The pooled and the pooled-lo optimizers are very useful to reduce the number of database roundtrips when writing multiple entities per database transaction.
Hibernate supports both entity queries (JPQL/HQL and Criteria API) and native SQL statements.
statements, make sure that you have all the right configuration properties in place, like ordering inserts and updates and activating batching for versioned data.
Check out this article for more details on this topic.
When the application is deployed on a production-like QA environment, and the deploy worked as expected, then pushing the deploy to a production environment should be straightforward since the latest schema migration was already tested.This has many benefits like reducing the load on the currently running Persistence Context because DTO projections don’t need to be managed.Although the second-level cache can reduce transaction response time since entities are retrieved from the cache rather than from the database, there are other options to achieve the same goal, and you should consider these alternatives prior to jumping to a second-level cache layer: Topically, a key-value application-level cache like Memcached or Redis is a common choice to store data aggregates.If you can duplicate all data in the key-value store, you have the option of taking down the database system for maintenance without completely loosing availability since read-only traffic can still be served from the cache.One of the main challenges of using an application-level cache is ensuring data consistency across entity aggregates.
That’s where the second-level cache comes to the rescue.