A true master will always have the heart of an apprentice!
1. Project Introduction
Detect hot spot data in milliseconds and push it to the memory of the server cluster in milliseconds
Second, the implementation of functions
Local cache of MySQL hot data:
In high-traffic systems, frequent reading of data from MySQL can lead to performance bottlenecks. To solve this problem, a local cache can be used to store hot data to reduce frequent access to MySQL. Caching can be implemented using an in-memory database such as Memcached or Redis. In an application, some requests can be fulfilled by caching the data in them, reducing the pressure on MySQL and improving the responsiveness of the system.
Local cache of Redis hot data:
Redis is an in-memory database that is ideal for caching, supporting rich data structures and efficient storage and retrieval operations. For hot data, you can store it in Redis and set an appropriate expiration time to ensure the freshness of the data. By caching hot data in Redis, you can significantly improve the read performance of the system and reduce the pressure on backend storage.
Local caching of blacklisted users:
For blacklisted users, there are usually sensitive operations that need to be restricted or monitored. To speed up the process of checking blacklisted users, you can cache the blacklisted user list to local memory, for example, using Redis or memory cache. This way, every time you need to check whether a user is in the blacklist, you can query it directly from the local cache instead of having to query the database or other remote storage every time.
Crawler user throttling:
Crawler users can cause unnecessary load on the system and can lead to security issues such as data breaches. In order to restrict the access of crawler users, you can use methods such as IP address throttling and access frequency limiting. These restrictions can be implemented using components such as Nginx, firewalls, or specialized throttling components to protect the stability and security of the system.
Interface and user throttling:
In high-concurrency systems, interfaces and users need to be throttled to protect core resources and ensure quality of service. Different throttling policies can be set based on the importance of the interface and the user's access frequency, such as using the token bucket algorithm or the leaky bucket algorithm. By limiting the access rate of each user or interface, you can effectively control the load of the system and prevent the system from being overwhelmed by excessive requests.
Stand-alone interface and user-level throttling:
In a single-machine environment, throttling can be achieved by implementing data structures such as counters or queues in the application. By monitoring the frequency of requests from each interface and user, and throttling the current according to pre-set thresholds, the system can be effectively protected from overload.
Cluster user throttling:
In a distributed system, throttling needs to consider cross-node user access. You can use distributed cache or message queues to share user request information and perform rate-limiting calculations on each node in the cluster. By centrally managing user request data and throttling policies, you can ensure the stability and reliability of the entire cluster under high loads.
Cluster interface throttling:
For interfaces in a cluster, you need to consider the load of the entire cluster and dynamically adjust the throttling policy based on the actual situation. You can use cluster management tools or automated scripts to monitor the access status of interfaces and dynamically adjust the rate limiting parameters based on the system load and preset rate limiting policies. By responding to changes in cluster load in a timely manner, the stability and reliability of the system can be ensured under high load conditions.
3. Technology selection
docker
mysql
SpringBoot
- Fourth, the interface display
5. Source code address
Private correspondence: 98