Using EhCache2 for caching in Java API development

With the continuous development of applications, caching has become an important part of ensuring system performance stability. In the development of Java applications, using EhCache2 for caching has become a common practice. This article will introduce the basic concepts and usage of EhCache2, and use sample code to demonstrate how to use EhCache2 for caching processing in Java API development.

What is EhCache2?

EhCache2 is an open source Java caching framework that can effectively improve the performance of applications and reduce the pressure on back-end databases. EhCache2 can be used to cache various types of data, such as objects, data records, files, etc. It not only supports memory caching, but also writes cached data to disk for persistence. In addition, EhCache2 also provides many advanced features, such as distributed caching, cache warm-up, cache expiration processing, etc.

Using EhCache2 for caching

It is very simple to use EhCache2 for caching in Java API development. First, you need to add the dependency of EhCache2 to the project:

<dependency>
    <groupId>org.ehcache</groupId>
    <artifactId>ehcache</artifactId>
    <version>3.8.1</version>
</dependency>

Next, create the EhCache configuration file ehcache.xml. This file should be located under the project's classpath and has the following content:

<ehcache xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:noNamespaceSchemaLocation="http://www.ehcache.org/ehcache.xsd"
         updateCheck="false">
 
    <cache name="myCache"
           maxEntriesLocalHeap="10000"
           eternal="false"
           timeToIdleSeconds="120"
           timeToLiveSeconds="120"
           diskSpoolBufferSizeMB="30"
           diskExpiryThreadIntervalSeconds="120"
           memoryStoreEvictionPolicy="LRU"
           statistics="true">
        <persistence strategy="localTempSwap"/>
    </cache>
 
</ehcache>

In this configuration file, we define a cache named "myCache", which has the following characteristics:

• The maximum number of cached elements in the heap is 10000
• If the element has not been accessed within 120 seconds, it will be automatically removed
• If the element has not been updated within 120 seconds, it will be automatically removed
• The cache size when elements are written to disk is 30 MB
• The disk cleaning thread runs every 120 seconds
• The memory cleaning policy is LRU
• Enable cache statistics

Next, use EhCache2 for caching processing in Java code. The sample code is as follows:

public class MyService {
 
    private final Cache myCache;
 
    public MyService() {
        final CacheManager cacheManager = CacheManagerBuilder.newCacheManagerBuilder()
                .withCache("myCache",
                        CacheConfigurationBuilder.newCacheConfigurationBuilder()
                                .withExpiry(ExpiryPolicyBuilder.timeToLiveExpiration(Duration.ofSeconds(120)))
                                .withSize(10, EntryUnit.THOUSAND)
                                .build())
                .build(true);
 
        myCache = cacheManager.getCache("myCache", String.class, String.class);
    }
 
    public String getValue(String key) {
        String value = myCache.get(key);
        if (value == null) {
            value = fetchValueFromDatabase(key);
            myCache.put(key, value);
        }
        return value;
    }
 
    private String fetchValueFromDatabase(String key) {
        // 从数据库中获取值的逻辑
    }
 
}

In this sample code, we create a cache instance named "myCache". When we need to get the value corresponding to key, we first try to get it from the cache. If the value does not exist in the cache, it is obtained from the database and written to the cache.

Summary

This article briefly introduces the basic concepts and usage of EhCache2, and demonstrates through sample code how to use EhCache2 for caching processing in Java API development. In actual development, using EhCache2 for caching can effectively improve the performance and reliability of applications and greatly alleviate the load pressure on the back-end database.

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