Why Doesn\'t My SQLAlchemy Data Update After I Change it in the Database?

Overcoming ORM Data Caching in SQLAlchemy

When leveraging SQLAlchemy for data management, users may encounter a perceived caching issue wherein retrieved data remains unchanged despite updates made directly to the database. This can be a puzzling predicament.

The Cache Unveiled: Understanding Transaction Isolation

The behavior experienced is not due to an explicit cache in SQLAlchemy, but rather a fundamental characteristic of transaction isolation. By default, SQLAlchemy operates in a transactional mode. Data modifications are only written to the database upon calling session.commit().

However, transaction isolation introduces a twist. While other active transactions cannot immediately access the modified data, they may also be unable to see it even after committing or rolling back their own transactions. This phenomenon, known as repeatable reads, ensures that transactions maintain the state they've loaded, even when the actual data has changed.

Disabling the Perceived Caching

While not a true cache, the effects of transaction isolation can be mitigated by using connection pooling. By ensuring that all connections originate from a common pool, you can avoid data caching across different connections and improve the visibility of updates. To enable connection pooling, set the pool_recycle parameter to a non-zero value.

For example:

engine = create_engine('mysql://user:password@host/db', pool_recycle=3600)

In this instance, connections will be recycled every 3600 seconds, preventing data caching issues related to transaction isolation.

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