Fix handling of PendingRollbackError when processing large datasets #307
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This exception is raised when the database disconnects mid-operation and has to be handled outside the ORM. This change includes some changes to the DB engine init args which make getting bad/stale connections less likely. It also changes the main work loop to operate of batches of paths, using a fresh DB session for each and adds handling for the remaindre of a batch should an exception occur somewhere in the middle of it. SQLAlchemy errors are handled specially, by propagating them up to where they can trigger a rollback and allow work to continue with a new DB session. This updates the Python version requirement to 3.12 (to get itertools.batched())
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This exception is raised when the database disconnects mid-operation and has to be handled outside the ORM.
This change includes some changes to the DB engine init args which make getting bad/stale connections less likely. It also changes the main work loop to operate on batches of paths, using a fresh DB session for each and adds handling for the remainder of a batch should an exception occur somewhere in the middle of it. SQLAlchemy errors are handled specially, by propagating them up to where they can trigger a rollback and allow work to continue with a new DB session.
This updates the Python version requirement to 3.12 (to get itertools.batched())