h1. Terracotta Distributed Data Cache

h2. Introduction
Clustered applications with a system of record (SOR) on the backend can benefit from a distributed cache that manages certain data in memory while reducing costly application-SOR interactions. However, using a cache can introduce increased complexity to software development, integration, operation, and maintenance.

The Terracotta distributed cache, implemented with the Terracotta Integration Module tim-evictor, takes both established and innovative approaches to the caching model. tim-evictor solves the performance and complexity issues by:

* making cached application data available in-memory across a cluster of application servers;
* offering standard methods for working with cache elements and performing cache-wide operations;
* incorporating concurrency for readers and writers;
* evicting data efficiently based on standard expiration metrics;
* obviating SOR commits for data with a limited lifetime;
* providing a lightweight implementation with a simple API that's easier to understand and code against;
* being "native" to Terracotta to eliminate integration issues;
* utilizing a flexible map implementation to adapt to more applications;
* minimizing inter-node faulting to speed data operations.

Implement using [tim-evictor|forge].

h3. Requirements
The Terracotta distributed data cache requires JDK 1.5 or greater.

h2. Structure and Characteristics
The Terracotta distributed cache is an interface incorporating a distributed map with standard map operations:

public interface DistributedMap<K, V> {
  // Single item operations
  void put(K key, V value);
  V get(K key);
  V remove(K key);
  boolean containsKey(K key);

  // Multi item operations
  int size();
  void clear();
  Set<K> getKeys();

  // For managing the background evictor thread
  void start();
  void shutdown();

Notable characteristics include:

* getValues() is not provided, but an iterator can be obtained for Set<K> to obtain values.
* A cache-wide Time To Live (TTL) value can be set. The TTL determines the maximum amount of time an object can remain in the cache before becoming eligible for eviction, regardless of other conditions such as use.
* A cache-wide Time To Idle (TTI) value can be set. The TTI determines the maximum amount of time an object can remain idle in the cache before becoming eligible for eviction. TTI is reset each time the object is used.
* Each cache element receives an internal timestamp used against the cache-wide TTL and TTI.
* The map containing the timestamps is clustered, allowing all nodes to be aware of expirations and pending evictions.

h2. Usage Example

The following is an example of cache that implements the Terracotta distributed cache:

Cache cache = new CacheBuilder()
	.setTimeToLive(10, TimeUnit.SECONDS)
	.setTimeToIdle(5, TimeUnit.SECONDS)
cache.put("Rabbit", "Carrots");
cache.put("Dog", "Bone");
cache.put("Owl", "Mouse");
// wait 3 seconds

// wait 2 seconds - expire Dog and Owl due to TTI
assert !cache.contains("Dog");
assert !cache.contains("Owl");
assert cache.contains("Rabbit");

// wait 5 seconds - expire Rabbit due to TTL
assert !cache.contains("Rabbit");

h2. Terracotta Distributed Cache in a Reference Application
The [Examinator reference application|orgsite:Exam App Reference] uses the Terracotta distributed cache to handle pending user registrations. This type of data has a "medium-term" lifetime which needs to be persisted long enough to give prospective registrants a chance to verify their registrations. If a registration isn't verified by the time TTL is reached, it can be evicted from the cache. Only if the registration is verified is it written to the database. 

The combination of Terracotta and the Terracotta distributed cache gives Examinator the following advantages:

* The simple Terracotta distributed cache's API makes it easy to integrate with Examinator and to maintain and troubleshoot.
* Medium-term data is not written to the database unnecessarily, improving application performance.
* Terracotta persists the pending registrations so they can survive node failure.
* Terracotta clusters (shares) the pending registration data so that any node can handle validation.