create barcode image c# Synchronous clustering in Java

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10.1.1 Synchronous clustering
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The classic approach for clustering a Lucene application is to share the same Lucene directory or directories among all nodes. Each node can read and write on the shared index. To prevent index corruption, Lucene uses a pessimistic lock, which is acquired when an index is updated. At a given time, only one node can update a given index.
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Concurrent changes can be applied if they belong to different Lucene indexes. If you follow the Hibernate Search default approach, one global lock per entity type is present, and concurrent changes to two different entity types are possible.
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Other nodes must wait for the lock. Figure 10.1 shows this problem. If you use a filesystem directory provider, the lock is materialized as a lock file. Of course, the index storage must be shared among several nodes, usually on some NFS. Unfortunately, this approach suffers from problems:
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Some NFS implementations cache directory contents for faster file discovery with the assumption that the directory content doesn t change very often and that immediate visibility isn t a concern. Unfortunately, Lucene relies (partially) on an accurate listing of files. Some NFS implementations don t implement the delete on last close semantic, which is needed by Lucene.
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Scalability: using Hibernate Search in a cluster
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While the situation is much better than it was back in the days of Lucene 1.4, implementing concurrent index writers on an NFS is still quite complex, and bugs in this area show up regularly depending on your NFS client/server configuration. This work in progress is known as the NFS saga in the Lucene community. If you go that route, be sure to read the abundant literature on the subject and pay particular attention to lock exceptions during your performance and stress tests. Some libraries such as Compass propose a database-backed Lucene Directory. The index and its lock are stored in a specific set of tables in a database. Databases are known for implementing a good locking scheme, making them less likely to run into the problems encountered by NFS. Unfortunately, this approach suffers from flaws as well:
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In most implementations blobs are used to represent segment files. Blobs are well known for not being the fastest structure in a database. This is particularly true of MySQL, which cannot consider a blob as a stream and loads the entire data flow into client memory. A pessimistic lock must be shared among all read-write nodes. Under intensive write sessions, most nodes will wait for the lock to transform the clustered application into a gigantic nonconcurrent system. The more nodes present, the higher the risk to hit the scalability limit, because more nodes are competing for the single lock.
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Figure 10.1 The index s pessimistic lock must be shared among all nodes.
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An in-memory distributed Lucene Directory is another interesting approach that uses the synchronous pessimistic lock solution. You can find a few providers: GigaSpace, JBoss Cache, and Terracotta are all distributed data solutions that support clustering of Lucene directories. You can easily write a Hibernate Search DirectoryProvider implementation that wraps any of these solutions. Generally speaking, these solutions do several things:
Exploring clustering approaches
They keep a map that represents the current list of Lucene directories locked and guarantee that only one node can acquire a directory lock at a given time cluster-wide. They distribute the index content across the cluster. If a directory is too big to fit in memory, only part of the content is retrieved from the grid. Some kind of garbage collection ensures that only the latest-used content is kept in local memory for a given node.
This approach still suffers from the theoretical pessimistic lock limit, but advocates have reported good performance and scalability, especially when indexes are sharded. Sharding helps to push the scalability limit a bit further. While it s true that a cluster-wide lock needs to be acquired when updating a Lucene index, you can make smaller indexes. By sharding your indexes, you effectively need a finer-grained lock when updating data. The sharding strategy will acquire locks on only the impacted shards; concurrent nodes will be able to update other shards of the index. On big indexes, the in-memory approach will require more network traffic to load parts of the index that didn t fit in memory. Likewise, filesystem-based approaches require more disk input/output. This book won t cover the configuration details of using an in-memory approach. Check the documentation of the data-clustering project you re interested in. Note that in production, file-based Lucene indexes are by far the most commonly used deployment mode.
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