Using Hazelcast distributed query cache in Java applications

21 Sep 2023

In a distributed system, caching can greatly improve the performance and scalability of your Java applications. One popular caching solution is Hazelcast, which provides a distributed query cache that allows you to cache the results of database queries in a distributed environment.

Here, we’ll explore how to use the Hazelcast distributed query cache in your Java applications.

Step 1: Configure Hazelcast

First, you need to set up Hazelcast in your Java application. Add the Hazelcast dependency to your project’s build file (e.g., Maven or Gradle). Then, create a Hazelcast instance by configuring the necessary properties, such as cluster members and caching options.

import com.hazelcast.config.Config;
import com.hazelcast.config.JoinConfig;
import com.hazelcast.config.NetworkConfig;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;

public class HazelcastConfiguration {
    public static HazelcastInstance createHazelcastInstance() {
        Config config = new Config();
        
        // Set up network configuration
        NetworkConfig networkConfig = config.getNetworkConfig();
        networkConfig.setPort(5701);
        JoinConfig joinConfig = networkConfig.getJoin().getTcpIpConfig();
        joinConfig.setEnabled(true).addMember("localhost").addMember("192.168.0.1");
        
        // Other Hazelcast configuration options
        
        return Hazelcast.newHazelcastInstance(config);
    }
}

Step 2: Configure Distributed Query Cache

Once you have configured Hazelcast, you can enable distributed query caching. You need to define your data structures (e.g., maps) and configure the caches on them. Hazelcast provides a convenient API for doing this.

import com.hazelcast.config.CacheConfig;
import com.hazelcast.config.EvictionPolicy;

public class DistributedQueryCache {
    public static void configureQueryCache() {
        // Get Hazelcast instance
        HazelcastInstance hazelcastInstance = HazelcastConfiguration.createHazelcastInstance();
        
        // Create or retrieve a map from Hazelcast instance
        IMap<Integer, String> map = hazelcastInstance.getMap("myMap");
        
        // Configure cache on the map
        CacheConfig<Integer, String> cacheConfig = new CacheConfig<>();
        cacheConfig.setName("queryCache");
        cacheConfig.setEvictionPolicy(EvictionPolicy.LRU);
        cacheConfig.setStatisticsEnabled(true);
        
        map.addIndex("name", true);
        map.addIndex("age", false);
        map.getQueryCache("myQueryCache", "name = 'John'", true);
        
        // Other cache configuration options
        
        map.put(1, "John");
        map.put(2, "Jane");
        map.put(3, "Emily");
    }
}

Step 3: Use Distributed Query Cache

Now that your distributed query cache is configured, you can use it to cache the results of your database queries.

import com.hazelcast.core.IMap;
import com.hazelcast.query.Predicate;
import com.hazelcast.query.SqlPredicate;

public class Application {
    public static void main(String[] args) {
        DistributedQueryCache.configureQueryCache();
        
        HazelcastInstance hazelcastInstance = HazelcastConfiguration.createHazelcastInstance();
        IMap<Integer, String> map = hazelcastInstance.getMap("myMap");
        
        // Use query cache to fetch data
        Predicate predicate = new SqlPredicate("name = 'John'");
        Collection<String> results = map.values(predicate);
        
        for (String result : results) {
            System.out.println(result);
        }
    }
}

Conclusion

Using the Hazelcast distributed query cache in your Java applications can significantly improve performance and scalability, especially in distributed environments. By caching query results, you can reduce the load on your databases and improve response times.

With Hazelcast’s easy-to-use API and powerful caching capabilities, you can seamlessly integrate distributed query caching into your applications and reap the benefits of faster and more efficient data access.

#techblog #distributedcaching