Caching is a popular technique used to improve the performance and scalability of an application by storing frequently accessed data in memory. However, when dealing with distributed systems, ensuring data consistency and preventing race conditions becomes a challenge.

In this blog post, we will explore how to implement a cache with distributed locking using Redisson and a HashMap in Java. Redisson is a Java library that provides a simple and efficient way to interact with Redis, a popular in-memory data store.

Table of Contents

• What is Redis?
• What is Redisson?
• Implementing the Cache
• Distributed Locking
• Conclusion

What is Redis?

Redis is an open-source, in-memory data store that can be used as a cache, database, or message broker. It supports various data structures, including strings, lists, hashes, sets, and more. Redis provides fast read and write operations, making it suitable for caching frequently accessed data.

What is Redisson?

Redisson is a Java client library for Redis that provides easy-to-use abstractions and features for working with Redis data structures. It offers distributed implementations of various Java objects, such as maps, lists, queues, and locks, allowing developers to leverage Redis for distributed caching and synchronization.

Implementing the Cache

To implement the cache, we will use a combination of Redisson’s RMapCache and a local HashMap. The RMapCache will serve as the distributed cache, while the HashMap will act as a local cache for faster access.

First, let’s create a singleton CacheManager class that will handle initializing Redisson and managing the cache:

import org.redisson.Redisson;
import org.redisson.api.RedissonClient;
import org.redisson.config.Config;

public class CacheManager {
    private static final String REDIS_HOST = "localhost";
    private static final int REDIS_PORT = 6379;
    private static final String MAP_CACHE_NAME = "myCache";
    private static final int MAP_CACHE_TTL_SECONDS = 60;
    
    private static RedissonClient redissonClient;
    private static RMapCache<String, Object> cache;
    
    private CacheManager() {
        Config config = new Config();
        config.useSingleServer().setAddress("redis://" + REDIS_HOST + ":" + REDIS_PORT);
        redissonClient = Redisson.create(config);
        
        cache = redissonClient.getMapCache(MAP_CACHE_NAME);
        cache.setMaxSize(10000);
        cache.setTimeToLive(MAP_CACHE_TTL_SECONDS);
    }
    
    public static RMapCache<String, Object> getCache() {
        return cache;
    }
}

In the CacheManager class, we initialize a Redisson client and create an RMapCache instance with a specified cache name and time-to-live (TTL) in seconds. We also set a maximum size for the cache to prevent it from growing indefinitely.

Now, we can utilize the cache in our application. Here’s an example of how to retrieve a value from the cache:

RMapCache<String, Object> cache = CacheManager.getCache();
String key = "myKey";
Object value = cache.get(key);
if (value == null) {
    // If the value is not present in the cache, fetch it from the data source
    value = fetchDataFromDataSource(key);
    cache.put(key, value);
}

Here, we first check if the value for a given key is already present in the cache. If not, we fetch the value from the data source and put it in the cache for future retrievals.

Distributed Locking

To prevent multiple threads or distributed instances from updating the cache simultaneously, we can introduce distributed locking using Redisson’s RLock. Here’s an example of how to perform a lock before updating the cache:

RMapCache<String, Object> cache = CacheManager.getCache();
RLock lock = cache.getLock("myLock");
lock.lock();
try {
    // Update or fetch data from the data source
    // Update the cache
} finally {
    lock.unlock();
}

In the example above, we acquire a lock named “myLock” from the cache. Inside the lock block, we can safely update or fetch data from the data source and update the cache accordingly. Finally, we release the lock by calling the unlock() method.

Distributed locking ensures that only one thread or instance can update the cache at a time, preventing race conditions and maintaining data consistency.

Conclusion

In this blog post, we have explored how to implement a cache with distributed locking using Redisson and a HashMap in Java. By leveraging Redisson’s features, we can create a distributed cache that provides fast, scalable, and consistent access to frequently accessed data.

Using Redis as a cache along with distributed locking adds resilience and concurrency control to our application, making it ideal for distributed systems handling high loads.