Is redis persistence enabled by default?

Linux system restricts user resources through the ulimit command to prevent excessive use of resources. 1.ulimit is a built-in shell command that can limit the number of file descriptors (-n), memory size (-v), thread count (-u), etc., which are divided into soft limit (current effective value) and hard limit (maximum upper limit). 2. Use the ulimit command directly for temporary modification, such as ulimit-n2048, but it is only valid for the current session. 3. For permanent effect, you need to modify /etc/security/limits.conf and PAM configuration files, and add sessionrequiredpam_limits.so. 4. The systemd service needs to set Lim in the unit file

Laravel's page caching strategy can significantly improve website performance. 1) Use cache helper functions to implement page caching, such as the Cache::remember method. 2) Select the appropriate cache backend, such as Redis. 3) Pay attention to data consistency issues, and you can use fine-grained caches or event listeners to clear the cache. 4) Further optimization is combined with routing cache, view cache and cache tags. By rationally applying these strategies, website performance can be effectively improved.

The steps for troubleshooting and repairing Redis master-slave replication failures include: 1. Check the network connection and use ping or telnet to test connectivity; 2. Check the Redis configuration file to ensure that the replicaof and repl-timeout are set correctly; 3. Check the Redis log file and find error information; 4. If it is a network problem, try to restart the network device or switch the alternate path; 5. If it is a configuration problem, modify the configuration file; 6. If it is a data synchronization problem, use the SLAVEOF command to resync the data.

The quick location and processing steps for Redis cluster node failure are as follows: 1. Confirm the fault: Use the CLUSTERNODES command to view the node status. If the fail is displayed, the node will fail. 2. Determine the cause: Check the network, hardware, and configuration. Common problems include memory limits exceeding. 3. Repair and restore: Take measures based on the reasons, such as restarting the service, replacing the hardware or modifying the configuration. 4. Notes: Ensure data consistency, select appropriate failover policies, and establish monitoring and alarm systems.

Redis and RabbitMQ each have their own advantages in performance and joint application scenarios. 1.Redis performs excellently in data reading and writing, with a latency of up to microseconds, suitable for high concurrency scenarios. 2.RabbitMQ focuses on messaging, latency at milliseconds, and supports multi-queue and consumer models. 3. In joint applications, Redis can be used for data storage, RabbitMQ handles asynchronous tasks, and improves system response speed and reliability.

Effective solutions to the problem of split brain in Redis cluster include: 1) Network configuration optimization to ensure connection stability; 2) Node monitoring and fault detection, real-time monitoring with tools; 3) Failover mechanism, setting high thresholds to avoid multiple master nodes; 4) Data consistency guarantee, using replication function to synchronize data; 5) Manual intervention and recovery, and manual processing if necessary.

Methods to improve Redis persistence performance through configuration include: 1. Adjust the save parameters of RDB to reduce the snapshot generation frequency; 2. Set the appendfsync parameter of AOF to everysec; 3. Use AOF and RDB in combination; 4. Use no-appendfsync-on-rewrite parameters to optimize AOF rewrite performance; 5. Enable hybrid persistence mode. These configurations can improve performance while ensuring data security.

Use the Bloom filter to protect cache penetration because it can quickly determine whether an element may exist, intercept non-existent requests, and protect the database. The Redis Bloom filter efficiently judges the existence of elements through low memory usage, successfully intercepts invalid requests, and reduces database pressure. Despite the misjudgment rate, such misjudgment is acceptable in cache penetration protection.