Cache penetration, cache breakdown, cache avalanche differences and solutions [easy to understand]

Hello everyone , I meet you again , I'm your friend, Quan Jun .

One 、 Cache processing flow

Front desk request , The background first fetches data from the cache , Take the direct return result , Can't get it from the database , Database access to update cache , And return the result , The database was not accessed , That returns a null result directly .

Two 、 Cache penetration

describe ：

Cache penetration refers to data that does not exist in cache or database , And users are constantly requesting , If initiated as id by “-1” Data or id For very large nonexistent data . The user at this time is likely to be the attacker , The attack will cause too much pressure on the database .

Solution ：

1. Verification is added to the interface layer , Such as user authentication verification ,id Do basic verification ,id<=0 Direct interception of ;
2. Data not available from cache , In the database, there is no access to , At this time, you can also key-value Write as key-null, Cache effective time can be set as a short point , Such as 30 second （ Setting too long will cause normal conditions to be unavailable ）. This prevents the attacker from repeatedly using the same id Violent attack

3、 ... and 、 Cache breakdown

describe ：

Cache breakdown refers to data not in the cache but in the database （ Generally, the cache time expires ）, At this time, there are many concurrent users , At the same time, the read cache does not read the data , At the same time go to the database to get data , Causes the database pressure to increase instantaneously , Cause too much pressure

Solution ：

1. Set hotspot data never to expire .
2. Add mutex lock , The mutex reference code is as follows ：

explain ：

1） Data in cache , Go straight to the above code 13 The result will be returned after line

2） There is no data in the cache , The first 1 Incoming threads , Get the lock and get the data from the database , Before you release the lock , Other threads in parallel will wait 100ms, Then cache the data again . In this way, we can prevent repeated data retrieval from the database , Data is repeatedly updated into the cache .

3） Of course, this is a simplification , In theory, if we can base it on key It's better to lock the value , The thread A Take... From the database key1 Data does not prevent threads from B take key2 The data of , The above code obviously can't do this .

Four 、 Cache avalanche

describe ：

Cache avalanche refers to the time from bulk to expiration of data in cache , And the amount of query data is huge , Causes database pressure to be too big even down machine . Unlike cache breakdown , Cache breakdown refers to concurrent query of the same data , The cache avalanche is that different data has expired , A lot of data can't be found to look up the database .

Solution ：

1. The expiration time of cache data is set randomly , Prevent a large number of data expiration at the same time .
2. If the cache database is a distributed deployment , Distribute the hot data evenly in different cache databases .
3. Set hotspot data never to expire .

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