How to implement high availability and deletion mechanism of distributed cache in Java

How to implement high availability and deletion mechanism of distributed cache in Java

Introduction:
With the advent of the big data era, distributed cache as A high-performance, high-availability solution that is widely used in various Internet applications. In order to ensure the high availability of the distributed cache and the consistency of the data, we need to implement a high availability mechanism and a deletion mechanism based on it. This article will introduce how to implement high availability and deletion mechanism of distributed cache in Java, and provide specific code examples.

1. High availability mechanism
In order to ensure the high availability of distributed cache, we can achieve it through master-slave replication and sharding mechanisms. Master-slave replication can copy data to multiple nodes to implement backup functions. When the master node goes down, you can select a slave node as the new master node to ensure the normal operation of the system. The sharding mechanism can divide data into multiple fragments and store them on different nodes. When a node goes down, it will only affect the data on that node, while the data on other nodes can still be accessed normally.

Specific implementation:

1. Master-slave replication
   In Java, you can use Redis as the storage engine of distributed cache and implement it by configuring the master-slave replication function of Redis High availability. The following is a sample code:

JedisPoolConfig config = new JedisPoolConfig();  // 创建连接池配置对象
JedisPool pool = new JedisPool(config, "masterIP", 6379);  // 创建主节点连接池

// 指定从节点的IP和端口
List<JedisShardInfo> shards = Arrays.asList(
    new JedisShardInfo("slave1IP", 6379),
    new JedisShardInfo("slave2IP", 6379)
);

JedisSentinelPool sentinelPool = new JedisSentinelPool("mymaster", shards, pool);  // 创建哨兵连接池

Jedis jedis = sentinelPool.getResource();  // 获取连接对象
jedis.set("key", "value");  // 设置缓存

String value = jedis.get("key");  // 获取缓存

2. Sharding mechanism
   In Java, the sharding mechanism can be implemented using a consistent hashing algorithm. The consistent hash algorithm can distribute data to different nodes according to hash values, thereby achieving sharded storage of data. The following is a sample code:

ConsistentHash<CacheNode> consistentHash = new ConsistentHash<>(new HashFunction(), 100, nodeList);  // 创建一致性哈希对象

CacheNode node = consistentHash.get(cacheKey);  // 根据缓存键获取对应的节点

node.put(cacheKey, cacheValue);  // 将缓存数据存储在对应的节点上

String value = node.get(cacheKey);  // 从对应的节点获取缓存数据

2. Delete back mechanism
In order to ensure the consistency of cached data, we need to implement a delete back mechanism in the application. The deletion mechanism can update or delete the corresponding data in the cache at the same time when the data is updated or deleted.

Specific implementation:

1. Update deletion
   When the data is updated, the corresponding data in the cache needs to be updated at the same time. The following is a sample code:

void updateData(String key, Object newValue) {
    // 更新数据库中的数据
    updateDatabase(key, newValue);
    
    // 更新缓存中的数据
    updateCache(key, newValue);
}

void updateCache(String key, Object newValue) {
    Cache cache = getCache();  // 获取缓存对象
    cache.put(key, newValue);  // 更新缓存数据
}

2. Delete deletion
   When data is deleted, the corresponding data in the cache needs to be deleted at the same time. The following is a sample code:

void deleteData(String key) {
    // 删除数据库中的数据
    deleteDatabase(key);
    
    // 删除缓存中的数据
    deleteCache(key);
}

void deleteCache(String key) {
    Cache cache = getCache();  // 获取缓存对象
    cache.remove(key);  // 删除缓存数据
}

Conclusion:
This article introduces how to implement the high availability and deletion mechanism of distributed cache in Java, and provides specific code examples. The mechanism to achieve high availability and the deletion mechanism can effectively ensure the stability of distributed cache and data consistency, and improve the performance and reliability of the system. I hope this article will be helpful to readers in the design and development of distributed caches in practical applications.

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