The system posts a hot news item and everyone accesses it, causing the system to slow down, freeze or even crash. How do you deal with it?

The system posts a hot news item and everyone accesses it, causing the system to slow down, freeze or even crash. How do you deal with it?

If this situation occurs in the system, the following measures need to be taken:

1. Increase server resources: You can increase server resources in a short time and increase bandwidth, memory and other resource allocation to support more user access.

2. Adjust the database index: If the database is the bottleneck, you can try to optimize the database query statement, add indexes and other operations to speed up the query.

3. Turn on caching: You can use caching technology to reduce access to system resources, such as using caching services such as Redis to reduce system pressure.

4. Current limiting control: Traffic limiting and other measures can be used to control user access traffic to avoid further system collapse.

5. Introducing load balancing: User requests can be distributed to multiple servers, and load balancing technology can be used to balance the load of the server and reduce the pressure on a single server.

6. Monitoring and early warning: It is necessary to monitor the operating status of the system in a timely manner, detect problems in a timely manner and deal with them. It is also necessary to set up some early warning mechanisms to prevent no one from responding when serious failures occur.

The above measures can be carried out at the same time. It requires detailed analysis of specific problems and comprehensive consideration of the system's situation and operating status to select the most appropriate measures to avoid system crashes.

If the system cannot handle high concurrent access, it will cause system performance degradation, prolonged response time, and even system crash. To handle high concurrent access, we need to start from many aspects.

First, you need to consider whether the system architecture can support high concurrent access. A distributed architecture is usually adopted to achieve horizontal expansion by increasing the number of servers and improve the system's processing capabilities.

Secondly, system performance optimization needs to be considered, including database optimization, cache use, code optimization, etc. Optimizing the database structure, adding indexes, optimizing SQL statements, etc. can reduce database access time; using caching can reduce database access and improve system response speed; code optimization can improve code execution efficiency, thereby improving system performance. .

In addition, the fault tolerance and load balancing capabilities of the system need to be considered. By increasing the number of servers and distributing requests to different servers, single points of failure can be avoided and system availability can be improved; the load balancing algorithm can be used to balance the load of each server, thereby avoiding excessive load on some servers.
In short, dealing with high concurrency requires comprehensive consideration of the system's architecture, performance optimization, fault tolerance, load balancing capabilities and other aspects to ensure high availability and high performance of the system.

Excessive system access leads to system crash, which is a common problem. Here are some possible solutions.

1. Horizontal expansion
   Horizontal expansion is a method of increasing system processing capabilities by increasing the number of servers. Horizontal expansion can be achieved by building a cluster. When the system access volume is too large, the system's processing capability can be improved by increasing the number of cluster nodes. Through horizontal scaling, system load can be balanced across multiple servers to avoid single points of failure.

2. Vertical Scaling
   Vertical scaling is a method of increasing the processing power of a system by increasing the processing power of a single server. Vertical expansion can be achieved by increasing the server's hardware resources such as CPU and memory. When the system access volume is too large, the system's processing capabilities can be improved by upgrading the server hardware. The advantage of vertical expansion is that it can increase the processing power of a single server, but the cost is relatively high.

3. Cache optimization
   Cache optimization is to cache some commonly used data to avoid frequent access to the database, thereby improving the response speed of the system. Various caching solutions can be used, such as Redis, Memcached, etc., to cache data in memory. Cached data can be static data or dynamic data. Cache optimization can greatly reduce the pressure on the database and improve system performance.

4. Database optimization
   Database optimization is to improve the performance of the database by optimizing the structure, index, SQL statements, etc. of the database, thereby improving the response speed of the system. The database can be optimized by reasonably designing the database structure, adding indexes, optimizing SQL statements, etc. Database optimization can greatly reduce database access time and improve system performance.

5. Asynchronous processing
   Asynchronous processing improves the response speed of the system by processing certain time-consuming operations asynchronously without blocking the normal operation of the system. Asynchronous processing can be implemented using asynchronous message queues, asynchronous tasks, etc. Asynchronous processing allows the system to handle a large number of requests without blocking, thereby avoiding system crashes.

Cause Analysis:

1. Database query: Database query is the bottleneck in many applications. If the amount of data queried is large or multiple tables need to be associated, the query will take a long time, causing the interface request to time out.
2. Third-party interface: The application may call a third-party interface. If the third-party interface takes a long time to respond or an error occurs, it will cause the application's interface request time to become longer.
3. Large file upload: In some scenarios, large files need to be uploaded. If the file is large, the upload time will be very long.
4. Network request: If the application needs to send a network request to a remote server, and the network speed is very slow or the requested server takes a long time to respond, it will cause the interface request time to become longer.
5. Code logic: In some cases, code logic may cause the program to enter an infinite loop or block for a long time, resulting in a longer interface request time.
6. System load: If the system load is high, such as when the server hardware resources are insufficient or the system fails, the program may be affected, resulting in longer interface request times.
7. Database connection pool: If the database connection pool used by the program is not set appropriately, it may cause all connections in the connection pool to be exhausted, resulting in longer interface request times.
8. Memory leak: If there is a memory leak in the program, it may cause the program to continuously consume memory, eventually causing the program to crash or the interface request time to become longer.
9. Thread pool: If the thread pool used by the program is not set properly, it may cause all threads in the thread pool to be exhausted, resulting in longer interface request time.
10. Configuration errors: If there are configuration errors in the program, such as configuring an incorrect port number or database connection string, the program may be unable to connect to the database or respond to interface requests, resulting in longer interface request times.
11. Concurrent access: If the program needs to handle concurrent requests, but the thread pool or cache mechanism is not set properly, it may lead to thread competition or low cache hit rate, thus affecting the interface request time.
12. Slow recursion: In some cases, the recursive algorithm may cause the program to enter an infinite loop or the recursion depth is too large, resulting in longer interface request time.
13. Garbage collection: The garbage collection mechanism in Java programs will have a certain impact on the application. If garbage collection is frequent or takes a long time, it may cause the interface request time to become longer.
14. Network congestion: Insufficient network bandwidth or improper network device configuration may cause network congestion, thus affecting the response time of interface requests.
15. Service deployment: If the program is deployed on a server, but the server's hardware configuration is insufficient or system resources are insufficient, it may cause the program to run slowly, thus affecting the interface request time.
16. Security issues: If the program has security issues, such as SQL injection or cross-site scripting attacks, the program may be exploited by attackers, thus affecting the response time of interface requests.
17. Program version upgrade: When the program version is upgraded, it may involve changes to the data structure or interface. If the upgrade process is not handled properly, the program may not be able to respond to interface requests, thus affecting the interface request time.
18. Exception handling: If the program does not handle exceptions well, it may cause the program to enter an infinite loop or be unable to respond to interface requests, thus affecting the interface request time.
19. System configuration: If the system where the program is located is not properly configured, such as an insufficient number of file descriptors or a process priority that is too low, the program may be unable to respond to interface requests, thus affecting the interface request time.
20. Logging: If there are too many log records during the running process of the program or the location of the log records is inappropriate, the performance of the program may be degraded, thereby affecting the interface request time.
21. Database transaction: Database transaction is a mechanism to ensure data consistency and integrity. If the program involves database transactions when processing interface requests, the transaction processing time may affect the interface request time.
22. Lock competition: If the program involves lock competition in a concurrent scenario, such as multiple threads accessing shared resources at the same time, the lock acquisition and release time may affect the interface request time.
23. Code optimization: The degree of optimization of the program code will also affect the interface request time. If the program is not well optimized, it may cause the interface request time to be longer.
24. Caching mechanism: If the program uses a caching mechanism to improve performance, but the cache hit rate is low or the cache is not cleared in time, the interface request time may become longer.
25. Preheating data: If the program needs to preheat data before running, such as loading configuration files or initializing database connections, it may affect the response time of interface requests.
26. Network failure: If there is a failure in the network environment where the program is located, such as network device failure or insufficient network bandwidth, the interface request time may become longer.
27. System load balancing: If the program is deployed on multiple servers, but the load balancing mechanism is not set properly, requests may be distributed to the same server, resulting in longer interface request times.
28. Security authentication: If the program involves security authentication, such as user login or authorization, it may cause the interface request time to become longer.
29. Memory limit: If the system where the program is running has insufficient memory resources, it may cause the program to be unable to process a large amount of data or respond to interface requests, thus affecting the interface request time.
30. Server performance: If the program is deployed on a server with lower performance, it may cause the program to run slowly, thus affecting the interface request time.