Sqlalchemy relationship through table welding

SQLAlchemy Association Tables | Python Central

a TextAsFrom with a positional set of columns, those columns should be welded to it. The statement should be able to work in any ORM context flawlessly, primary_key=True) bs = relationship("B") class B(Base): __tablename__ = 'b' id to an ORM or Core table model, no system of labeling or de-duping of common . [Sqlalchemy-tickets] Issue # SQL Server VARCHAR(MAX) on reflection can' t find a simple test program that creates a table with a String() column, then reflects it with a positional set of columns, those columns should be welded to it . primary_key=True) bs = relationship("B") class B(Base): __tablename__ = 'b' id. Within this effort, I'd like to stress that from my POV, everything is on the table, and mature features provided by SQLAlchemy (namely relationship() and it's .. the code more repetetive and inconsistent, welds together application-level and.

Read on, I have some things we can start with.

Struggling with SQLAlchemy : learnpython

I agree that a lot of the patterns that I've seen in Nova in particular are not well-suited to the approach used by SQLAlchemy's ORM, however I have observed that the applications still rely a lot on very sophisticated and mature features provided by SQLAlchemy namely relationship and it's integration with eager loading and the unit of work featuresand throwing it away entirely will incur a lot of reinvention, not just of the features that were lost but also of a totally new crop of stability and performance issues to solve.

To that degree, I'd like to propose a twist to the "we want to write our own ORM" idea edit: As a secondary issue, some devs I've spoken to have referred to the nature of OpenStack API-based applications being a part of the problem.

This refers to the fact that OpenStack apps like Nova and Neutron expose fine-grained APIs which end up breaking out composed use cases into dozens or hundreds of small API operations, each of which invokes within it's own database transaction and ORM session state.

This makes it impossible, without introducing some elaborate offline state management system, for a larger operation to be performed so that larger groups of objects can be loaded and manipulated at once, greatly decreasing the number of database round trips present. No matter what ORM you use or don't use, the pattern of many small round trips is difficult to scale on relational databases.

On my end, I have no intention of focusing on this second issue for the time being, I'm going to stick with leaving OS applications as much as they are right now as possible, applying iterative changes to database access code which I hope can optimize to a significant degree within the current usage patterns.

It is not at all fun to maintain the same datamodel and API against both a KV store as well as relational tables. I had a detailed look at Ceilometer with its team members early on, and while I'm pretty confident that if it were desireable, I could get it's relational backend to compete with the Mongo backend performance-wise, there's no reason to get into this if the Mongo licensing issue can be solved alone.

Naturally, I picked an example that is very juicy in this regard; it has a very easily fixable issue with the kind of query it emits that has an enormous 10x impact on its performance which I feel is worth illustrating just for how dramatic it isand it then illustrates some new SQLAlchemy extensions that more or less may be going into SQLAlchemy directly, which allow the remaining operation to complete in less than half the time.

These extensions can be ported to oslo. The profiling focuses on the nature of this method as an API feature all it's own, and simulates the case where the API method is called thousands of times, not unlike other API methods that seek to add some small amount of data each time they are called. Running scenario default Scenario default, total calls for operations: Any API feature that only returns a simple read-only object or a "values" dictionary that does not rely upon relationship loading should be using this pattern, or similar, by using a Column Bundle.

I will be exploring adding the following feature to oslo.

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Relationships might be tricky here but I can at least get them on board as regular lazy loads just like any other ORM would do anyway. Fast Object Save We explore replacing the unit of work flush call used by object. Baked Queries Something that has been in the works for a long time and has recently seen lots of work in the past months is the "baked query" feature; this pattern is ideal for OpenStack's "many short queries" pattern, and allows caching of the generation of SQL.

Recent versions of this pattern have gotten very slick, and can cache virtually everything that happens Python-wise from the construction of the Query object, to calling all the methods on the query, to the query-objects construction of a Core SQL statement, to the compilation of that statement as a string - all of these steps are removed from the call-graph after the first such call.

The pattern involves a bit more verbosity to that of constructing a query, where here I've built off of some of the ideas of the Pony ORM to use Python function information as the source of a cache key. A query such as: For this slight increase in verbosity, we get an improvement like this: Migrations Migrations are a huge deal.

Here's where it looks like this is going. And in talking with Nova devs, they really like that they can test their migrations against SQLite. Unless folks think otherwise. In order to provide the full suite of ALTER operations that all other databases provide, tools such as SQLAlchemy-Migrate will create a copy of the target table with changes applied, copy data from old table to new, then drop the old table and rename the new one. But it has been on the Alembic roadmap to add SQLite migrations in a style similar to that of Migrate for a long time, emulating this same approach in some way.

Alembic will have this! However in talking with some folks, it appears like some people might actually like this "make a new table and switch it" approach for other databases too, as a way to work around locking of tables. But by the time I got Alembic released and later autogenerate working, Migrate already had it. Here's what we need to finish up in autogenerate: ForeignKeyConstraint change detection - this is an entirely straightforward feature add and has long been on the todo list.

MySQL implicit indexes - this is actually done; recent versions of Alembic can navigate around MySQLs goofy production of indexes automatically on foreign key columns, and not accidentally spit them out in autogenerates. It was seriously tough to get index autogen mostly working on all backends, so new issues will continue to be fixed as they are reported. Type comparison - Alembic balks on type comparison by default, because ultimately SQLAlchemy should add comparision features to its type objects natively.

Alembic already allows user-specified rules in this regard, so they can be part of oslo. Table order - Alembic's autogenerate should be spitting out table creates and drops in the order of foreign key dependency, though we don't have good test coverage for this yet and it might not be working.

We'll fix that, no biggie! Alternatively, Alembic could include a mode of operation that includes all the ForeignKeyConstraint objects after all the tables.

Input on how we'd like to see this work would be welcome. New Migration Features There's a lot of features I'd like to add to Alembic, and if OpenStack has a need for them, that would justify the effort: Lots of people are looking for it. This would turn Alembic's current "linked list" version model into a full directed acyclic graph DAG.

Any particular migration can be dependent on any other group of migrations, or none at all; individual branches can be maintained and upgraded along their path, or automatically merged.

Multiple version directories - This would allow migration files to be present in more than one place for a single migrations environment.

Currently, you can get this approach by using multiple base directories, but that requires separate env. With this issue, cross-dependent migration files can live in multiple places, working nicely with the multiple heads support of In this case, the message wants us to qualify each relationship by instructing for each one which foreign key column should be considered, and the appropriate form is as follows: The linkage of the two columns also plays a role during persistence; the newly generated primary key of a just-inserted Address object will be copied into the appropriate foreign key column of an associated Customer object during a flush.

The custom criteria we use in a primaryjoin is generally only significant when SQLAlchemy is rendering SQL in order to load or represent this relationship.

The objects will remain present in the collection until the attribute is expired and re-loaded from the database where the criterion is applied.

Configuring how Relationship Joins — SQLAlchemy Documentation

The city criteria has no effect here, as the flush process only cares about synchronizing primary key values into referencing foreign key values. We need to use cast in order to cast one side of the join to the type of the other: For custom operators we use the Operators. What this refers to originates from the fact that Article. The warning lets us know this is the case. To solve this, we need to break out the behavior of Article to include all three of the following features: Article first and foremost writes to Article.

SQLAlchemy 1.3 Documentation

Article can write to Article. To get just 1 and 2, we could specify only Article. One such example is the materialized path pattern, where we compare strings for overlapping path tokens in order to produce a tree structure.

Through careful use of foreign and remotewe can build a relationship that effectively produces a rudimentary materialized path system.

Inserting, Updating, and Deleting from a Database in Flask-SQLAlchemy

Support has been added to allow a single-column comparison to itself within a primaryjoin condition, as well as for primaryjoin conditions that use ColumnOperators.