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CHAPTER 13 Optimizing fetching and caching available memory. You ll have to consult the JBoss Cache documentation for eviction policy configuration; the usage of Hibernate region names and eviction settings is similar to EHCache. JBoss Cache also supports invalidation instead of replication of modified data in a cluster, a potentially better performing choice. The Hibernate query cache, however, requires replication. You can also switch to OPTIMISTIC locking instead of pessimistic, again boosting scalability of the clustered cache. Doing so requires a different Hibernate cache provider plug-in, org.hibernte.cache.OptimisticTreeCacheProvider. Finally, let s look at the JGroups cluster communication configuration. The order of communication protocols is extremely important, so don t change or add lines randomly. Most interesting is the first protocol, . The loopback attribute must be set to true if node A is a Microsoft Windows machine (it isn t in this case). The other JGroups attributes are more complex and can be found in the JGroups documentation. They deal with the discovery algorithms used to detect new nodes in a group, failure detection, and, in general, the management of the group communication. After changing the cache concurrency strategy of your persistent classes to transactional (or read-only) and creating a treecache.xml file for node A, you can start up your application and look at the log output. We recommend enabling DEBUG logging for the org.jboss.cache package; you ll then see how JBoss Cache reads the configuration and reports node A as the first node in the cluster. To deploy node B, deploy the application on that node; no configuration file needs to be changed (if the second node also isn t a Microsoft Windows machine). If you start this second node, you should see join messages on both nodes. Your Hibernate application now uses fully transactional caching in a cluster. There is one final optional setting to consider. For cluster cache providers, it may be better to set the Hibernate configuration option hibernate.cache.use_ minimal_puts to true. When this setting is enabled, Hibernate adds an item to the cache only after checking to ensure that the item isn t already cached. This strategy performs better if cache writes (puts) are much more expensive than cache reads (gets). This is the case for a replicated cache in a cluster, but not for a local cache or a cache provider that relies on invalidation instead of replication. No matter if you re using a cluster or a local cache, you sometimes need to control it programmatically, either for testing or tuning purposes.
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Caching in practice down_thread=”false”/>

Granted, this configuration file may look scary at first, but it s easy to understand. You have to know that it s not only a configuration file for JBoss Cache, it s many things in one: a JMX service configuration for JBoss AS deployment, a configuration file for TreeCache, and a fine-grained configuration of JGroups, the communication library. Ignore the JBoss-deployment related first lines and look at the first attribute, TransactionManagerLookupClass. The GenericTransactionManagerLookup tries to find the transaction manager in most popular application servers, but it also works in a stand-alone environment without JTA (clustered caching without a transaction manager is a rare scenario). If JBoss Cache throws an exception on startup, telling you that it can t find the transaction manager, you ll have to create such a lookup class yourself for your JTA provider/application server. Next are the configuration attributes for a replicated cache that uses synchronized communication. This means that a node sending a synchronization message waits until all nodes in the group acknowledge the message. This is a good choice for a real replicated cache; asynchronous nonblocking communication would be more appropriate if the node B was a hot standby (a node that immediately takes over if node A fails) instead of a live partner. This is a question of failover versus computing capacity, both good reasons to set up a cluster. Most of the configuration attributes should be self-explanatory, such as timeouts and the fetching of state when a node comes into a cluster. JBoss Cache can also evict elements, to prevent memory exhaustion. In this example, you don t set up an eviction policy, so the cache slowly starts to fill all
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CHAPTER 13 Optimizing fetching and caching
org.hibernate.cache.TreeCacheProvider JBoss Cache has its own configuration file, treecache.xml, expected in the class- path of your application. In some scenarios, you need a different configuration for each node in your cluster, and you must make sure the correct file is copied to the classpath on deployment. Let s look at a typical configuration file. In the two-node cluster (named MyCluster), this file is used on node A: jboss:service=Naming jboss:service=TransactionManager org.jboss.cache.GenericTransactionManagerLookup MyCluster PESSIMISTIC REPL_SYNC REPEATABLE_READ false 20000 20000 15000
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Caching in practice scalable: No Hibernate aspect limits the nodes on which your application is deployed. With a few changes to your cache setup, you may even use a clustered caching system. We recommend JBoss Cache, a cluster-safe caching system based on TreeCache and the JGroups multicast library. JBoss Cache is extremely scalable and cluster communication can be tuned in any way imaginable. We now step through a setup of JBoss Cache for CaveatEmptor for a small cluster of two nodes, called node A and node B. However, we only scratch the surface of the topic, cluster configurations are by nature complex and many settings depend on the particular scenario. First, you have to check that all the mapping files use read-only or transactional as a cache concurrency strategy. These are the only strategies supported by the JBoss Cache provider. There is a nice trick that helps avoiding this search and replace problem in the future. Instead of placing elements in your mapping files, you can centralize cache configuration in your hibernate.cfg.xml:
You enabled transactional caching for Item and the bids collection in this example. However, there is one important caveat: At the time of writing, Hibernate runs into a conflict if you also have elements in the mapping file for Item. You therefore can t use the global configuration to override the mapping file settings. We recommend using the centralized cache configuration right from the start, especially if you aren t sure how your application may be deployed. It s also easier to tune cache settings with a single configuration location. The next step to the cluster setup is the configuration of the JBoss Cache provider. First, you enable it in the Hibernate configuration for example, if you aren t using properties, in hibernate.cfg.xml:
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CHAPTER 13 Optimizing fetching and caching The timeToIdleSeconds attribute defines the expiry time in seconds since an element was last accessed in the cache. You must set a sensible value here, because you don t want unused bids to consume memory. The timeToLiveSeconds attribute defines the maximum expiry time in seconds since the element was added to the cache. Because bids are immutable, you don t need them to be removed from the cache if they re being accessed regularly. Hence, timeToLive- Seconds is set to a high number. The result is that cached bids are removed from the cache if they haven t been used in the past 30 minutes or if they re the least recently used item when the total size of the cache has reached its maximum limit of 50,000 elements. You disable the disk-based cache in this example because you anticipate that the application server will be deployed to the same machine as the database. If the expected physical architecture was different, you might enable the disk-based cache to reduce network traffic. Accessing data on the local disk is faster than accessing the database across a network. Optimal cache eviction policies are, as you can see, specific to the data and application. You must consider many external factors, including available memory on the application server machine, expected load on the database machine, network latency, existence of legacy applications, and so on. Some of these factors can t be known at development time, so you often need to iteratively test the performance impact of different settings in the production environment or a simulation. We consider optimization with the second-level cache something you won t do during development, because testing without real datasets and concurrency doesn t show the final performance and scalability of the system. This is especially true in a more complex scenario, with a replicated cache in a cluster of machines. 13.4.4 Setting up a replicated cache EHCache is an excellent cache provider if your application is deployed on a single virtual machine. However, enterprise applications supporting thousands of concurrent users may require more computing power, and scaling your application may be critical to the success of your project. Hibernate applications are naturally
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