Ten minutes to thoroughly master cache breakdown, cache penetration, cache avalanche

Write an article to slow down , Share the old stereotype ： Cache breakdown 、 Cache penetration 、 Cache avalanche .

Before we understand these three problems , We need to understand , Commonly used distributed cache Redis The amount of single machine concurrency can reach 10000 , Common relational databases MySQL The general concurrency is 1000 , The amount of concurrency they support may be ten times worse , Therefore, the traffic should be intercepted in the cache layer as much as possible .

Why? ？ It's like draining more water in a big lake , It may wash out the small river , I don't know if you've heard of —— The water of the Yangtze River comes up from the sky , Baiyangdian can't hold .

Contents of this article :

             Cache breakdown

What is cache breakdown

How to solve

            Cache penetration

What is cache penetration

How to solve cache penetration

          Cache avalanche

What is a cache avalanche

How to solve the cache avalanche

Text ：

What is cache breakdown

Start with cache breakdown .

Cache breakdown ：  A large number of concurrent visits key Expire at a certain time , Cause all requests to be typed directly in DB On .

Cache breakdown

Cache breakdown will increase the load on the database , Let's see how to alleviate .

How to solve

Lock update

The query cache , It is found that there is no... In the cache , Lock , Let other threads wait , Let only one thread update the cache .

Lock update

Asynchronous update

Another possible solution is to set the cache to never expire . How to update the cache ？ Update the cache asynchronously .

For example, a daemon thread is set in the background to update the cache regularly , But this timing is difficult to grasp .

Asynchronous update mechanism is actually more suitable for cache preheating .

Cache penetration

What is cache penetration

Cache penetration ： Cache penetration refers to data that does not exist in both the query cache and the database , In this way, each request is directly called to the database , It's like the cache doesn't exist .

Cache penetration

Cache penetration will cause non-existent data to be queried in the storage layer every time it is requested , Lost the significance of cache protection back-end storage .

Cache penetration may increase the back-end storage load , If a large number of storage layer null hits are found , Maybe there is a cache penetration problem .

Cache penetration can occur for two reasons ：

1. Own business code problem

2. A malicious attack , Reptiles cause empty hits

Let's see how to solve .

How to solve cache penetration

Cache null / The default value is

One way is after a database miss , Save an empty object or default value to the cache , Then access the data , It gets it from the cache , This protects the database .

Cache null

There are two major problems with caching null values ：

1. Null values are cached , It means that there are more keys in the cache layer , Need more memory space （ If it's an attack , The problem is more serious ）, More effective

   The method is to set a shorter expiration time for this kind of data , Let it automatically remove .

2. The data of cache layer and storage layer will be inconsistent for a period of time , It may have some impact on the business .

   For example, the expiration time is set to 5 minute , If the storage layer adds this data at this time , Then there will be inconsistencies between the cache layer and the storage layer data in this period of time .

   At this time, you can use message queue or other asynchronous methods to clean up the empty objects in the cache .

The bloon filter

In addition to caching empty objects , We can also store and cache before , Add a bloom filter , Do a layer of filtering .

The bloom filter will store whether the data exists , If you judge that the data is not, you can no longer , No access to storage .

The bloom filter filters

What's the bloom filter ？ Find out if it will be slow ？

What is a bloon filter ？

I don't know how much you know about hash tables , The bloom filter is a similar thing .

It is a continuous data structure , Each storage bit is a bit, namely 0 perhaps 1, To identify whether the data exists .

When storing data , Use K A different hash function maps this variable to bit Of the list K A little bit , Set them as 1.

Bulon filter structure

We judge the cache key Whether there is , Again ,K Hash functions , Mapping to bit On the list K A little bit , Judgment is not 1：

• If it's not 1, that key non-existent ;

• If it's all 1, It just means key Possible .

As for why ？ Because hash functions may collide .

There are two main solutions for cache penetration , Let's make a simple comparison ：

Cache empty object core bloom filter scheme comparison

Cache avalanche

Next, let's look at the most serious situation , Cache avalanche .

What is a cache avalanche

Cache avalanche ：  When ⼀ All the time ⽣⼤ Large scale cache invalidation , For example, the cache service is down 、 A lot of key Expire at the same time , The result is ⼤ The quantity request comes in and calls directly to DB On , May cause the whole system to crash , It's called an avalanche .

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