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This article mainly shows you "Redis distributed locks how to prevent cache breakdown", the content is easy to understand, clear, hope to help you solve your doubts, let the editor lead you to study and learn "Redis distributed locks how to prevent cache breakdown" this article.

Cache breakdown

Unlike cache penetration, cache breakdown refers to hot data that is not in the cache but exists in the database.

For example: hot news on the home page, hot data with a large number of concurrent visits, if the cache expires, the server will query DB. At this time, if a large number of concurrent queries to DB, it may instantly crush DB.

A schematic diagram is drawn as follows:

Solution: DB query plus distributed lock.

An unlocked condition.

Before you solve the problem, take a look at the code and operation that are not processed.

Query the commodity details code according to the commodity ID

Clear the Redis cache and start 5 threads to access the test concurrently. The test code is as follows:

We expect DB to query only once, and the next four queries can be fetched from the Redis cache, but the result:

There is no distributed lock, and the result is expected, but this container puts a lot of pressure on DB.

If it is a single server, use Java's synchronization lock directly.

Unfortunately, clusters are usually deployed at the back end, and there is no way for Java synchronous locks to implement distributed locks.

Redis distributed lock solves cache breakdown

The built-in lock of Java can only be used on a single machine, and it can not be distributed, so we can skillfully use Redis to realize distributed lock.

Code with distributed locks added

/ / query goods @ GetMapping ("/ {id}") public R id (@ PathVariable String id) according to ID {/ / first check Redis cache Object o = redisTemplate.opsForValue () .get (id); if (o! = null) {/ / hit cache System.err.println ("id:" + id+ ", hit redis cache.") Return R.success (o);} / cache missed query database String lockKey = "lock" + id; / / locked, and expired for ( ) {if (redisTemplate.opsForValue () .setIfAbsent (lockKey, System.currentTimeMillis (), 10L, TimeUnit.SECONDS)) {/ / successfully locked thread, check o = redisTemplate.opsForValue () .get (id) again If (o! = null) {/ / hit cache System.err.println ("Thread:" + Thread.currentThread () .getName () + ", id:" + id+ ", hit redis cache.") / / release lock redisTemplate.delete (lockKey); return R.success (o) } / / still missed System.err.println ("Thread:" + Thread.currentThread (). GetName () + ", id:" + id + ", query DB..."); Goods goods = goodsMapper.selectById (id) / / the result is stored in Redis redisTemplate.opsForValue () .set (id, goods); / / release lock redisTemplate.delete (lockKey); return R.success (goods) } / / cannot compete to lock, temporarily give up CPU resource Thread.yield ();}}

Start 5 threads and access them concurrently. The result is shown below:

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