Introduction

In my previous post I described how application-level transactions offer a suitable concurrency control mechanism for long conversations.

All entities are loaded within the context of a Hibernate Session, acting as a transactional write-behind cache.

A Hibernate persistence context can hold one and only one reference to a given entity. The first level cache guarantees session-level repeatable reads.

If the conversation spans over multiple requests we can have application-level repeatable reads. Long conversations are inherently stateful so we can opt for detached objects or long persistence contexts. But application-level repeatable reads require an application-level concurrency control strategy such as optimistic locking.

The catch

But this behavior may prove unexpected at times.

If your Hibernate Session has already loaded a given entity then any successive entity query (JPQL/HQL) is going to return the very same object reference (disregarding the current loaded database snapshot):

In this example, we can see that the first level cache prevents overwriting an already loaded entity. To prove this behavior, I came up with the following test case:

doInTransaction(session -> {
    Product product = new Product();
    product.setId(1L);
    product.setQuantity(7L);
    session.persist(product);
});
doInTransaction(session -> {
    final Product product = (Product) session.get(Product.class, 1L);
    try {
        executeSync(() -> doInTransaction(_session -> {
            Product otherThreadProduct = (Product) _session.get(Product.class, 1L);
            assertNotSame(product, otherThreadProduct);
            otherThreadProduct.setQuantity(6L);
        }));
        Product reloadedProduct = (Product) session.createQuery("from Product").uniqueResult();
        assertEquals(7L, reloadedProduct.getQuantity());
        assertEquals(6L, 
            ((Number) session
            .createSQLQuery("select quantity from product where id = :id")
            .setParameter("id", product.getId())
            .uniqueResult())
            .longValue()
        );
    } catch (Exception e) {
        fail(e.getMessage());
    }
});

This test case clearly illustrates the differences between entity queries and SQL projections. While SQL query projections always load the latest database state, entity query results are managed by the first level cache, ensuring session-level repeatable reads.

Workaround 1: If your use case demands reloading the latest database entity state then you can simply refresh the entity in question.

Workaround 2: If you want an entity to be disassociated from the Hibernate first level cache you can easily evict it, so the next entity query can use the latest database entity value.

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Conclusion

Hibernate is a means, not a goal. A data access layer requires both reads and writes and neither plain-old JDBC nor Hibernate is one-size-fits-all solutions. A data knowledge stack is much more appropriate for getting the most of your data read queries and write DML statements.

While native SQL remains the de facto relational data reading technique, Hibernate excels in writing data. Hibernate is a persistence framework and you should never forget that. Loading entities make sense if you plan on propagating changes back to the database. You don’t need to load entities for displaying read-only views, an SQL projection being a much better alternative in this case.

Session-level repeatable reads prevent lost updates in concurrent writes scenarios, so there’s a good reason why entities don’t get refreshed automatically. Maybe we’ve chosen to manually flush dirty properties and an automated entity refresh might overwrite synchronized pending changes.

Designing the data access patterns is not a trivial task to do and a solid integration testing foundation is worth investing in. To avoid any unknown behaviors, I strongly advise you to validate all automatically generated SQL statements to prove their effectiveness and efficiency.

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