Redis (VIII) - enterprise level solution (I)

Redis（ 8、 ... and ）—— Enterprise solutions （ One ）

1、 Cache preheating

Troubleshoot problems

• The number of requests is high
• The data throughput between master and slave is large , The operation frequency of data synchronization is high

Solution

• Preparatory work ：
  • Daily routine statistical data access records , Statistics of hot data with high access frequency
  • utilize LRU Data deletion strategy , Building a data retention queue for example ：storm And kafka coordination
• preparation ：
  • Classify the data in the statistical results , According to the level ,redis Load high-level hot data first
  • Using distributed multi server to read data at the same time , Speed up the data loading process
  • Hot data master and slave warm up at the same time
• The implementation of ：
  • Use script program to fix trigger data warm-up process
  • If conditions permit , Used CDN（ Content distribution network ）, It works better

summary

Cache warm-up is before the system starts , Load the relevant cache data directly into the cache system in advance . Avoid when the user requests , Query database first , And then cache the data ！ Users directly query the pre heated cache data ！

2、 Cache avalanche

Database server crash （1）

1. During the smooth operation of the system , All of a sudden, the number of database connections surged
2. The application server cannot process the request in time
3. A lot of 408,500 The error page appears
4. Customers repeatedly refresh the page to get data
5. Database crash
6. The application server crashed
7. It is invalid to restart the application server
8. Redis Server crash
9. Redis Cluster crash
10. After restarting the database, it is put down by the instantaneous traffic again

Troubleshoot problems

1. In a Shorter In time , More in the cache key Concentration expired
2. Request access to expired data during this cycle ,redis Not hit ,redis Getting data from a database
3. The database receives a large number of requests at the same time and cannot process them in time
4. Redis There's a huge backlog of requests , It's starting to time out
5. Database traffic surges , Database crash
6. There is no data available in the cache after restart
7. Redis Server resources are heavily occupied ,Redis Server crash
8. Redis The cluster is collapsing , Cluster collapse
9. The application server can't get the data in time to respond to the request , There are more and more requests from clients , The application server crashed
10. application server ,redis, Restart all databases , The effect is not ideal

Problem analysis

• In a short period of time
• A lot of key Concentration expired

Solution （ Avenue ）

1. More static page processing
2. structure Multi level cache architecture Nginx cache +redis cache +ehcache cache
3. testing Mysql Serious time-consuming business optimization Check the bottleneck of the database ： For example, timeout query 、 Time consuming, high transaction, etc
4. Disaster warning mechanism monitor redis Server performance metrics
   • CPU Occupy 、CPU Usage rate
   • Memory capacity
   • Average query response time
   • Number of threads
5. Current limiting 、 Downgrade Sacrifice some customer experience in a short period of time , Restrict access to some requests , Reduce application server pressure , After the business is running at a low speed, the access will be gradually released

Solution （ Technique ）

1. LRU And LFU Switch
2. Data validity policy adjustment
   • According to the validity period of business data Classified peak staggering ,A class 90 minute ,B class 80 minute ,C class 70 minute
   • The expiration time is fixed time + The form of random values , Dilution Centralized due key The number of
3. Hyperthermia Data usage is permanent key
4. Regular maintenance （ Automatically + artificial ） Do traffic analysis for data that is about to expire , Confirm whether there is a delay , With the visit statistics , Delay of hot data
5. Lock Use with caution ！

summary

The cache avalanche is The amount of data that expired in a moment is too large , Causing pressure on the database server . If you can Effectively avoid expiration time concentration , It can effectively solve the emergence of avalanche phenomenon （ about 40%）, Use with other strategies , And monitor the running data of the server , Make quick adjustment according to the operation record .

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