Fetchall sqlalchemy relationship

python - How to map the result from a query to a custom object in sqlalchemy? - Stack Overflow

Collect useful snippets of SQLAlchemy. from "EX1" where id=1;') result = ddttrh.infoe('SELECT * FROM "EX1"') print(ddttrh.infoll()) from ddttrh.info import relationship from ddttrh.infoative import declarative_base. When the linkage defines a one-to-many or many-to-many relationship, it's represented as a Python collection when objects are loaded and manipulated. By “related objects” we refer to collections or scalar associations configured on a mapper using relationship(). This behavior can be configured at mapper.

However when instances of MyClass are freshly loaded from the database, the children collection stays empty. The noload strategy is also available on a query option basis using the orm.

The rationale for this is to ensure that an application is not emitting any unexpected lazy loads within a certain context. The raise strategy is also available on a query option basis using the orm. New in version 1. When using MySQL, an appropriate storage engine must be selected. When using SQLite, foreign key support must be enabled explicitly. See Foreign Key Support for details. By default, this collection is a list: This because objects are always loaded from the database as lists, and a key-generation strategy must be available to populate the dictionary correctly.

It produces a dictionary class that will apply a particular attribute of the mapped class as a key. Below we map an Item class containing a dictionary of Note items keyed to the Note.

Such as, when assigning to Item. Note 'a', 'atext''b': Using a regular Python property allows virtually any detail or combination of details about the object to be used as the key, as below when we establish it as a tuple of Note. Assigning to this reverse relationship, the Note is added to the Item. The key value must be immutable for the lifetime of the object. You can not, for example, map on foreign key values if those key values will change during the session, i.

Returns a MappedCollection factory with a keying function generated from keyfunc, a callable that takes an entity and returns a key value. In other cases, special decorators are needed to tell SQLAlchemy more detail about how the collection operates. As additional rows are received for a User object just loaded in a previous row, the additional columns that refer to new Address objects are directed into additional results within the User. Joined eager loading used for scalar references is however compatible with Query.

It is critical to understand the distinction that while Query. A particular query might start out using all lazy loads. After using it in context, it might be revealed that particular attributes or collections are always accessed, and that it would be more efficient to change the loader strategy for these.

The strategy can be changed with no other modifications to the query, the results will remain identical, but fewer SQL statements would be emitted. In theory and pretty much in practicenothing you can do to the Query would make it load a different set of primary or related objects based on a change in loader strategy.

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The correct way to load the User records and order by email address is to use Query. We can add joinedload back in, so that there are two joins - one is that which we are ordering on, the other is used anonymously to load the contents of the User. In this case, the two joins most probably appear redundant - which they are. But to see why joinedload does what it does, consider if we were filtering on a particular Address: One will match exactly one row, that of the join of User and Address where Address.

By changing the usage of joinedload to another style of loading, we can change how the collection is loaded completely independently of SQL used to retrieve the actual User rows we want. Below we change joinedload into subqueryload: The operation of subquery eager loading is to emit a second SELECT statement for each relationship to be loaded, across all result objects at once. This SELECT statement refers to the original SELECT statement, wrapped inside of a subquery, so that we retrieve the same list of primary keys for the primary object being returned, then link that to the sum of all the collection members to load them at once: Secondly, it allows for many collections to be eagerly loaded without producing a single query that has many JOINs in it, which can be even less efficient; each relationship is loaded in a fully separate query.

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Finally, because the additional query only needs to load the collection items and not the lead object, it can use an inner JOIN in all cases for greater query efficiency.

Disadvantages of subqueryload include that the complexity of the original query is transferred to the relationship queries, which when combined with the use of a subquery, can on some backends in some cases notably MySQL produce significantly slow queries. The newer style of loading provided by selectinload solves these limitations of subqueryload. Without it, there is a chance that the inner query could return the wrong rows: Additionally, select IN loading applies itself to subsets of the load result at a time, so unlike joined and subquery eager loading, is compatible with batching of results using Query.

Overall, especially as of the 1. The only scenario in which selectin eager loading is not feasible is when the model is using composite primary keys, and the backend database does not support tuples with IN, which includes SQLite, Oracle and SQL Server. New in version 1. This style of loading emits a SELECT that refers to the primary key values of the parent object inside of an IN clause, in order to load related associations: Because the relatonship between User and Address provides that the primary key values for User can be derived from Address.

Changed in version 1.

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Things to know about this kind of loading include: In comparison, joined and subquery eager loading always refer to multiple JOINs up to the original parent. First, you can pretend it's a tuple and access its columns by position. We can also access the row as if it were a dictionary row['name']. SQLAlchemy lets us access the columns as if they were attributes of the row object. And finally, we can even use the actual Column objects themselves as keys to lookup results from a row.

You probably won't use this very often, but it can be extremely useful in some circumstances. This is especially useful when you expect your SELECT query to return a huge result set that would be too large to load into memory: Select Statements Now let's take a little time to examine some of the various ways in which we can select rows from our database.

Copy the following code into "selectdemo.

SQLAlchemy — Python Tutorial

Just load it from the database using the "autoload" feature. If you forget the parentheses, you'll be surprised by the results: This is an oversight, and the next SQLAlchemy release will fix this. But if you're running the 0. Joins At this point, you're probably wondering about using multiple tables in a single select statement.

Copy the following into "joindemo. Default is a "left outer join", which means "all records from the left-hand table, plus their corresponding values from the right-hand table, if any". Now let's move on to the really interesting part: Mapping your objects to SQL rows Now for the really interesting part: In other object-relational mappers such as SQLObject, the table definition also doubles as the class whose instances are rows of data from the table. SQLAlchemy, on the other hand, makes a strict distinction between the table definition and the data class.

You first create the table definition, then create an empty class definition that will hold your data objects, and then create a mapper that will map that class onto the database. It's perhaps easier to show how this works than to explain it. Copy the following into "mapper1. Unless you've deleted that file, the data four users and four email addresses should still be there as well.