One 、 through

1、 What is penetration

The user wants to query a data , Find out redis Memory databases don't have , That is, cache miss , So query the persistence layer database . There is no , So this query failed . When there are a lot of users , No cache hits , So they all went to the persistence layer database . This will put a lot of pressure on the persistence layer database , This is equivalent to cache penetration .

2、 Solution

programme 1： The bloon filter

It's a data structure , For all possible query parameters, use hash stored , Check at the control level first , If not, discard , Thus, the query pressure on the underlying storage system is avoided

programme 2： Caching empty objects

When the storage tier misses , Even empty objects returned are cached , At the same time, an expiration time will be set , Then accessing this data will get from the cache , Protected back-end data sources ;

But there are two problems with this approach ：

1、 If null values can be cached , This means that the cache needs more space to store more keys , Because there may be a lot of empty keys ;

2、 Even if expiration time is set for null value , There will be some inconsistency between the data of cache layer and storage layer for a period of time , This has an impact on businesses that need to be consistent .

programme 3： Set up an accessible list

Use bitmap Type defines a list of accessible , list id As bitmaps Offset , Every visit and bitmap Inside id Compare , If you visit id be not in bitmaps Inside to intercept

programme 4： Real-time monitoring

Find out redis The hit rate began to decrease rapidly Configure access objects and data Set blacklist restrictions

Two 、 breakdown

1、 What is breakdown

It means a key Very hot , Constantly carrying big concurrency , Large concurrent set Access this point , When this key At the moment of failure , Continuous large concurrency breaks through the cache , Direct request database , Like in a A hole was cut in a barrier . When a key At the moment of expiration , There are a lot of requests for concurrent access , This kind of data is generally hot data , Due to cache expiration , Will visit at the same time Ask the database for the latest data , And write back to the cache , Will cause the database transient pressure is too large .

2、 Solution

1、 Preset hot data ：redis Before the summit visit , Put some hot data into redis Inside , Increase the of these popular data key Long time

2、 Real time adjustments ： What data is hot on the spot , Real time adjustments key Expiration time

3、 Using locks ：

When the cache fails （ Judge that the value is empty ）, Not immediately load db;

First, use some operations of the caching tool with the return value of successful operations, such as redis Of SETNX

3、 ... and 、 Cache avalanche problem

1、 What is a cache avalanche

Cache avalanche ： A large amount of data at the same time key Centralized expiration of , Request all forwarding To DB,DB Avalanche with excessive instantaneous pressure . The server crashed

2、 Solution

1、 Building a multi-level cache architecture ：nginx cache +redis cache + Other caches

2、 Use locks or queues ： Lock or queue to ensure that there will not be a large number of threads reading and writing to the database at one time , Avoid a large number of concurrent requests falling on the underlying storage system when it fails , Not for high concurrency

3、 Set expiration flag to update cache ： Record whether the cache data is out of date （ Set the lead amount ）, If it expires, it will trigger another thread to update the actual situation in the background key The cache of

4、 Spread the cache expiration time ： For example, you can add a random value to the original failure time , such as 1-5 Minutes at random , In this way, the expiration time repetition rate of each cache will be reduced , It's hard to trigger a collective failure