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Caching Best Practices
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In concurrency theory, locking is used for protecting mutable shared data against hazardous data integrity anomalies. Because lock management is a very complex problem, most applications rely on their data provider implicit locking techniques.
Delegating the whole locking responsibility to the database system can both simplify application development and prevent concurrency issues, such as deadlocking. Deadlocks can still occur, but the database can detect and take safety measures (arbitrarily releasing one of the two competing locks).
Most database systems use shared (read) and exclusive (write) locks, attributed to specific locking elements (rows, tables). While physical locking is demanded by the SQL standard, the pessimistic approach might hinder scalability.
Modern databases have implemented lightweight locking techniques, such as MVCC.
The implicit database locking is hidden behind the transaction isolation level configuration. Each isolation level comes with a predefined locking scheme, aimed at preventing a certain set of data integrity anomalies.
READ COMMITTED uses query-level shared locks and exclusive locks for the current transaction modified data. REPEATABLE READ and SERIALIZABLE use transaction-level shared locks when reading and exclusive locks when writing.
If database locking is sufficient for batch processing systems, a multi-request web flow spans over several database transactions. For long conversations, a logical (optimistic) locking mechanism is much more appropriate.
Paired with a conversation-level repeatable read storage, optimistic locking can ensure data integrity without trading scalability.
While implicit locking is probably the best choice for most applications concurrency control requirements, there might be times when you want a finer-grained locking strategy.
Most database systems support query-time exclusive locking directives, such as SELECT FOR UPDATE or SELECT FOR SHARE. We can, therefore, use lower level default isolation levels (READ COMMITTED), while requesting share or exclusive locks for specific transaction scenarios.
Most optimistic locking implementations verify modified data only, but JPA allows explicit optimistic locking as well.
As a database abstraction layer, JPA can benefit from the implicit locking mechanisms offered by the underlying RDBMS. For logical locking, JPA offers an optional automated entity version control mechanism as well.
JPA supports explicit locking for the following operations:
The LockModeType contains the following optimistic and pessimistic locking modes:
|Lock Mode Type||Description|
|NONE||In the absence of explicit locking, the application will use implicit locking (optimistic or pessimistic)|
|OPTIMISTIC||Always issues a version check upon transaction commit, therefore ensuring optimistic locking repeatable reads.|
|READ||Same as OPTIMISTIC.|
|OPTIMISTIC_FORCE_INCREMENT||Always increases the entity version (even when the entity doesn’t change) and issues a version check upon transaction commit, therefore ensuring optimistic locking repeatable reads.|
|WRITE||Same as OPTIMISTIC_FORCE_INCREMENT.|
|PESSIMISTIC_READ||A shared lock is acquired to prevent any other transaction from acquiring a PESSIMISTIC_WRITE lock.|
|PESSIMISTIC_WRITE||An exclusive lock is acquired to prevent any other transaction from acquiring a PESSIMISTIC_READ or a PESSIMISTIC_WRITE lock.|
|PESSIMISTIC_FORCE_INCREMENT||A database lock is acquired to prevent any other transaction from acquiring a PESSIMISTIC_READ or a PESSIMISTIC_WRITE lock and the entity version is incremented upon transaction commit.|
JPA 2.0 defined the javax.persistence.lock.scope property, taking one of the following values:
Because object graphs can span to multiple tables, an explicit locking request might propagate to more than one table (e.g. joined inheritance, secondary tables).
Because the entire entity associated row(s) are locked, many-to-one and one-to-one foreign keys will be locked as well but without locking the other side parent associations. This scope doesn’t propagate to children collections.
The explicit lock is propagated to element collections and junction tables, but it doesn’t lock the actual children entities. The lock is only useful for protecting against removing existing children, while permitting phantom reads or changes to the actual children entity states.
JPA 2.0 also introduced the javax.persistence.lock.timeout property, allowing us to configure the amount of time (milliseconds) a lock request will wait before throwing a PessimisticLockException.
Hibernate supports all JPA locking modes and some additional specific locking options. As with JPA, explicit locking can be configured for the following operations:
The LockModeConverter takes care of mapping JPA and Hibernate lock modes as follows:
|Hibernate LockMode||JPA LockModeType|
If you enjoyed this article, I bet you are going to love my upcoming Online Workshops!
- Caching Best Practices with JPA and Hibernate (2.5 hours) on the 30th of September
- High-Performance SQL (4 hours) on the 6th of October in collaboration with Voxxed Days Ticino
- High-Performance SQL (12 hours) starting on the 28th of October in collaboration with Bouvet
Hibernate also defines scope and timeout locking options:
The lock scope allows explicit locking cascade to owned associations.
A timeout interval may prevent a locking request from waiting indefinitely.
Hypersistence Optimizer 2.2 has been released!