Server performance optimization method

Abstract

The invention discloses a server performance optimization method, which comprises server end optimization, client end optimization, cache optimization, database optimization and cluster optimization, the server end optimization comprises data transmission optimization, underlying architecture optimization and coding optimization, the client end optimization comprises request management and API (Application Program Interface) call optimization, and the cluster optimization comprises data transmission optimization, underlying architecture optimization and coding optimization. The database optimization comprises index and table structure and SQL query optimization, and the cluster optimization comprises a service cluster, a database cluster, a MongoDB cluster and a Redis cluster. According to the method for optimizing the performance of the server, the data of the server can be automatically compressed, so that in the subsequent transmission process, the data volume of network transmission can be reduced, the database connection pool can consume memory resources, the memory is greatly saved, the service efficiency of the server is improved, and the service quality of the server is improved. And more customer services can be supported, and the server can load some required resources in advance, so that the feedback efficiency of the server is improved.

Description

Technical Field

[0001] The present invention relates to a server, and particularly to a method for optimizing server performance. Background Art

[0002] A server refers to a node with a fixed address that provides services to network users. It can improve access speed and also act as a firewall. Generally speaking, a server is a computer system in a network that can provide certain services to other machines (if a PC provides ftp services externally, it can also be called a server). Narrowly speaking, a server specifically refers to some high-performance computers that can provide services externally through a network. Servers run faster, have higher loads, and are more expensive than ordinary computers. In a network, a server provides computing or application services for other client machines (such as PC machines, smartphones, ATMs, and even large devices such as train systems). Servers have high-speed CPU computing capabilities, long-term reliable operation, powerful I / O external data throughput capabilities, and better scalability. Generally, according to the services provided by the server, the server has the ability to undertake response service requests, undertake services, and guarantee services.

[0003] However, when the server is running, the efficiency of data transmission will be slow due to the large amount of data, and when the client sends multiple access requests, the server cannot respond quickly due to a large accumulation of internal data, which will reduce the service efficiency of the server. Summary of the Invention

[0004] The main objective of the present invention is to provide a method for optimizing server performance, which can effectively solve the technical problems in the background art.

[0005] To achieve the above objective, the technical solution adopted by the present invention is as follows: A method for optimizing server performance includes server-side optimization, client-side optimization, cache optimization, database optimization, and cluster optimization. The server-side optimization includes data transmission optimization, underlying architecture optimization, and coding optimization. The client-side optimization includes request management and API call optimization. The database optimization includes index and table structure and SQL query optimization. The cluster optimization includes service clusters, database clusters, MongoDB clusters, and Redis clusters.

[0006] Further preferably, for the data transmission optimization, an instruction for automatic compression is set for the server. When the server receives data, the server automatically compresses the received data. In this way, in the subsequent transmission process, the amount of data transmitted over the network can be reduced, and useless fields in the response are removed during the transmission process, thereby achieving a streamlined response body and improving the transmission efficiency.

[0007] Further preferably, for the encoding optimization, data structures such as Map are used to improve the algorithm efficiency, appropriate type values are used to process data. For example, Byte type is used for status values, thread pools and database connection pools are used. In terms of thread pools, the number of threads in the thread pool is increased. Several threads are started first and these threads are all in the sleep state. When there is a new request from the client, one of the sleeping threads in the thread pool will be awakened to handle this request from the client. After processing this request, the thread will be in the sleep state again. This will save a large amount of system resources, enabling more CPU time and memory to be used for processing actual business applications instead of frequent thread creation and destruction. The database connection pool can consume memory resources, greatly saving memory and improving the service efficiency of the server, and being able to support more customer services. By using the connection pool, the program running efficiency will be greatly improved.

[0008] Further preferably, for the request management, the server conducts request verification on the requests sent by the client, directly intercepts some invalid requests, preloads some required resources, and uses CON to accelerate the access to static resources by means of the content delivery network.

[0009] Further preferably, for the API call optimization, the network round-trip times are reduced by combining multiple API requests into one batch request.

[0010] Further preferably, for the cache optimization, Redis is used to store frequently accessed data, and for hot data with a large amount of data, MongoDB can be used for storage.

[0011] Further preferably, for the index optimization, indexes are established for commonly queried fields to avoid the situation of index invalidation. The physical files in the BTree index are stored in the BTree structure, the data is stored in the leaf nodes, and a data linked list is formed through pointers to accelerate the retrieval efficiency of adjacent data; for the table structure optimization, more reasonable field types are used, such as using TinyInt for status values.

[0012] Further preferably, for the SQL query optimization, the pressure on a single database is reduced by separating read and write operations, and database sharding and table partitioning are performed according to business requirements, including database sharding by module, horizontal table partitioning, and vertical table partitioning.

[0013] Further preferably, for the service cluster, high availability and scalability of services are achieved through load balancing; for the database cluster, the read and write capabilities and fault tolerance of the database are improved. The service cluster combines multiple servers together to jointly handle the workload and provide a high-availability solution. By sharing the load and providing redundancy in the cluster, the server cluster can improve performance, scalability, and fault tolerance; for the Redis cluster, high availability and scalability are provided through the collaborative work of multiple Redis nodes. Each node in the cluster is treated equally, without a proxy node or a central node, and the data is automatically split and stored on multiple nodes.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, through optimization methods such as server-side optimization, client-side optimization, cache optimization, database optimization, and cluster optimization, the data on the server can be automatically compressed. In this way, during the subsequent transmission process, the amount of data transmitted over the network can be reduced. The database connection pool can consume memory resources, greatly saving memory and improving the service efficiency of the server, enabling it to support more customer services. The server can pre-load some required resources in advance, thereby improving the feedback efficiency of the server. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of a method for optimizing the performance of a server according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the technical means, creative features, achieved purposes, and functions of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0017] As Figure 1 shown, a method for optimizing the performance of a server includes server-side optimization, client-side optimization, cache optimization, database optimization, and cluster optimization. The server-side optimization includes data transmission optimization, underlying architecture optimization, and coding optimization. The client-side optimization includes request management and API call optimization. The database optimization includes index and table structure and SQL query optimization. The cluster optimization includes service cluster, database cluster, MongoDB cluster, and Redis cluster.

[0018] Furthermore, for the data transmission optimization, an instruction for automatic compression is set for the server. When the server receives data, the server automatically compresses the received data. In this way, during the subsequent transmission process, the amount of data transmitted over the network can be reduced, and useless fields in the response are removed during the transmission process, thereby achieving a streamlined response body and improving the transmission efficiency.

[0019] Furthermore, for the encoding optimization, data structures such as Map are used to improve the algorithm efficiency, appropriate type values are used to process data. For example, Byte type is used for status values. Thread pools and database connection pools are used. In terms of thread pools, the number of threads in the thread pool is increased. A certain number of threads are started first and these threads are all put into the sleep state. When there is a new request from the client, one of the sleeping threads in the thread pool will be awakened to handle this request from the client. After handling this request, the thread will be in the sleep state again. This will save a large amount of system resources, enabling more CPU time and memory to be used for processing actual business applications instead of frequent thread creation and destruction. The database connection pool can consume memory resources, greatly saving memory and improving the service efficiency of the server, and being able to support more customer services. By using the connection pool, the program running efficiency will be greatly improved.

[0020] Furthermore, for the request management, the server conducts request verification on the requests sent by the client, directly intercepts some invalid requests, preloads some required resources, and uses CON to accelerate the access to static resources by means of the content delivery network.

[0021] Furthermore, for the API call optimization, the number of network round trips is reduced by combining multiple API requests into one batch request, and the data that is frequently accessed and does not change frequently is cached to reduce the requests to the API. For example, information such as product lists and store settings can be cached. For data that is not immediately needed, lazy loading can be used. For example, the product details page can load relevant information only when the user clicks to view the details.

[0022] Furthermore, for the cache optimization, Redis is used to store the data that is frequently accessed. For hot data with a large amount of data, MongoDB can be used for storage; MongoDB storage refers to its own document storage (data in BSON format) and the GridFS external storage system. Its own document storage (data in BSON format) has a size limit, with a maximum of 16M; the GridFS external storage system is suitable for storing large files.

[0023] Furthermore, for the index optimization, indexes are established for commonly queried fields to avoid the situation of index invalidation. The physical files in the BTree index are stored in the BTree structure, the data is stored in the leaf nodes, and a data linked list is formed through pointers to accelerate the retrieval efficiency of adjacent data; for the table structure optimization, more reasonable field types are used, such as using TinyInt for status values.

[0024] Furthermore, for the SQL query optimization, the pressure on a single database is reduced by separating read and write operations, and database sharding and table partitioning are carried out according to business requirements, including database sharding by module, horizontal table partitioning, and vertical table partitioning.

[0025] Furthermore, for the service cluster, high availability and scalability of services are achieved through load balancing; for the database cluster, the read and write capabilities and fault tolerance of the database are improved. The service cluster combines multiple servers together to jointly handle the workload and provide a high-availability solution. By sharing the load and providing redundancy in the cluster, the server cluster can improve performance, scalability, and fault tolerance. By combining multiple servers together, the workload can be effectively dispersed, thereby improving the overall processing capacity. This distributed architecture enables the system to handle more requests while maintaining a low response time. The design of the service cluster allows the system to easily add or remove servers without interrupting the service. This flexibility enables the system to dynamically scale according to demand, thus better adapting to the changing business requirements. By configuring redundant servers in the cluster, the service cluster can automatically switch to the standby server when a single server fails, thereby ensuring the continuity and reliability of the service; for the Redis cluster, high availability and scalability are provided through the collaborative work of multiple Redis nodes. Each node in the cluster is treated equally, without a proxy node or a central node, and the data is automatically sharded and stored on multiple nodes.

[0026] The above has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing server performance, characterized in that: It includes server - side optimization, client - side optimization, cache optimization, database optimization, and cluster optimization. The server - side optimization includes data transmission optimization, underlying architecture optimization, and coding optimization. The client - side optimization includes request management and API call optimization. The database optimization includes index and table structure optimization and SQL query optimization. The cluster optimization includes service clusters, database clusters, MongoDB clusters, and Redis clusters.

2. The method for optimizing server performance according to claim 1, wherein: For the data transmission optimization, an instruction for automatic compression is set for the server. When the server receives data, it automatically compresses the received data. In this way, during subsequent transmission, the amount of data transmitted over the network can be reduced, and useless fields in the response are removed during transmission, thus achieving a streamlined response body and improving transmission efficiency.

3. The method for optimizing server performance according to claim 2, wherein: For the coding optimization, data structures such as Map are used to improve algorithm efficiency, and appropriate type values are used to process data. For example, Byte type is used for status values. Thread pools and database connection pools are used. In terms of thread pools, the number of threads in the thread pool is increased. A certain number of threads are started first and made to be in a sleeping state. When there is a new request from the client, one of the sleeping threads in the thread pool is awakened to handle this request from the client. After processing this request, the thread returns to the sleeping state. This will save a large amount of system resources, enabling more CPU time and memory to be used for processing actual business applications rather than frequent thread creation and destruction. The database connection pool can consume memory resources, greatly saving memory and improving the service efficiency of the server, and being able to support more client services. By using the connection pool, the program running efficiency will be greatly improved.

4. A method for optimizing server performance according to claim 3, characterized in that: For the request management, the server conducts request verification on requests sent by the client, directly intercepts some invalid requests, pre - loads some required resources, and uses CON to accelerate the access to static resources through the content delivery network.

5. The method for optimizing server performance according to claim 4, wherein: For the API call optimization, the number of network round - trips is reduced by combining multiple API requests into a batch request.

6. The method for optimizing server performance according to claim 5, wherein: For the cache optimization, Redis is used to store frequently accessed data. For hot data with a large amount of data, MongoDB can be used for storage.

7. A method for optimizing server performance according to claim 6, characterized in that: For the index optimization, indexes are established for commonly queried fields to avoid the situation of index invalidation. In a BTree index, the physical file is stored in a BTree structure, and the data is stored in the leaf nodes and connected by pointers to form a data linked list, which speeds up the retrieval efficiency of adjacent data. For the table structure optimization, more reasonable field types are used, such as using TinyInt for status values.

8. A method for optimizing server performance according to claim 7, characterized in that: For the SQL query optimization, the pressure on a single database is reduced through read - write separation, and database sharding and table partitioning are performed according to business requirements, including database sharding by module, horizontal table partitioning, and vertical table partitioning.

9. A method for optimizing server performance according to claim 8, characterized in that: The service cluster achieves high availability and scalability of services through load balancing; the database cluster improves the read and write capabilities and fault tolerance of the database. The service cluster combines multiple servers together to jointly handle workloads and provide a high-availability solution. By sharing the load and providing redundancy in the cluster, the server cluster can improve performance, scalability, and fault tolerance; the Redis cluster provides high availability and scalability through the collaborative work of multiple Redis nodes. Each node in the cluster is treated equally, without a proxy node or a central node, and the data is automatically split and stored on multiple nodes.

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