How to solve the avalanche caused by redis

Use the SCAN command to cooperate with UNLINK to safely delete keys that match patterns in Redis. First, use SCAN0MATCHpatternCOUNT to batch obtain the key name to avoid blocking; then use UNLINK asynchronously to improve performance. It is recommended to use redis-cli--scan--pattern'pattern'|xargsredis-cliunlink to achieve efficient deletion on the command line, and prohibit the use of KEYS command in production environments.

Running Redis with Docker without installing it on the host, it can be quickly started through the dockerrun command; it can customize configuration files and mount them to implement memory policies and other settings; it can persist data by naming volume redis-data; it is recommended to use DockerCompose to facilitate the deployment and maintenance of the development environment.

UseBGSAVEformanualorconfiguresavepointsforautomaticRDBsnapshotstobackupRedis;2.Locatethedump.rdbfileviaconfigandcopyitsecurely;3.Torestore,stopRedis,replacetheRDBfile,ensureproperpermissions,restart,andhandleAOFifenabled;4.Followbestpracticeslikesche

First add SpringDataRedis dependencies, then set Redis connection information in the configuration file, then enable cache through @EnableCaching and use cache annotations, and finally operate data through RedisTemplate or StringRedisTemplate to realize cache, session storage or high-speed data access.

UseFLUSHDBtoclearthecurrentdatabaseorFLUSHALLforalldatabases;bothsupportASYNC(background)orSYNC(blocking)modes,withASYNCpreferredinproductiontoavoidlatency.

Installing Redis can be done through APT or source code, and APT is simpler; 2. Update the package index and install redis-server; 3. Start and enable the power-on self-start; 4. Use redis-cliping to test PONG; 5. Optional configuration files to adjust bindings, passwords, etc.; 6. Restart the service and complete the installation.

Choosing a persistence model requires trade-offs based on application requirements, load behavior, and data type. Common models include memory only (fast but not durable), disk storage (slow but persistent), hybrid mode (both speed and durable), and write-pre-log (high persistence). If you process key data, you should choose a WAL or ACID database; if you can tolerate a small amount of data loss, you can choose a memory or a hybrid model. At the same time, consider the complexity of operation and maintenance, such as cloud environments, you should choose a good integrated solution. Common errors need to be avoided, such as mistakenly treating snapshots as lasting guarantees, ignoring crash recovery tests, untuning synchronization frequency, etc. In short, it is key to clarifying priorities and performing exception scenario testing.

HyperLogLog provides a memory efficient and fast unique count estimation method in Redis via PFADD and PFCOUNT commands. 1. HyperLogLog is a probability algorithm used to estimate the number of different elements in the dataset. It only requires a small amount of fixed memory to process large-scale datasets. It is suitable for tracking independent visitors or high-frequency search queries and other scenarios; 2. PFADD is used to add elements to HyperLogLog, and PFCOUNT returns the unique element estimate value in one or more structures; 3. Using meaningful key names, directly adding string values, and merging multiple HLLs to avoid repeated calculations are the best practices for using PFADD and PFCOUNT; 4. HyperLo