What are the two pitfalls that Redis distributed locks must avoid?

1 The first pit: Wrong lock release timing

1.1. Found the problem

Analyze the problems in the following code:

// 分布式锁服务
public interface RedisLockService {
    // 获取锁
    public boolean getLock(String key);
    // 释放锁
    public boolean releaseLock(String key);
}

// 业务服务
public class BizService {

    @Resource
    private RedisLockService redisLockService;

    public void bizMethod(String bizId) {
        try {
            // 获取锁
            if(redisLockService.getLock(bizId)) {
                // 业务重复校验
                if(!bizValidate(bizId)) {
                    throw new BizException(ErrorBizCode.REPEATED);
                }
                // 执行业务
                return doBusiness();
            }
            // 获取锁失败
            throw new BizException(ErrorBizCode.GET_LOCK_ERROR);
        } finally {
            // 释放锁
            redisLockService.releaseLock(bizId);
        }
    }
}

The above code seems to be fine, In fact, it hides a big problem. The problem is that when releasing the lock, there is no verification whether the current thread has obtained the lock:

• Thread 1 and Thread 2 access the business method at the same time

• Thread 2 Obtain the lock successfully and perform business processing

• Thread 1 did not acquire the lock, but released the lock successfully

• At this time, thread 3 tried to acquire The lock is successful, but the business of Thread 2 has not been processed, so Thread 3 will not cause a business duplication exception

• Ultimately, Thread 2 and Thread 3 will repeatedly execute the business

1.2 Solve the problem

The solution is to allow the lock to be released only after confirming that the lock acquisition is successful:

public class BizService {

    @Resource
    private RedisLockService redisLockService;

    public void bizMethod(String bizId) {
        boolean getLockSuccess = false;
        try {
            // 尝试获取锁
            getLockSuccess = redisLockService.getLock(bizId);
            // 获取锁成功
            if(getLockSuccess) {
                // 业务重复校验
                if(!bizValidate(bizId)) {
                    throw new BizException(ErrorBizCode.REPEATED);
                }
                // 执行业务
                return doBusiness();
            }
            // 获取锁失败
            throw new BizException(ErrorBizCode.GET_LOCK_ERROR);
        } finally {
            // 获取锁成功才允许释放锁
            if(getLockSuccess) {
                redisLockService.releaseLock(bizId);
            }
        }
    }
}

2 The second pit: cache invalidation problem

No. The second problem is that Redis also has a memory cleaning mechanism, which may cause distributed locks to fail.

2.1 Expiration cleanup mechanism

(1) Regular deletion

Redis regularly checks which keys have expired, and deletes them if they are found to be expired

(2) Lazy deletion

If there are too many keys, regular deletion will consume a lot of resources, so a lazy deletion strategy is introduced

If Redis finds that the key has expired when accessing it, Direct deletion

2.2 Memory recycling mechanism

When memory is insufficient, Redis will select some elements for deletion:

no-enviction

Prohibit eviction of data, new The write operation will report an error

volatile-lru

Select the least recently used data from the data set with an expiration time to be eliminated

volatile-ttl

Select the data to be expired from the data set with expiration time set to be eliminated

volatile-random

Select any data to be eliminated from the data set with expiration time set

allkeys- lru

Select the least recently used data from the data set to eliminate

allkeys-random

Select any data from the data set to eliminate

There are at least two Scenario causes distributed lock failure problem:

• Scenario 1: Redis has insufficient memory for memory recycling, use allkeys-lru or allkeys-random The recycling strategy causes lock failure

• Scenario 2: The thread successfully acquires the distributed lock, but the processing time is too long. At this time, the lock expires and is regularly cleared, causing other threads to successfully acquire the lock and fail. Repeated execution of business

2.3 Optimistic lock

The general solution is to protect it at the database layer. For example, the inventory deduction business uses optimistic locking at the database layer.

udpate goods set stock = stock - #{acquire}
where sku_id = #{skuId} and stock - #{acquire} >= 0

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