The Coherence architecture is modular, allowing almost any piece to be extended or even replaced with a custom implementation. One of the responsibilities of the Coherence system that is completely configurable, extendable and replaceable is local storage. Local storage refers to the data structures that actually store or cache the data that is managed by Coherence. For an object to provide local storage, it must support the same standard collections interface, java.util.Map. When a local storage implementation is used by Coherence to store replicated or distributed data, it is called a backing map, because Coherence is actually backed by that local storage implementation. The other common uses of local storage is in front of a distributed cache and as a backup behind the distributed cache.
Typically, Coherence uses one of the following local storage implementations:
- Safe HashMap: This is the default lossless implementation. A lossless implementation is one, like Java's Hashtable class, that is neither size-limited nor auto-expiring. In other words, it is an implementation that never evicts ("loses") cache items on its own. This particular HashMap implementation is optimized for extremely high thread-level concurrency. (For the default implementation, use class com.tangosol.util.SafeHashMap; when an implementation is required that provides cache events, use com.tangosol.util.ObservableHashMap. These implementations are thread-safe.)
- Local Cache: This is the default size-limiting and/or auto-expiring implementation. The local cache is covered in more detail below, but the primary points to remember about it are that it can limit the size of the cache, and it can automatically expire cache items after a certain period of time. (For the default implementation, use com.tangosol.net.cache.LocalCache; this implementation is thread safe and supports cache events, com.tangosol.net.CacheLoader, CacheStore and configurable/pluggable eviction policies.)
- Read/Write Backing Map: This is the default backing map implementation for caches that load from a database on a cache miss. It can be configured as a read-only cache (consumer model) or as either a write-through or a write-behind cache (for the consumer/producer model). The write-through and write-behind modes are intended only for use with the distributed cache service. If used with a near cache and the near cache must be kept in sync with the distributed cache, it is possible to combine the use of this backing map with a Seppuku-based near cache (for near cache invalidation purposes); however, given these requirements, it is suggested that the versioned implementation be used. (For the default implementation, use class com.tangosol.net.cache.ReadWriteBackingMap.)
- Versioned Backing Map: This is an optimized version of the read/write backing map that optimizes its handling of the data by utilizing a data versioning technique. For example, to invalidate near caches, it simply provides a version change notification, and to determine whether cached data needs to be written back to the database, it can compare the persistent (database) version information with the transient (cached) version information. The versioned implementation can provide very balanced performance in large scale clusters, both for read-intensive and write-intensive data. (For the default implementation, use class com.tangosol.net.cache.VersionedBackingMap; with this backing map, you can optionally use the com.tangosol.net.cache.VersionedNearCache as a near cache implementation.)
- Binary Map (Java NIO): This is a backing map implementation that can store its information in memory but outside of the Java heap, or even in memory-mapped files, which means that it does not affect the Java heap size and the related JVM garbage-collection performance that can be responsible for application pauses. This implementation is also available for distributed cache backups, which is particularly useful for read-mostly and read-only caches that require backup for high availability purposes, because it means that the backup does not affect the Java heap size yet it is immediately available in case of failover.
- Serialization Map: This is a backing map implementation that translates its data to a form that can be stored on disk, referred to as a serialized form. It requires a separate com.tangosol.io.BinaryStore object into which it stores the serialized form of the data; usually, this is the built-in LH disk store implementation, but the Serialization Map supports any custom implementation of BinaryStore. (For the default implementation of Serialization Map, use com.tangosol.net.cache.SerializationMap.)
- Serialization Cache: This is an extension of the SerializationMap that supports an LRU eviction policy. This can be used to limit the size of disk files, for example. (For the default implementation of Serialization Cache, use com.tangosol.net.cache.SerializationCache.)
- Overflow Map: An overflow map doesn't actually provide storage, but it deserves mention in this section because it can tie together two local storage implementations so that when the first one fills up, it will overflow into the second. (For the default implementation of OverflowMap, use com.tangosol.net.cache.OverflowMap.)