High concurrency integration of PHP and database

As a PHP developer, we often need to deal with databases. In the face of high concurrency, we need to optimize the design and use of the database to avoid performance problems. This article will introduce some common high-concurrency integration methods between PHP and database so that developers can better cope with high-concurrency scenarios.

1. Database connection pool

The connection from a PHP script to the database server is usually time-consuming because it requires establishing a TCP connection, performing authentication and other operations. When high concurrency scenarios occur, each request will create a new database connection, which will cause resource consumption and response time delays on the server side. Therefore, we can use database connection pooling to avoid this problem.

The principle of the database connection pool is to initialize multiple database connections when the program starts and store these connections in the connection pool. When a new request arrives, obtain an idle connection from the connection pool, and then release the connection back to the connection pool after use. This eliminates the need to create a new database connection with every request, improving server efficiency. We can use PHP extensions such as phpredis or Pdo_mysql_pool to implement connection pooling.

2. Database master-slave replication

Separation of data reading and writing is an important method to improve concurrent access to the database. In a high-concurrency environment, for scenarios where there are many reads and few writes, the slave library can be used to read data, and the master library can be used to write data. This avoids database performance problems caused by a large number of read operations.

Database master-slave replication can be achieved by configuring the relationship between the master database and the slave database. During this process, the master library is responsible for processing all write operations, and the slave library is responsible for processing read operations. When reading data, connect by specifying the IP address of the slave library. Of course, when using master-slave replication, you need to pay attention to the data delay issue and load balancing of the slave database.

3. Using cache

Cache is an important means to improve performance. Common applications include caching database connections, caching query results, caching page output, etc. When caching is in effect, the response time of client requests will be greatly reduced, thereby improving the user experience.

When using cache, we can use a cache server such as Memcached or Redis. Memcached is a high-performance, distributed caching system, while Redis provides support for complex data structures and more powerful caching capabilities.

4. Table partitioning

When the data table in the database becomes larger and larger, the efficiency of querying the table will be affected. For tables with temporal or certain clear business groupings, we can partition the table to improve query efficiency.

Table partitioning divides the data table into multiple small tables according to specific rules, and each small table only contains a part of specific data. In this way, only a specific small table needs to be queried when querying, thereby avoiding the efficiency problem of querying the entire large table. It is worth noting that business scenarios and data structure design need to be considered before table partitioning.

Summary

In the process of high-concurrency database access, we need to pay attention to the use of connection pools, the establishment of master-slave replication, the storage of cached data, and the use of table partitions and other techniques to optimize Database performance. At the same time, we need to consider the specific circumstances of the business scenario and the complexity of the design, and apply it flexibly based on the actual situation. Only in this way can we better deal with database access problems in high concurrency scenarios.

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