实现多级缓存的策略方法
保证多级缓存数据一致性是一个复杂的任务，尤其是在分布式和高并发环境中。以下是一些常见的方法和策略，可以帮助实现多级缓存的数据一致性
1. 缓存失效策略
1.1 主动失效
在更新数据库时，主动使相关缓存失效。
步骤：
- 更新数据库
- 删除或失效缓存
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
public void updateData(Data data) {
database.update(data);
String cacheKey = "data:" + data.getId();
localCache.delete(cacheKey);
redisCache.delete(cacheKey);
}
}
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1.2 延迟双删（Lazy Delete）
在更新数据库前后都删除缓存，确保缓存数据的准确性。
步骤：
- 更新前删除缓存
- 更新数据库
- 更新后再删除一次缓存
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
public void updateData(Data data) {
String cacheKey = "data:" + data.getId();
localCache.delete(cacheKey);
redisCache.delete(cacheKey);
database.update(data);
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
localCache.delete(cacheKey);
redisCache.delete(cacheKey);
}
}
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2. 双写一致性策略
2.1 先更新数据库，再更新缓存
步骤：
- 更新数据库
- 更新缓存
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
public void updateData(Data data) {
database.update(data);
String cacheKey = "data:" + data.getId();
localCache.set(cacheKey, data);
redisCache.set(cacheKey, data);
}
}
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2.2 先删除缓存，再更新数据库
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
public void updateData(Data data) {
String cacheKey = "data:" + data.getId();
localCache.delete(cacheKey);
redisCache.delete(cacheKey);
database.update(data);
}
}
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3. 缓存更新策略
3.1 写通过（Write-through）
在写入数据库时，同时更新缓存。
步骤：
- 更新数据库
- 更新缓存
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
public void updateData(Data data) {
database.update(data);
String cacheKey = "data:" + data.getId();
localCache.set(cacheKey, data);
redisCache.set(cacheKey, data);
}
}
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3.2 写回（Write-back）
先更新缓存，定期将缓存数据写回数据库。
步骤：
- 更新缓存
- 定期将缓存数据写回数据库
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
public void updateData(Data data) {
String cacheKey = "data:" + data.getId();
localCache.set(cacheKey, data);
redisCache.set(cacheKey, data);
scheduleWriteBack(data);
}
private void scheduleWriteBack(Data data) {
}
}
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4. 分布式锁
使用分布式锁（如 Redis 的 RedLock）来确保多节点环境下的一致性。
步骤：
- 获取分布式锁
- 更新数据库和缓存
- 释放分布式锁
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
private JedisPool jedisPool;
public void updateDataWithLock(Data data) {
String lockKey = "lock:data:" + data.getId();
try (Jedis jedis = jedisPool.getResource()) {
String lock = acquireLock(jedis, lockKey, 10000);
if (lock != null) {
try {
updateData(data);
} finally {
releaseLock(jedis, lockKey, lock);
}
}
}
}
private void updateData(Data data) {
database.update(data);
String cacheKey = "data:" + data.getId();
localCache.set(cacheKey, data);
redisCache.set(cacheKey, data);
}
private String acquireLock(Jedis jedis, String lockKey, int timeout) {
}
private void releaseLock(Jedis jedis, String lockKey, String lock) {
}
}
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5. 异步更新
使用异步机制，在更新数据库后异步更新缓存。
步骤：
- 更新数据库
- 异步更新缓存
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
private ExecutorService executorService = Executors.newFixedThreadPool(10);
public void updateData(Data data) {
database.update(data);
String cacheKey = "data:" + data.getId();
executorService.submit(() -> updateCache(cacheKey, data));
}
private void updateCache(String cacheKey, Data data) {
localCache.set(cacheKey, data);
redisCache.set(cacheKey, data);
}
}
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6. 使用消息队列
通过消息队列通知其他缓存节点更新缓存。这里举例使用rabbitmq简单实现，也可以用其他mq或者Canal实现
步骤：
- 更新数据库
- 发送缓存失效消息到消息队列
- 各缓存节点订阅消息队列，接收到消息后失效缓存
import com.rabbitmq.client.ConnectionFactory;
import com.rabbitmq.client.Connection;
import com.rabbitmq.client.Channel;
public class RabbitMQConfig {
public static final String QUEUE_NAME = "cache_invalidation_queue";
public static Channel createChannel() throws Exception {
ConnectionFactory factory = new ConnectionFactory();
factory.setHost("localhost");
Connection connection = factory.newConnection();
return connection.createChannel();
}
}
public class CacheService {
private LocalCache localCache;
private RedisCache redisCache;
private Database database;
private Channel channel;
public CacheService() throws Exception {
this.channel = RabbitMQConfig.createChannel();
channel.queueDeclare(RabbitMQConfig.QUEUE_NAME, false, false, false, null);
}
public void updateData(Data data) throws Exception {
database.update(data);
String cacheKey = "data:" + data.getId();
channel.basicPublish("", RabbitMQConfig.QUEUE_NAME, null, cacheKey.getBytes());
}
}
import com.rabbitmq.client.*;
public class CacheInvalidationListener {
private LocalCache localCache;
private RedisCache redisCache;
private Channel channel;
public CacheInvalidationListener() throws Exception {
this.channel = RabbitMQConfig.createChannel();
channel.queueDeclare(RabbitMQConfig.QUEUE_NAME, false, false, false, null);
}
public void startListener() throws Exception {
Consumer consumer = new DefaultConsumer(channel) {
@Override
public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException {
String cacheKey = new String(body, "UTF-8");
localCache.delete(cacheKey);
redisCache.delete(cacheKey);
}
};
channel.basicConsume(RabbitMQConfig.QUEUE_NAME, true, consumer);
}
public static void main(String[] args) throws Exception {
CacheInvalidationListener listener = new CacheInvalidationListener();
listener.startListener();
}
}
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TIPS：异步更新和消息队列思想的区别
异步更新：适用于单节点或小规模系统，依赖于应用内部的异步处理机制，较为简单但在分布式环境中扩展性差。
使用消息队列：适用于分布式和大规模系统，依赖于外部消息队列系统，在多个节点之间确保数据一致性，更具扩展性但实现和运维复杂度较高。
结论
通过结合使用主动失效、延迟双删、双写一致性、写通过、写回、分布式锁和异步更新等策略，可以有效地保证多级缓存的数据一致性。选择合适的策略取决于具体的应用场景和需求O(∩_∩)O
