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More on visitors and commands in the next section. Interceptor implementations are chained together in the InterceptorChain class, which dispatches a command across the chain of interceptors.
A special interceptor, the CallInterceptor , always sits at the end of this chain to invoke the command being passed up the chain by calling the command's process method. JBoss Cache ships with several interceptors, representing different behavioral aspects, some of which are:.
The interceptor chain configured for your cache instance can be obtained and inspected by calling CacheSPI. Custom interceptors to add specific aspects or features can be written by extending CommandInterceptor and overriding the relevant visitXXX methods based on the commands you are interested in intercepting.
Please see their respective javadocs for details on the extra features provided. The custom interceptor will need to be added to the interceptor chain by using the Cache. See the javadocs on these methods for details. Whenever a method is called on the cache interface, the CacheInvocationDelegate , which implements the Cache interface, creates an instance of VisitableCommand and dispatches this command up a chain of interceptors. Interceptors, which implement the Visitor interface, are able to handle VisitableCommand s they are interested in, and add behavior to the command.
Each command contains all knowledge of the command being executed such as parameters used and processing behavior, encapsulated in a process method. For example, the RemoveNodeCommand is created and passed up the interceptor chain when Cache. In addition to being visitable, commands are also replicable. The JBoss Cache marshallers know how to efficiently marshall commands and invoke them on remote cache instances using an internal RPC mechanism based on JGroups.
InvocationContext holds intermediate state for the duration of a single invocation, and is set up and destroyed by the InvocationContextInterceptor which sits at the start of the interceptor chain.
InvocationContext , as its name implies, holds contextual information associated with a single cache method invocation. Contextual information includes associated javax. GlobalTransaction , method invocation origin InvocationContext. The InvocationContext can be obtained by calling Cache. Some aspects and functionality is shared by more than a single interceptor. Some of these have been encapsulated into managers, for use by various interceptors, and are made available by the CacheSPI interface.
This class is responsible for calls made via the JGroups channel for all RPC calls to remote caches, and encapsulates the JGroups channel used. This class manages buddy groups and invokes group organization remote calls to organize a cluster of caches into smaller sub-groups.
Sets up and configures cache loaders. Early versions of JBoss Cache simply wrote cached data to the network by writing to an ObjectOutputStream during replication. Over various releases in the JBoss Cache 1. In the JBoss Cache 2. The Marshaller interface extends RpcDispatcher. This interface has two main implementations - a delegating VersionAwareMarshaller and a concrete CacheMarshaller The marshaller can be obtained by calling CacheSPI.
Users may also write their own marshallers by implementing the Marshaller interface or extending the AbstractMarshaller class, and adding it to their configuration by using the Configuration. As the name suggests, this marshaller adds a version short to the start of any stream when writing, enabling similar VersionAwareMarshaller instances to read the version short and know which specific marshaller implementation to delegate the call to.
Using a VersionAwareMarshaller helps achieve wire protocol compatibility between minor releases but still affords us the flexibility to tweak and improve the wire protocol between minor or micro releases. When used to cluster state of application servers, applications deployed in the application tend to put instances of objects specific to their application in the cache or in an HttpSession object which would require replication. It is common for application servers to assign separate ClassLoader instances to each application deployed, but have JBoss Cache libraries referenced by the application server's ClassLoader.
To enable us to successfully marshall and unmarshall objects from such class loaders, we use a concept called regions. A region is a portion of the cache which share a common class loader a region also has other uses - see eviction policies. A region is created by using the Cache. By default, regions are active unless the InactiveOnStartup configuration attribute is set to true. JBoss Cache can be configured to be either local standalone or clustered.
If in a cluster, the cache can be configured to replicate changes, or to invalidate changes. A detailed discussion on this follows.
Local caches don't join a cluster and don't communicate with other caches in a cluster. The dependency on the JGroups library is still there, although a JGroups channel is not started. Replicated caches replicate all changes to some or all of the other cache instances in the cluster. Replication can either happen after each modification no transactions or batches , or at the end of a transaction or batch. Replication can be synchronous or asynchronous.
Use of either one of the options is application dependent. Synchronous replication blocks the caller e. Asynchronous replication performs replication in the background the put returns immediately. JBoss Cache also offers a replication queue, where modifications are replicated periodically i.
A replication queue can therefore offer much higher performance as the actual replication is performed by a background thread. Asynchronous replication is faster no caller blocking , because synchronous replication requires acknowledgments from all nodes in a cluster that they received and applied the modification successfully round-trip time.
However, when a synchronous replication returns successfully, the caller knows for sure that all modifications have been applied to all cache instances, whereas this is not be the case with asynchronous replication. With asynchronous replication, errors are simply written to a log. Even when using transactions, a transaction may succeed but replication may not succeed on all cache instances.
When using transactions, replication only occurs at the transaction boundary - i. This results in minimizing replication traffic since a single modification is broadcast rather than a series of individual modifications, and can be a lot more efficient than not using transactions.
Another effect of this is that if a transaction were to roll back, nothing is broadcast across a cluster. Depending on whether you are running your cluster in asynchronous or synchronous mode, JBoss Cache will use either a single phase or two phase commit protocol, respectively.
All modifications are replicated in a single call, which instructs remote caches to apply the changes to their local in-memory state and commit locally. Upon committing your transaction, JBoss Cache broadcasts a prepare call, which carries all modifications relevant to the transaction.
Remote caches then acquire local locks on their in-memory state and apply the modifications. Once all remote caches respond to the prepare call, the originator of the transaction broadcasts a commit.
This instructs all remote caches to commit their data. If any of the caches fail to respond to the prepare phase, the originator broadcasts a rollback. Note that although the prepare phase is synchronous, the commit and rollback phases are asynchronous. This is because Sun's JTA specification does not specify how transactional resources should deal with failures at this stage of a transaction; and other resources participating in the transaction may have indeterminate state anyway.
As such, we do away with the overhead of synchronous communication for this phase of the transaction. That said, they can be forced to be synchronous using the SyncCommitPhase and SyncRollbackPhase configuration attributes.
Buddy Replication allows you to suppress replicating your data to all instances in a cluster. Instead, each instance picks one or more 'buddies' in the cluster, and only replicates to these specific buddies. This greatly helps scalability as there is no longer a memory and network traffic impact every time another instance is added to a cluster.
One of the most common use cases of Buddy Replication is when a replicated cache is used by a servlet container to store HTTP session data. One of the pre-requisites to buddy replication working well and being a real benefit is the use of session affinity , more casually known as sticky sessions in HTTP session replication speak. What this means is that if certain data is frequently accessed, it is desirable that this is always accessed on one instance rather than in a round-robin fashion as this helps the cache cluster optimize how it chooses buddies, where it stores data, and minimizes replication traffic.
If this is not possible, Buddy Replication may prove to be more of an overhead than a benefit. Buddy Replication uses an instance of a BuddyLocator which contains the logic used to select buddies in a network.
JBoss Cache currently ships with a single implementation, NextMemberBuddyLocator , which is used as a default if no implementation is provided. The NextMemberBuddyLocator selects the next member in the cluster, as the name suggests, and guarantees an even spread of buddies for each instance. The NextMemberBuddyLocator takes in 2 parameters, both optional.
Also known as replication groups , a buddy pool is an optional construct where each instance in a cluster may be configured with a buddy pool name. Think of this as an 'exclusive club membership' where when selecting buddies, BuddyLocator s that support buddy pools would try and select buddies sharing the same buddy pool name. This allows system administrators a degree of flexibility and control over how buddies are selected. For example, a sysadmin may put two instances on two separate physical servers that may be on two separate physical racks in the same buddy pool.
So rather than picking an instance on a different host on the same rack, BuddyLocator s would rather pick the instance in the same buddy pool, on a separate rack which may add a degree of redundancy. In the unfortunate event of an instance crashing, it is assumed that the client connecting to the cache directly or indirectly, via some other service such as HTTP session replication is able to redirect the request to any other random cache instance in the cluster.
This is where a concept of Data Gravitation comes in. Data Gravitation is a concept where if a request is made on a cache in the cluster and the cache does not contain this information, it asks other instances in the cluster for the data. In other words, data is lazily transferred, migrating only when other nodes ask for it. This strategy prevents a network storm effect where lots of data is pushed around healthy nodes because only one or a few of them die.
If the data is not found in the primary section of some node, it would optionally ask other instances to check in the backup data they store for other caches. This means that even if a cache containing your session dies, other instances will still be able to access this data by asking the cluster to search through their backups for this data.
Once located, this data is transferred to the instance which requested it and is added to this instance's data tree. The data is then optionally removed from all other instances and backups so that if session affinity is used, the affinity should now be to this new cache instance which has just taken ownership of this data. Data Gravitation is implemented as an interceptor. The following all optional configuration properties pertain to data gravitation. If a cache is configured for invalidation rather than replication, every time data is changed in a cache other caches in the cluster receive a message informing them that their data is now stale and should be evicted from memory.
Invalidation, when used with a shared cache loader see chapter on cache loaders would cause remote caches to refer to the shared cache loader to retrieve modified data. The benefit of this is twofold: Invalidation messages are sent after each modification no transactions or batches , or at the end of a transaction or batch, upon successful commit. This is usually more efficient as invalidation messages can be optimized for the transaction as a whole rather than on a per-modification basis.
Invalidation too can be synchronous or asynchronous, and just as in the case of replication, synchronous invalidation blocks until all caches in the cluster receive invalidation messages and have evicted stale data while asynchronous invalidation works in a 'fire-and-forget' mode, where invalidation messages are broadcast but doesn't block and wait for responses.
State Transfer refers to the process by which a JBoss Cache instance prepares itself to begin providing a service by acquiring the current state from another cache instance and integrating that state into its own state. There are three divisions of state transfer types depending on a point of view related to state transfer. First, in the context of particular state transfer implementation, the underlying plumbing, there are two starkly different state transfer types: Second, state transfer can be full or partial state transfer depending on a subtree being transferred.
Entire cache tree transfer represents full transfer while transfer of a particular subtree represents partial state transfer. And finally state transfer can be "in-memory" and "persistent" transfer depending on a particular use of cache. Byte array based transfer was a default and only transfer methodology for cache in all previous releases up to 2.
Byte array based transfer loads entire state transferred into a byte array and sends it to a state receiving member. Streaming state transfer provides an InputStream to a state reader and an OutputStream to a state writer. OutputStream and InputStream abstractions enable state transfer in byte chunks thus resulting in smaller memory requirements. For example, if application state is represented as a tree whose aggregate size is 1GB, rather than having to provide a 1GB byte array streaming state transfer transfers the state in chunks of N bytes where N is user configurable.
Byte array and streaming based state transfer are completely API transparent, interchangeable, and statically configured through a standard cache configuration XML file. Refer to JGroups documentation on how to change from one type of transfer to another.
If either in-memory or persistent state transfer is enabled, a full or partial state transfer will be done at various times, depending on how the cache is used. A "partial" state transfer is one where just a portion of the tree is transferred -- i. If either in-memory or persistent state transfer is enabled, state transfer will occur at the following times:. This occurs when the cache is first started as part of the processing of the start method. This is a full state transfer. The state is retrieved from the cache instance that has been operational the longest.
The cache's InactiveOnStartup property is true. This property is used in conjunction with region-based marshalling. Buddy replication is used. See below for more on state transfer with buddy replication. Partial state transfer following region activation.
When region-based marshalling is used, the application needs to register a specific class loader with the cache. This class loader is used to unmarshall the state for a specific region subtree of the cache.
After registration, the application calls cache. The request is first made to the oldest cache instance in the cluster. However, if that instance responds with no state, it is then requested from each instance in turn until one either provides state or all instances have been queried. Typically when region-based marshalling is used, the cache's InactiveOnStartup property is set to true. This suppresses initial state transfer, which would fail due to the inability to deserialize the transferred state.
When buddy replication is used, initial state transfer is disabled. Instead, when a cache instance joins the cluster, it becomes the buddy of one or more other instances, and one or more other instances become its buddy. Each time an instance determines it has a new buddy providing backup for it, it pushes its current state to the new buddy.
This "pushing" of state to the new buddy is slightly different from other forms of state transfer, which are based on a "pull" approach i. However, the process of preparing and integrating the state is the same. This "push" of state upon buddy group formation only occurs if the InactiveOnStartup property is set to false.
If it is true , state transfer amongst the buddies only occurs when the application activates the region on the various members of the group. Partial state transfer following a region activation call is slightly different in the buddy replication case as well. Instead of requesting the partial state from one cache instance, and trying all instances until one responds, with buddy replication the instance that is activating a region will request partial state from each instance for which it is serving as a backup.
This consists of the actual in-memory state of another cache instance - the contents of the various in-memory nodes in the cache that is providing state are serialized and transferred; the recipient deserializes the data, creates corresponding nodes in its own in-memory tree, and populates them with the transferred data.
Only applicable if a non-shared cache loader is used. The state stored in the state-provider cache's persistent store is deserialized and transferred; the recipient passes the data to its own cache loader, which persists it to the recipient's persistent store. If multiple cache loaders are configured in a chain, only one can have this property set to true; otherwise you will get an exception at startup.
Persistent state transfer with a shared cache loader does not make sense, as the same persistent store that provides the data will just end up receiving it. Therefore, if a shared cache loader is used, the cache will not allow a persistent state transfer even if a cache loader has fetchPersistentState set to true.
Which of these types of state transfer is appropriate depends on the usage of the cache. If a write-through cache loader is used, the current cache state is fully represented by the persistent state. Data may have been evicted from the in-memory state, but it will still be in the persistent store. In this case, if the cache loader is not shared, persistent state transfer is used to ensure the new cache has the correct state.
In-memory state can be transferred as well if the desire is to have a "hot" cache -- one that has all relevant data in memory when the cache begins providing service. This approach somewhat reduces the burden on the cache instance providing state, but increases the load on the persistent store on the recipient side.
If a cache loader is used with passivation, the full representation of the state can only be obtained by combining the in-memory i. Therefore an in-memory state transfer is necessary. A persistent state transfer is necessary if the cache loader is not shared. If no cache loader is used and the cache is solely a write-aside cache i. New in JBoss Cache 3. This is particularly important if there is a large volume of state, where generation and streaming of the state can take some time and can cause ongoing transactions on the sender to time out and fail.
To ensure state transfer behaves as expected, it is important that all nodes in the cluster are configured with the same settings for persistent and transient state. This is because byte array based transfers, when requested, rely only on the requester's configuration while stream based transfers rely on both the requester and sender's configuration, and this is expected to be identical.
JBoss Cache can use a CacheLoader to back up the in-memory cache to a backend datastore. If JBoss Cache is configured with a cache loader, then the following features are provided:. When CacheLoaderConfiguration see below is non-null, an instance of each configured CacheLoader is created when the cache is created, and started when the cache is started. Correspondingly, stop and destroy are called when the cache is stopped. Next, setConfig and setCache are called. The latter can be used to store a reference to the cache, the former is used to configure this instance of the CacheLoader.
For example, here a database cache loader could establish a connection to the database. The CacheLoader interface has a set of methods that are called when no transactions are used: These methods are described as javadoc comments in the interface. Then there are three methods that are used with transactions: The prepare method is called when a transaction is to be committed. It has a transaction object and a list of modfications as argument. The transaction object can be used as a key into a hashmap of transactions, where the values are the lists of modifications.
Each modification list has a number of Modification elements, which represent the changes made to a cache for a given transaction. When prepare returns successfully, then the cache loader must be able to commit or rollback the transaction successfully.
JBoss Cache takes care of calling prepare , commit and rollback on the cache loaders at the right time. The commit method tells the cache loader to commit the transaction, and the rollback method tells the cache loader to discard the changes associated with that transaction. See the javadocs on this interface for a detailed explanation on each method and the contract implementations would need to fulfill.
Note that you can define several cache loaders, in a chain. The impact is that the cache will look at all of the cache loaders in the order they've been configured, until it finds a valid, non-null element of data.
When performing writes, all cache loaders are written to except if the ignoreModifications element has been set to true for a specific cache loader.
See the configuration section below for details. The class element defines the class of the cache loader implementation. Note that an implementation of cache loader has to have an empty constructor.
The properties element defines a configuration specific to the given implementation. The filesystem-based implementation for example defines the root directory to be used, whereas a database implementation might define the database URL, name and password to establish a database connection. This configuration is passed to the cache loader implementation via CacheLoader.
Note that backspaces may have to be escaped. Anything else is loaded lazily when accessed. Preloading makes sense when one anticipates using elements under a given subtree frequently. Only one configured cache loader may set this property to true; if more than one cache loader does so, a configuration exception will be thrown when starting your cache service.
If this is set to true, an instance of org. AsyncCacheLoader is constructed with an instance of the actual cache loader to be used. The AsyncCacheLoader then delegates all requests to the underlying cache loader, using a separate thread if necessary. See the Javadocs on AsyncCacheLoader for more details. If unspecified, the async element defaults to false. Note on using the async element: This needs to be kept in mind when setting the async element to true.
Situations may arise where transient application data should only reside in a file based cache loader on the same server as the in-memory cache, for example, with a further shared JDBCCacheLoader used by all servers in the network. This property defaults to false , so writes are propagated to all cache loaders configured.
Setting this to true prevents repeated and unnecessary writes of the same data to the cache loader by different cache instances. Default value is false. Essentially, whenever any data comes in to some node it is always replicated so as to keep the caches' in-memory states in sync; the coordinator, though, has the sole responsibility of pushing that state to disk. This functionality can be activated setting the enabled subelement to true in all nodes, but again only the coordinator of the cluster will store the modifications in the underlying cache loader as defined in loader element.
You cannot define a cache loader as shared and with singletonStore enabled at the same time. Default value for enabled is false. Optionally, within the singletonStore element, you can define a class element that specifies the implementation class that provides the singleton store functionality. This class must extend org. AbstractDelegatingCacheLoader , and if absent, it defaults to org. The properties subelement defines properties that allow changing the behavior of the class providing the singleton store functionality.
By default, pushStateWhenCoordinator and pushStateWhenCoordinatorTimeout properties have been defined, but more could be added as required by the user-defined class providing singleton store functionality.
This can be very useful in situations where the coordinator crashes and there's a gap in time until the new coordinator is elected. During this time, if this property was set to false and the cache was updated, these changes would never be persisted.
Setting this property to true would ensure that any changes during this process also get stored in the cache loader. You would also want to set this property to true if each node's cache loader is configured with a different location. Default value is true. Default value is Note on using the singletonStore element: If a node is to be passivated as a result of an eviction, while the cluster is in the process of electing a new coordinator, the data will be lost.
This is because no coordinator is active at that time and therefore, none of the nodes in the cluster will store the passivated node. A new coordinator is elected in the cluster when either, the coordinator leaves the cluster, the coordinator crashes or stops responding. JBoss Cache ships with several cache loaders that utilize the file system as a data store. FileCacheLoader , which is a simple filesystem-based implementation. By default, this cache loader checks for any potential character portability issues in the location or tree node names, for example invalid characters, producing warning messages.
These checks can be disabled adding check. The FileCacheLoader has some severe limitations which restrict its use in a production environment, or if used in such an environment, it should be used with due care and sufficient understanding of these limitations. As a rule of thumb, it is recommended that the FileCacheLoader not be used in a highly concurrent, transactional or stressful environment, and its use is restricted to testing.
Note that the BerkeleyDB implementation is much more efficient than the filesystem-based implementation, and provides transactional guarantees, but requires a commercial license if distributed with an application see http: ClusteredCacheLoader , which allows querying of other caches in the same cluster for in-memory data via the same clustering protocols used to replicate data.
Writes are not 'stored' though, as replication would take care of any updates needed. You need to specify a property called timeout , a long value telling the cache loader how many milliseconds to wait for responses from the cluster before assuming a null value.
The implementing class is org. The current implementation uses just one table. Each row in the table represents one node and contains three columns:. Fqn s are stored as strings. Node content is stored as a BLOB.
JBoss Cache does not impose any limitations on the types of objects used in Fqn but this implementation of cache loader requires Fqn to contain only objects of type java.
Another limitation for Fqn is its length. Since Fqn is a primary key, its default column type is VARCHAR which can store text values up to some maximum length determined by the database in use.
See this wiki page for configuration tips with specific database systems. Table and column names as well as column types are configurable with the following properties. If you are using JBossCache in a managed environment e.
JBoss Cache implements JDBC connection pooling when running outside of an application server standalone using the c3p0: In order to enable it, just edit the following property:. You can also set any c3p0 parameters in the same cache loader properties section but don't forget to start the property name with 'c3p0.
To find a list of available properties, please check the c3p0 documentation for the c3p0 library version distributed in c3p0: Also, in order to provide quick and easy way to try out different pooling parameters, any of these properties can be set via a System property overriding any values these properties might have in the JBoss Cache XML configuration file, for example: If a c3p0 property is not defined in either the configuration file or as a System property, default value, as indicated in the c3p0 documentation, will apply.
The CacheLoaderConfiguration XML element contains an arbitrary set of properties which define the database-related configuration. As an alternative to configuring the entire JDBC connection, the name of an existing data source can be given:. Since Amazon S3 is remote network storage and has fairly high latency, it is really best for caches that store large pieces of data, such as media or files.
But consider this cache loader over the JDBC or file system based cache loaders if you want remotely managed, highly reliable storage. JBoss Cache itself provides in-memory caching for your data to minimize the amount of remote access calls, thus reducing the latency and cost of fetching your Amazon S3 data.
With cache replication, you are also able to load data from your local cluster without having to remotely access it every time. Note that Amazon S3 does not support transactions. If transactions are used in your application then there is some possibility of state inconsistency when using this cache loader.
However, writes are atomic, in that if a write fails nothing is considered written and data is never corrupted. Data is stored in keys based on the Fqn of the Node and Node data is serialized as a java. Map using the CacheSPI.
Read the javadoc on how data is structured and stored. Data is stored using Java serialization. Your feedback and help would be appreciated to extend this cache loader for that purpose.
With this cache loader, single-key operations such as Node. Use bulk operations such as Node. The S3 cache loader is provided with the default distribution but requires a library to access the service at runtime. This runtime library may be obtained through a Sourceforge Maven Repository.
Include the following sections in your pom. At a minimum, you must configure your Amazon S3 access key and secret access key. The following configuration keys are listed in general order of utility. JBoss Cache stores nodes in a tree format and automatically creates intermediate parent nodes as necessary. The S3 cache loader must also create these parent nodes as well to allow for operations such as getChildrenNames to work properly. Checking if all parent nodes exists for every put operation is fairly expensive, so by default the cache loader caches the existence of these parent nodes.
This cache loader allows to delegate loads and stores to another instance of JBoss Cache, which could reside a in the same address space, b in a different process on the same host, or c in a different process on a different host.
A TcpDelegatingCacheLoader talks to a remote org. The TcpCacheServer has a reference to another JBoss Cache instance, which it can create itself, or which is given to it e.
As of JBoss Cache 2. In addition, 2 new optional parameters are used to control transparent reconnecting to the TcpCacheServer. The timeout property defaults to specifies the length of time the cache loader must continue retrying to connect to the TcpCacheServer before giving up and throwing an exception. The reconnectWaitTime defaults to is how long the cache loader should wait before attempting a reconnect if it detects a communication failure.
The last two parameters can be used to add a level of fault tolerance to the cache loader, do deal with TcpCacheServer restarts. A typical use case could be multiple replicated instances of JBoss Cache in the same cluster, all delegating to the same TcpCacheServer instance.
If the nodes went directly to the database, then we'd have the same SQL executed multiple times. So TcpCacheServer serves as a natural cache in front of the DB assuming that a network round trip is faster than a DB access which usually also include a network round trip. To alleviate single point of failure, we could configure several cache loaders.
Such change is trivial for replication purposes as it just requires the rest of the nodes to understand this format. However, changing the format of the data in cache stores brings up a new problem: With this in mind, JBoss Cache 2. These are one-off cache loaders that read data from the cache store in JBoss Cache 1.
The idea is for users to modify their existing cache configuration file s momentarily to use these cache loaders and for them to create a small Java application that creates an instance of this cache, recursively reads the entire cache and writes the data read back into the cache. Once the data is transformed, users can revert back to their original cache configuration file s.
This example, called examples. TransformStore , is independent of the actual data stored in the cache as it writes back whatever it was read recursively. It is highly recommended that anyone interested in porting their data run this example first, which contains a readme. A cache loader can be used to enforce node passivation and activation on eviction in a cache. Cache Passivation is the process of removing an object from in-memory cache and writing it to a secondary data store e.
Cache Activation is the process of restoring an object from the data store into the in-memory cache when it's needed to be used. In both cases, the configured cache loader will be used to read from the data store and write to the data store. When an eviction policy in effect evicts a node from the cache, if passivation is enabled, a notification that the node is being passivated will be emitted to the cache listeners and the node and its children will be stored in the cache loader store.
When a user attempts to retrieve a node that was evicted earlier, the node is loaded lazy loaded from the cache loader store into memory. When the node and its children have been loaded, they're removed from the cache loader and a notification is emitted to the cache listeners that the node has been activated. The default is false. When passivation is used, only the first cache loader configured is used and all others are ignored.
When passivation is disabled, whenever an element is modified, added or removed, then that modification is persisted in the backend store via the cache loader. There is no direct relationship between eviction and cache loading.
If you don't use eviction, what's in the persistent store is basically a copy of what's in memory. If you do use eviction, what's in the persistent store is basically a superset of what's in memory i. When passivation is enabled, there is a direct relationship between eviction and the cache loader.
Writes to the persistent store via the cache loader only occur as part of the eviction process. Data is deleted from the persistent store when the application reads it back into memory. In this case, what's in memory and what's in the persistent store are two subsets of the total information set, with no intersection between the subsets.
Following is a simple example, showing what state is in RAM and in the persistent store after each step of a 6 step process:. This section discusses different patterns of combining different cache loader types and configuration options to achieve specific outcomes.
This is the simplest case. The cache loader simply loads non-existing elements from the store and stores modifications back to the store. When the cache is started, depending on the preload element, certain data can be preloaded, so that the cache is partly warmed up. The following figure shows 2 JBoss Cache instances sharing the same backend store:.
Both nodes have a cache loader that accesses a common shared backend store. This could for example be a shared filesystem using the FileCacheLoader , or a shared database. Because both nodes access the same store, they don't necessarily need state transfer on startup.
This would mean that individual caches in a cluster might have different in-memory state at any given time largely depending on their preloading and eviction strategies.
When storing a value, the writer takes care of storing the change in the backend store. For example, if node1 made change C1 and node2 C2, then node1 would tell its cache loader to store C1, and node2 would tell its cache loader to store C2. This is a similar case to the previous one, but here only one node in the cluster interacts with a backend store via its cache loader.
All other nodes perform in-memory replication. The idea here is all application state is kept in memory in each node, with the existence of multiple caches making the data highly available.
This assumes that a client that needs the data is able to somehow fail over from one cache to another. The single persistent backend store then provides a backup copy of the data in case all caches in the cluster fail or need to be restarted.
Note that here it may make sense for the cache loader to store changes asynchronously, that is not on the caller's thread, in order not to slow down the cluster by accessing for example a database. This is a non-issue when using asynchronous replication. Format x a schedule hour a pirate alert like you are supposed. Binaryoptions follow binary considered part time auto binary options role.
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Its main competitors during its heyday were ICQ , Yahoo! Messenger and MSN Messenger. AOL particularly had a rivalry or 'chat war' with rival Microsoft starting in There were several attempts from Microsoft to simultaneously log into their own and AIM's protocol servers. Around AIM started to lose popularity rapidly, partly due to the quick rise of Gmail and its built-in real-name Google Chat instant messenger integration in and because many people started purely moving onto SMS text messaging and later social networking websites in particular, Facebook Messenger , which was released as a standalone app the same year for instant messaging.
This was a yellow stickman -like figure, often called the "Running Man". The mascot appeared on all AIM logos and most wordmarks, and always appeared at the top of the buddy list. AIM's popularity in the late s and the s led to the "Running Man" becoming a familiar brand on the Internet. After over 14 years, the iconic logo finally disappeared as part of the AIM rebranding in However, in August , the "Running Man" once again returned. In , a Complex editor called it a "symbol of America".
However, in March , this service was discontinued. For privacy regulations, AIM has strict age restrictions. AIM accounts are available only for people over the age of 13; children younger than that were not permitted access to AIM.
The profile of the user has no privacy. If public content is accessed. This is outlined in the policy and terms of service: This allows anything one posts to be used without a separate request for permission. The issue of AIM's security had been called into question.
AOL stated that it had taken great steps to insure that personal information will not be accessed by unauthorized members, but that it cannot guarantee that that will not happen. AIM is different from other clients, such as Yahoo! Messenger , in that it does not require approval from one buddy to be added to another's buddy list.
As a result, it was possible for users to keep other unsuspecting users on their buddy list to see when they are online, read their status and away messages, and read their profiles. A more complete privacy option was to select a menu option allowing communication only with those on one's buddy list; this caused blocking thus appearing offline to all users who were not on one's buddy list. AOL and various other companies supplied robots on AIM that could receive messages and send a response based on the bot's purpose.
For example, bots could help with studying, like StudyBuddy. Some were made to relate to children and teenagers, like Spleak , others gave advice, and others were for more general purposes, such as SmarterChild.
The more useful chat bots had features like the ability to play games, get sport scores, weather forecasts or financial stock information. Users were able to talk to automated chat bots that could respond to natural human language.
They were primarily put into place as a marketing strategy and for unique advertising options. It was used by advertisers to market products or build better consumer relations.