Home » Author Archives: Vlad Mihalcea (page 2)

Author Archives: Vlad Mihalcea

Vlad Mihalcea is a software architect passionate about software integration, high scalability and concurrency challenges.

How does Hibernate Collection Cache work

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Introduction Previously, I described the second-level cache entry structure, Hibernate uses for storing entities. Besides entities, Hibernate can also store entity associations and this article will unravel the inner workings of collection caching.       Domain model For the up-coming tests we are going to use the following entity model: A Repository has a collection of Commit entities: @org.hibernate.annotations.Cache( ...

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How to optimize Hibernate EllementCollection statements

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Introduction Hibernate supports three data mapping types: basic (e.g String, int), Embeddable and Entity. Most often, a database row is mapped to an Entity, each database column being associated to a basic attribute. Embeddable types are more common when combining several field mappings into a reusable group (the Embeddable being merged into the owning Entity mapping structure). Both basic types ...

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How does Hibernate READ_ONLY CacheConcurrencyStrategy work

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Introduction As I previously explained, enterprise caching requires diligence. Because data is duplicated between the database (system of record) and the caching layer, we need to make sure the two separate data sources don’t drift apart. If the cached data is immutable (neither the database nor the cache are able modify it), we can safely cache it without worrying of ...

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A beginner’s guide to Cache synchronization strategies

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Introduction A system of record is the authoritative data source when information is scattered among various data providers. When we introduce a caching solution, we automatically duplicate our data. To avoid inconsistent reads and data integrity issues, it’s very important to synchronize the database and the cache (whenever a change occurs into the system). There are various ways to keep ...

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Things to consider before jumping to enterprise caching

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Introduction Relational database transactions are ACID and the strong consistency model simplifies application development. Because enabling Hibernate caching is one configurations away, it’s very appealing to turn to caching whenever the data access layer starts showing performance issues. Adding a caching layer can indeed improve application performance, but it has its price and you need to be aware of it. ...

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How does Hibernate store second-level cache entries

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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 ...

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SELECT statements batch fetching with JDBC and Hibernate

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Introduction Now that I covered Hibernate batch support for INSERT, UPDATE and DELETE statements, it’s time to analyze SELECT statements result set batch fetching. JDBC ResultSet fetching The JDBC ResultSet offers a client-side Proxy 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. ...

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How to batch DELETE statements with Hibernate

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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:       The Post entity has a one-to-many association to a Comment and a one-to-one relationship with the PostDetails entity: @OneToMany(cascade = ...

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How to batch INSERT and UPDATE statements with Hibernate

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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 ...

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Hibernate CascadeType.LOCK gotchas

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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. ...

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