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InfoQ Homepage News Deploying a 1 Terabyte Cache using EhCache Server

Deploying a 1 Terabyte Cache using EhCache Server

Greg Luck, of the EhCache team, announced in early August the availability of SOAP and RESTful APIs for caching. As described in the documentation:

Ehcache now comes with a Cache Server, available as a WAR for most web containers, or as a standalone server. The Cache Server has two APIs: RESTful resource oriented, and SOAP. Both support clients in any programming language.

In a follow up post, Greg outlines his thoughts on deployment options for a theoretical 1 terabyte cache:

The largest ehcache single instances run at around 20GB in memory. The largest disk stores run at 100Gb each. Add nodes together, with cache data partitioned across them, to get larger sizes. 50 nodes at 20GB gets you to 1 Terabyte.

The first, and simplest, approach involves setting up several nodes running  ehcache server and have the client determine the server to use based on an object's hashcode:

String[] cacheservers = new String[]{"", "", "", "", "", ""};
Object key = "123231";
int hash = Math.abs(key.hashCode());
int cacheserverIndex = hash % cacheservers.length;
String cacheserver =cacheservers[cacheserverIndex];

To support redundancy, a load balancer is introduced, and each node runs two ehcache server instances, with replication between them enabled using the existing distributed caching options (RMI or JGroups). In this approach, clients would still determine their servers using the hashcode, but now failures are handled transparently behind the virtual IP assigned by the load balancer.

The third option Greg describes involves moving the responsibility for routing requests to the load balancer.

The RESTful version of the EhCache Server is based on Jersey - the JSR 311 reference implementation. Paul Sandoz, one of the Jersey developers, discussed how the client API of jersey could be used to access the cache for creating and retrieving a sample XML document:

// retrieving a node
Node n = r.accept("application/xml").get(DOMSource.class).getNode();
// creating a node
String xmlDocument = "...";
Client c = Client.create();
WebResource r = c.resource(http://localhost:8080/ehcache/rest/sampleCache2/2);

So, in what scenarios would a RESTful cache be useful? James Webster reports on seeing an increase in adoption of this architectural style in large enterprises:

An architectural pattern that I have observed a few investment banks implement is a distributed memory cache accessed via a RESTful front-end over HTTP for providing access to market data (e.g.. stock prices, interest rate curves, or derived values like volatility surfaces & correlations) and static data (e.g. counterparty details, settlement defaults). The distributed cache can be ‘easily’ scaled to hold massive data sets and the front-end allows the data to be accessed in a technology agnostic fashion, as long as the client can speak HTTP.

As James points out, it will be interesting to see how long it will take commercial vendors (such as Oracle and Gigaspaces) to support RESTful interfaces in their products.

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