How does Hibernate store second-level cache entries
Introduction The benefit of using a database access abstraction layer is that caching can be implemented transparently, without leaking into the business logic code. Hibernate Persistence Context acts as a transactional write-behind cache, translating entity state transitions into DML statements. The Persistence Context acts as a logical transaction storage, and each Entity instance can have at-most one managed reference. No matter how many times we try to load the same Entity, the Hibernate Session will always return the same object reference. This behavior is generally depicted as the first-level cache. The Hibernate… Read More
JDBC Statement fetchSize property
Introduction In this article, we are going to see how the JDBC Statement fetchSize property works when using Oracle, SQL Server, PostgreSQL, or MySQL. JDBC ResultSet fetching The JDBC ResultSet offers a client-side cursor for fetching the current statement return data. When the statement gets executed, the result must be transferred from the database cursor to the client-side one. This operation can either be done at once or on demand.
How to batch DELETE statements with Hibernate
Introduction In my previous post, I explained the Hibernate configurations required for batching INSERT and UPDATE statements. This post will continue this topic with DELETE statements batching. Domain model entities We’ll start with the following entity model:
How to batch INSERT and UPDATE statements with Hibernate
Introduction JDBC has long been offering support for DML statement batching. By default, all statements are sent one after the other, each one in a separate network round-trip. Batching allows us to send multiple statements in one-shot, saving unnecessary socket stream flushing. Hibernate hides the database statements behind a transactional write-behind abstraction layer. An intermediate layer allows us to hide the JDBC batching semantics from the persistence layer logic. This way, we can change the JDBC batching strategy without altering the data access code. Configuring Hibernate to support JDBC batching is not… Read More
How does CascadeType.LOCK works in JPA and Hibernate
Introduction Having introduced Hibernate explicit locking support, as well as Cascade Types, it’s time to analyze the CascadeType.LOCK behavior. A Hibernate lock request triggers an internal LockEvent. The associated DefaultLockEventListener may cascade the lock request to the locking entity children. Since CascadeType.ALL includes CascadeType.LOCK too, it’s worth understanding when a lock request propagates from a Parent to a Child entity.
A beginner’s guide to JPA and Hibernate Cascade Types
Introduction In this article, we are going to learn how the JPA and Hibernate Cascade Types work. JPA translates entity state transitions to database DML statements. Because it’s common to operate on entity graphs, JPA allows us to propagate entity state changes from Parents to Child entities. This behavior is configured through the CascadeType mappings.
How do LockModeType.PESSIMISTIC_READ and LockModeType.PESSIMISTIC_WRITE work in JPA and Hibernate
Introduction Java Persistence API comes with a thorough concurrency control mechanism, supporting both implicit and explicit locking. The implicit locking mechanism is straightforward and it relies on: Optimistic locking: Entity state changes can trigger a version incrementation Row-level locking: Based on the current running transaction isolation level, the INSERT/UPDATE/DELETE statements may acquire exclusive row locks While implicit locking is suitable for many scenarios, an explicit locking mechanism can leverage a finer-grained concurrency control. In my previous posts, I covered the explicit optimistic lock modes: OPTIMISTIC OPTIMISTIC_FORCE_INCREMENT PESSIMISTIC_FORCE_INCREMENT In this post, I am… Read More
How does LockModeType.PESSIMISTIC_FORCE_INCREMENT work in JPA and Hibernate
Introduction In my previous post, I introduced the OPTIMISTIC_FORCE_INCREMENT Lock Mode and we applied it for propagating a child entity version change to a locked parent entity. In this post, I am going to reveal the PESSIMISTIC_FORCE_INCREMENT Lock Mode and compare it with its optimistic counterpart.
The best way to use OPTIMISTIC_FORCE_INCREMENT with JPA and Hibernate
Introduction In this article we are going to see what is the best way to use the OPTIMISTIC_FORCE_INCREMENT LockModeType with JPA and Hibernate. With LockModeType.OPTIMISTIC, the locked entity version is checked towards the end of the current running transaction, to make sure we don’t use a stale entity state. Because of the application-level validation nature, this strategy is susceptible to race-conditions, therefore requiring an additional pessimistic lock . The LockModeType.OPTIMISTIC_FORCE_INCREMENT not only it checks the expected locked entity version, but it also increments it. Both the check and the update happen in… Read More
How to fix optimistic locking race conditions with pessimistic locking
Recap In my previous post, I explained the benefits of using explicit optimistic locking. As we then discovered, there’s a very short time window in which a concurrent transaction can still commit a Product price change right before our current transaction gets committed. This issue can be depicted as follows: Alice fetches a Product She then decides to order it The Product optimistic lock is acquired The Order is inserted in the current transaction database session The Product version is checked by the Hibernate explicit optimistic locking routine The price engine manages… Read More
