使用Redisson的布隆过滤器解决缓存穿透问题
import org.redisson.api.RBloomFilter;
import org.redisson.api.RedissonClient;
import org.redisson.Redisson;
import org.redisson.config.Config;
import java.util.concurrent.ConcurrentHashMap;
public class BloomFilterExample {
private static RedissonClient redisson;
private static RBloomFilter<String> bloomFilter;
// 模拟数据库
private static ConcurrentHashMap<String, String> database = new ConcurrentHashMap<>();
static {
// 初始化Redisson
Config config = new Config();
config.useSingleServer().setAddress("redis://127.0.0.1:6379");
redisson = Redisson.create(config);
// 创建布隆过滤器
bloomFilter = redisson.getBloomFilter("myBloomFilter");
bloomFilter.tryInit(1000000, 0.03); // 初始化布隆过滤器：预计插入100万条数据，误判率0.03%
// 模拟数据库数据
database.put("user:1", "John");
database.put("user:2", "Jane");
// 将存在的用户ID加入布隆过滤器
bloomFilter.add("user:1");
bloomFilter.add("user:2");
}
public static void main(String[] args) {
System.out.println(getUserData("user:1")); // 从数据库获取
System.out.println(getUserData("user:3")); // 触发布隆过滤器
}
public static String getUserData(String userId) {
// 检查布隆过滤器
if (!bloomFilter.contains(userId)) {
return "Invalid Request (User does not exist)";
}
// 模拟数据库查询
String userData = database.get(userId);
if (userData != null) {
return "User Data: " + userData;
} else {
return "User Data Not Found";
}
}
}