Caching Data with GemFire :: Learn how to cache data in GemFire.
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This guide walks through the process of using GemFire’s data fabric to cache certain calls from your code.

What you’ll build

You’ll build a service that requests quotes from a CloudFoundry hosted Quote service and caches them in GemFire. You’ll then see that fetching the same quote again eliminates the expensive call to the Quote service.

The Quote service is located at…​

The Quote service has the following API…​

GET /api         - get all quotes
GET /api/random  - get random quote
GET /api/{id}    - get specific quote

Create a bindable object for fetching data

Now that you’ve set up the project and build system, you can focus on defining the domain objects necessary to capture the bits you need to pull quotes (the data) from the Quote service.



The Quote domain class has id and quote properties along with standard getters and setters. These are the two primary attributes you will gather further along in this guide.

In addition to Quote, the QuoteResponse captures the entire payload of the Quote service sent in response to a quote request. It includes the status (a.k.a. type) of the request along with the quote.



A typical response from the Quote service appears as follows:

  "value": {
    "quote":"Working with Spring Boot is like pair-programming with the Spring developers."

Both classes are marked with @JsonIgnoreProperties(ignoreUnknown=true). This means even though other JSON attributes may be retrieved, they’ll be ignored.

Query Quote service for data

Your next step is to create a service that queries the Quote service for quotes.



This service uses Spring’s RestTemplate to query the Quote service’s API. The Quote service returns a JSON object, but Spring binds the data to produce a QuoteResponse and ultimately, a Quote object.

The key piece of this service is how requestQuote has been annotated with @Cacheable("Quotes"). Spring’s caching abstraction intercepts the call to `requestQuote`to check whether it’s already been called. If so, Spring’s caching abstraction returns the cached copy. Otherwise, it proceeds to invoke the method, store the response in the cache, and then return the results to the caller.

We also used the @CachePut annotation on the requestRandomQuote service method. Since the quote returned from this service invocation will be random, we don’t know which quote we will receive. Therefore, we cannot consult the cache (i.e. Quotes) prior to the call, but we can cache the result of the call, which will have a positive affect on subsequent requestQuote(id) calls, assuming the quote of interest was randomly selected and cache previously.

The @CachePut includes a SpEL expression ( to access the result of the service method invocation and retrieve the ID of the quote to use as the cache key. You can learn more about Spring’s cache abstraction SpEL context here.

You must supply the name of the cache. We named it "Quotes" for demonstration purposes, but in production, it is recommended to pick an appropriately descriptive name. This also means different methods can be associated with different caches. This is useful if you have different configuration settings for each cache, such as expiration or eviction policies, and so on.

Later on when you run the code, you will see the time it takes to run each call and be able to discern the impact that caching has on service response times. This demonstrates the value of caching certain calls. If your application is constantly looking up the same data, caching the results can improve your performance dramatically.

Make the application executable

Although GemFire caching can be embedded in web apps and WAR files, the simpler approach demonstrated below creates a standalone application. You package everything in a single, executable JAR file, driven by a good old Java main() method.



At the top of the configuration is a single vital annotation: @EnableCaching This turns on caching and adds important beans in the background to support caching with GemFire.

The first bean is an instance of QuoteService.

The next four are needed to connect with GemFire and provide caching.

  • gemfireProperties GemFire System properties used to configure a standalone GemFire data node.

  • gemfireCache creates a GemFire cache bean in the Spring application context.

  • quotesRegion defines a GemFire Region inside the cache to store quotes. It is named "Quotes" and must match your usage of @Cacheable("Quotes").

  • cacheManager supports Spring’s caching abstraction using GemFire as the provider.

Two of these beans are factory beans. This is a common pattern used for objects that need special creation logic. Basically, CacheFactoryBean results in a Cache bean and LocalRegionFactoryBean results in a LocalRegion bean being registered with the context.

The first time a quote is requested (using requestQuote(id)), a cache miss occurs and the service method will be invoked, incurring a noticeable delay, i.e. not close to zero ms. In this case, caching is linked by the input parameters (i.e. id) of the service method, requestQuote. In other words, the id method parameter is the cache key. Subsequent requests for the same quote identified by ID, will result in a cache hit, thereby avoiding the expensive service call.

For demonstration purposes, the call to the QuoteService is wrapped in a separate method (requestQuote inside the Application class) to capture the time to make the service call. This lets you see exactly how long any one request is taking.

Logging output is displayed. The service should be up and running within a few seconds.

"@springboot with @springframework is pure productivity! Who said in #java one has to write double the code than in other langs? #newFavLib"
Cache Miss [true] - Elapsed Time [776 ms]
"@springboot with @springframework is pure productivity! Who said in #java one has to write double the code than in other langs? #newFavLib"
Cache Miss [false] - Elapsed Time [0 ms]
"Really loving Spring Boot, makes stand alone Spring apps easy."
Cache Miss [true] - Elapsed Time [96 ms]

From this you can see that the first call to the Quote service for a quote took 776 ms and resulted in a cache miss. However, the second call requesting the same quote took 0 ms and resulted in a cache hit. This clearly shows that the second call was cached and never actually hit the Quote service. However, when a final service call for a specific, non-cached quote request is made, it took 96 ms and resulted in a cache miss since this new quote was not previously in the cache before the call.


Congratulations! You’ve just built a service that performed an expensive operation and tagged it so that it will cache results.