需求背景：
限制某sql在30秒内最多只能执行3次
需求分析
微服务分布式部署，既然是分布式限流，首先自然就想到了结合redis的zset数据结构来实现。
分析对zset的操作，有几个步骤，首先，判断zset中符合rangeScore的元素个数是否已经达到阈值，如果未达到阈值，则add元素，并返回true。如果已达到阈值，则直接返回false。
代码实现
首先，我们需要根据需求编写一个lua脚本
redis.call('ZREMRANGEBYSCORE', KEYS[1], 0, tonumber(ARGV[3]))
local res = 0
if(redis.call('ZCARD', KEYS[1])
ARGV[1]: zset element
ARGV[2]: zset score(当前时间戳)
ARGV[3]: 30秒前的时间戳
ARGV[4]: zset key 过期时间30秒
ARGV[5]: 限流阈值
private final RedisTemplate redisTemplate; public boolean execLuaScript(String luaStr, List keys, List
测试一下效果
@SpringBootTest
public class ApiApplicationTest {
@Test
public void test2() throws InterruptedException{
String luaStr = "redis.call('ZREMRANGEBYSCORE', KEYS[1], 0, tonumber(ARGV[3]))n" +
"local res = 0n" +
"if(redis.call('ZCARD', KEYS[1])
测试结果符合预期！
扩展阅读
lua脚本每次都需要传一长串脚本内容来回传输，会增加网络流量和延迟，而且每次都需要服务器重新解释和编译，效率较为低下。因此，不建议在实际生产环境中直接执行lua脚本，而应该使用lua脚本的hash值来进行传输。
为了方便使用，我们先把方法封装一下
import lombok.RequiredArgsConstructor;
import org.springframework.data.redis.connection.RedisScriptingCommands;
import org.springframework.data.redis.connection.ReturnType;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.serializer.RedisSerializer;
import org.springframework.stereotype.Component;
import java.util.List;
/**
* @author 敖癸
* @formatter:on
* @since 2024/3/25
*/
@Component
@RequiredArgsConstructor
public class RedisService {
private final RedisTemplate redisTemplate;
private static RedisScriptingCommands commands;
private static RedisSerializer keySerializer;
private static RedisSerializer valSerializer;
public String loadScript(String luaStr) {
byte[] bytes = RedisSerializer.string().serialize(luaStr);
return this.getCommands().scriptLoad(bytes);
}
public T execLuaHashScript(String hash, Class returnType, List keys, Object[] args) {
byte[][] keysAndArgs = toByteArray(this.getKeySerializer(), this.getValSerializer(), keys, args);
return this.getCommands().evalSha(hash, ReturnType.fromJavaType(returnType), keys.size(), keysAndArgs);
}
private static byte[][] toByteArray(RedisSerializer keySerializer, RedisSerializer argsSerializer, List keys, Object[] args) {
final int keySize = keys != null ? keys.size() : 0;
byte[][] keysAndArgs = new byte[args.length + keySize][];
int i = 0;
if (keys != null) {
for (String key : keys) {
keysAndArgs[i++] = keySerializer.serialize(key);
}
}
for (Object arg : args) {
if (arg instanceof byte[]) {
keysAndArgs[i++] = (byte[]) arg;
} else {
keysAndArgs[i++] = argsSerializer.serialize(arg);
}
}
return keysAndArgs;
}
private RedisScriptingCommands getCommands() {
if (commands == null) {
commands = redisTemplate.getRequiredConnectionFactory().getConnection().scriptingCommands();
}
return commands;
}
private RedisSerializer getKeySerializer() {
if (keySerializer == null) {
keySerializer = redisTemplate.getKeySerializer();
}
return keySerializer;
}
private RedisSerializer getValSerializer() {
if (valSerializer == null) {
valSerializer = redisTemplate.getValueSerializer();
}
return valSerializer;
}
}
- 测试一下：
@SpringBootTest
@TestInstance(TestInstance.Lifecycle.PER_CLASS)
public class ApiApplicationTest implements ApplicationContextAware {
private static ApplicationContext context;
private static RedisService redisService;
public static String luaHash;
private final static String LUA_STR = "redis.call('ZREMRANGEBYSCORE', KEYS[1], 0, tonumber(ARGV[3]))n" +
"local res = 0n" +
"if(redis.call('ZCARD', KEYS[1]) keys = Collections.singletonList("aaaa");
Object[] args = new Object[]{"ele" + i, System.currentTimeMillis(), System.currentTimeMillis() - 30 * 1000, 30, 3};
Boolean b = redisService.execLuaHashScript(luaHash, Boolean.class, keys, args);
System.out.println(b);
Thread.sleep(3000);
}
}
}
使用的时候在项目启动时候，把脚本load一下，后续直接用hash值就行了
搞定收工！
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