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JBoss Cache, aside from storing the Lucene directory in memory in a cluster, has an interesting additional feature. You can combine Hibernate Search and JBoss Cache to enable full-text search capabilities on JBoss Cache distributed objects. A JBoss Cache module named JBossCacheSearchable has the ability to index and search Java objects stored in JBoss Cache. It uses Hibernate Search to index objects when they are added, updated, or removed from the cache. By reusing the same Hibernate Search mapping annotations, you can make your cached object full-text searchable. Check out the JBossCacheSearchable project at http://www.jboss.org/jbosscache (the current documentation lives in http://www.jboss.org/community/docs/DOC-10286).
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The synchronous approach has the benefit of propagating changes immediately to all nodes. It works best on:
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Small and medium-size indexes (especially if you use the in-memory approach) because network traffic will be needed to retrieve the index. Low to moderate write-intensive applications. Pessimistic locks still have to be acquired cluster-wise.
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Scalability: using Hibernate Search in a cluster
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The index size depends on a lot of factors, including the amount of RAM you dedicate to indexes. Try it in your environment. You can use Hibernate Search in a synchronized approach by configuring the DirectoryProvider on each Hibernate Search node to point to the same physical index storage. However, Hibernate Search comes with an interesting alternative solution.
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10.1.2 Asynchronous clustering
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Hibernate Search is fairly agnostic with regard to your architecture. The software is flexible and extensible enough to support a variety of clustering solutions. The ability to write your own DirectoryProvider (see section 5.1.5) gives you the freedom to decide how Lucene indexes are shared among different nodes in the cluster (physically sharing the same location, replicated across all nodes, and so on). A second extension point gives you the ability to customize the backend of Hibernate Search (see section 5.3.4). The backend is responsible for taking a list of indexing operations and performing them. Instead of writing straight to the Lucene index, an implementation could use JGroups (the communication library) to delegate the work to a thirdparty machine. That being said, Hibernate Search doesn t leave you alone in this ocean of flexibility. One approach is provided to you out of the box and is promoted by the Hibernate Search team. This approach is an answer to the pessimistic lock problem and its scalability issues. This architecture is shown in figure 10.2. A single node is responsible for writing in the Lucene index or indexes and pushes changes to all other nodes on a regular basis. This node is named the master in the Hibernate Search terminology. The other nodes (known as slaves) execute their fulltext queries on a local copy of the index and push their changes to a shared queue instead of applying them to their local Lucene indexes. The shared queue is exclusively processed by the master. This approach (one master, many slaves) has quite a few advantages:
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You no longer have to deal with NFS lock madness. The pessimistic lock is no longer shared but used exclusively by the master. Slaves that usually host the main application are no longer waiting on the index lock. We ve just freed scalability for our application. You can now align many slave nodes without problem. Slaves execute their full-text queries on a local copy of the index, keeping performance high and independent of the network. A temporary glitch won t affect the ability to answer full-text queries. Resource consumption (CPU, memory, I/O) on slave nodes is lower because indexing is delegated to the master.
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Let s have a look at this approach in a more detailed way.
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Exploring clustering approaches
Figure 10.2 Recommended architecture for asynchronous clustering
ANATOMY OF A SLAVE NODE
A slave node behaves and is in many ways just like a regular Hibernate Search application. The slave node collects all changes applied to indexed entities per transaction. It transforms the entity into its Lucene-friendly structure (a Document object). It executes full-text queries by opening a Lucene index (or indexes). However, instead of sharing the same Lucene index location with all other nodes, a slave keeps a local copy of the index. Having a local copy guarantees faster query response time (compared to an NFS solution) and ensures that a slave can serve fulltext queries autonomously. At regular intervals, a slave node obtains a copy of the master node index from a shared location. Remember, the slave doesn t apply changes to its local index; it passes the work list to the master. The master applies the changes to the Lucene index. Because of this asynchronous approach, there s a delay between the time an element is changed in the database and the time it s reflected by full-text queries. This
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