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## Chapter 21. Caching

In almost all enterprise applications, the database is the primary bottleneck, and the least scalable tier of the runtime environment. People from a PHP/Ruby environment will try to tell you that so-called "shared nothing" architectures scale well. While that may be literally true, I don't know of many interesting multi-user applications which can be implemented with no sharing of resources between different nodes of the cluster. What these silly people are really thinking of is a "share nothing except for the database" architecture. Of course, sharing the database is the primary problem with scaling a multi-user application—so the claim that this architecture is highly scalable is absurd, and tells you a lot about the kind of applications that these folks spend most of their time working on.

Almost anything we can possibly do to share the database less often is worth doing.

This calls for a cache. Well, not just one cache. A well designed Seam application will feature a rich, multi-layered caching strategy that impacts every layer of the application:

- The database, of course, has its own cache. This is super-important, but can't scale like a cache in the application tier.

- Your ORM solution (Hibernate, or some other JPA implementation) has a second-level cache of data from the database. This is a very powerful capability, but is often misused. In a clustered environment, keeping the data in the cache transactionally consistent across the whole cluster, and with the database, is quite expensive. It makes most sense for data which is shared between many users, and is updated rarely. In traditional stateless architectures, people often try to use the second-level cache for conversational state. This is always bad, and is especially wrong in Seam.

- The Seam conversation context is a cache of conversational state. Components you put into the conversation context can hold and cache state relating to the current user interaction.

- In particular, the Seam-managed persistence context (or an extended EJB container-managed persistence context associated with a conversation-scoped stateful session bean) acts as a cache of data that has been read in the current conversation. This cache tends to have a pretty high hitrate! Seam optimizes the replication of Seam-managed persistence contexts in a clustered environment, and there is no requirement for transactional consistency with the database (optimistic locking is sufficient) so you don't need to worry too much about the performance implications of this cache, unless you read thousands of objects into a single persistence context.

- The application can cache non-transactional state in the Seam application context. State kept in the application context is of course not visible to other nodes in the cluster.

- The application can cache transactional state using the Seam `pojoCache` component, which integrates JBossCache into the Seam environment. This state will be visible to other nodes if you run JBoss cache in a clustered mode.

- Finally, Seam lets you cache rendered fragments of a JSF page. Unlike the ORM second-level cache, this cache is not automatically invalidated when data changes, so you need to write application code to perform explicit invalidation, or set appropriate expiration policies.

For more information about the second-level cache, you'll need to refer to the documentation of your ORM solution, since this is an extremely complex topic. In this section we'll discuss the use of JBossCache directly, via the `pojoCache` component, or as the page fragment cache, via the `<s:cache>` control.

## 21.1. Using JBossCache in Seam

The built-in `pojoCache` component manages an instance of `org.jboss.cache.aop.PojoCache`. You can safely put any immutable Java object in the cache, and it will be replicated across the cluster (assuming that replication is enabled). If you want to keep mutable objects in the cache, you'll need to run the JBossCache bytecode preprocessor to ensure that changes to the objects will be automatically detected and replicated.

To use `pojoCache`, all you need to do is put the JBossCache jars in the classpath, and provide a resource named `treecache.xml` with an appropriate cache configuration. JBossCache has many scary and confusing configuration settings, so we won't discuss them here. Please refer to the JBossCache documentation for more information.

You can find a sample `treecache.xml` in `examples/blog/resources/treecache.xml`.

For an EAR depoyment of Seam, we recommend that the JBossCache jars and configuration go directly into the EAR. Make sure you place both `jboss-cache.jar` and `jgroups.jar` in your EAR's lib folder.

Now you can inject the cache into any Seam component:

    @Name("chatroom")

    public class Chatroom {

        @In PojoCache pojoCache;

        public void join(String username) {

          try

          {

             Set<String> userList = (Set<String>) pojoCache.get("chatroom", "userList");

             if (userList==null)

             {

                userList = new HashSet<String>();

                pojoCache.put("chatroom", "userList", userList);

             }

             userList.put(username);

          }

          catch (CacheException ce)

          {

             throw new RuntimeException(ce);

          }

        }

    }

If you want to have multiple JBossCache configurations in your application, use `components.xml`:

    <core:pojo-cache name="myCache" cfg-resource-name="myown/cache.xml"/>

## 21.2. Page fragment caching

The most interesting user of JBossCache is the `<s:cache>` tag, Seam's solution to the problem of page fragment caching in JSF. `<s:cache>` uses `pojoCache` internally, so you need to follow the steps listed above before you can use it. (Put the jars in the EAR, wade through the scary configuration options, etc.)

`<s:cache>` is used for caching some rendered content which changes rarely. For example, the welcome page of our blog displays the recent blog entries:

    <s:cache key="recentEntries-#{blog.id}" region="welcomePageFragments">

       <h:dataTable value="#{blog.recentEntries}" var="blogEntry">

          <h:column>

             <h3>#{blogEntry.title}</h3>

             <div>

                <s:formattedText value="#{blogEntry.body}"/>

             </div>

          </h:column>

       </h:dataTable>

    </s:cache>

The `key` let's you have multiple cached versions of each page fragment. In this case, there is one cached version per blog. The `region` determines the JBossCache node that all version will be stored in. Different nodes may have different expiry policies. (That's the stuff you set up using the aforementioned scary configuration options.)

Of course, the big problem with `<s:cache>` is that it is too stupid to know when the underlying data changes (for example, when the blogger posts a new entry). So you need to evict the cached fragment manually:

    public void post() {

        ...

        entityManager.persist(blogEntry);

        pojoCache.remove("welcomePageFragments", "recentEntries-" + blog.getId() );

    }

Alternatively, if it is not critical that changes are immediately visible to the user, you could set a short expiry time on the JbossCache node.

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