferred only when needed in fixture loading? Or am I missing some other valid use case for deferred foreign keys in Django? If so, that would naturally also make the above join-removal optimization invalid. Django has an advantage in that even if we are using deferred foreign keys, we know that in normal usage the defer should not have any effect. This complication of deferred foreign keys also make this a hard optimization target for databases. If the user ends up in situation where he has a row in the m2m table, but not in the target table, he has done something wrong - m2m targets should always be saved into the DB first before adding them to m2m collections. I don't know if that is something Django should guard against. There is a slight complication because of deferred foreign keys used in Django - it is possible to insert a row into course_students table without there being a matching row in course table. The last join is not needed, as the null/not null status of the first join tells us already if the filter condition is correct or not. Naturally, if there is no rows in the first join, then there will be no rows in the second join, either. This means that if the first join yields any rows, so must the second join, too. Foreign keys ensure that if there is a row in course_students table, then there will be a matching row in course table. The last join in that chain is not necessary at all. Thus a join against only the m2m intermediate table should suffice. ![]() The query is asking for items with no m2m assigned items. To see the generated SQL query, simply run "python manage.py anti-join." I'm attaching a sample project with the model already set up. Here's the difference I'm seeing on real data: Changing WHERE to "course_students"."student_id" IS NOT NULL yields orders of magnitude improved query plan. The problem is that the way the WHERE clause is generated is very inefficient (at least when used with Postgres). ON ("course_students"."course_id" = "course"."id") ON ("student"."id" = "course_students"."student_id") (course_isnull=True)ĭjango translates this into the following query: For background on this ticket please see the following two discussions:īasically, in a many-to-many mappings between models Student and Course, if I want to find all instances of Students that aren't registered for classes, I would issue the following Django query: ![]()
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