Performance optimization method of distributed trade system oriented to high concurrent transactions

Abstract

The invention relates to a performance optimization method of a distributed trade system oriented to high-concurrency transactions, and aims to improve the processing efficiency and stability of the system in a high-concurrency environment. According to the method, the system bottleneck in the high-concurrency transaction scene is analyzed, and the load balancing strategy is optimized by adopting the consistent Hash algorithm, so that the balance and the stability of the server nodes in load distribution are ensured. Meanwhile, the distributed cache technology is designed, the data access speed is increased, and the system response delay is reduced. Through the combination of the multi-level cache and the data prefetching technology, the efficient processing of transaction data and the improvement of the cache hit rate are realized. In addition, the system dynamically adjusts load balancing and caching strategies according to real-time performance monitoring data, and stable operation of the system under the high-concurrency condition is ensured. According to the technical scheme, the transaction processing efficiency is remarkably improved, the response time is shortened, the user experience is improved, and the method is particularly suitable for a distributed trade management system for processing large-scale and high-frequency transactions.

Description

Technical Field

[0001] The present invention relates to a performance optimization method for a distributed trading system for high-concurrency transactions, which is particularly suitable for large-scale, high-frequency trading scenarios in the steel industry, and belongs to the field of distributed computing and network technology. Specifically, the present invention relates to enhancing the stability and response speed of the system in a high-concurrency trading environment by optimizing load balancing strategies and improving data access efficiency, so as to meet the needs of steel trading companies for real-time transaction processing and efficient resource management. Background Art

[0002] With the rapid development of the steel industry and the continuous expansion of the global market, steel trading companies are facing increasing transaction volumes and increasingly complex trade networks. In this context, how to ensure the stability and efficiency of the system in a high-concurrency transaction environment has become a key challenge in enterprise supply chain management. Traditional trade management systems are often based on a centralized architecture, which is difficult to adapt to the frequent and high-concurrency transaction needs in the steel industry, resulting in frequent system bottlenecks, reduced response speed, and even possible system crashes.

[0003] Especially under the current wave of digital transformation, the steel industry has gradually designed advanced technologies such as big data analysis, Internet of Things (IoT), and artificial intelligence (AI). Although these technologies have improved the intelligence and automation of business processes, they have also put forward higher requirements on the performance of trade management systems. In high-concurrency transaction scenarios, how to effectively process massive data, quickly respond to user requests, and ensure the high availability and reliability of the system has become an urgent problem that needs to be solved.

[0004] The currently used distributed systems and load balancing technologies often fail to fully realize their potential when faced with the unique business model and transaction requirements of the steel industry. Traditional load balancing algorithms such as polling and minimum number of connections, although they perform well in ordinary application scenarios, often fail to perform well in special scenarios such as steel trade, where data volumes are large and transactions are frequent. The system's response delay and insufficient data processing capabilities directly affect the company's operational efficiency and market competitiveness.

[0005] In order to solve the system bottleneck problem in high-concurrency transaction scenarios, the industry has proposed a variety of technical solutions in recent years, among which distributed architecture and caching technology have gradually become the mainstream choice for optimizing system performance. In distributed systems, load balancing and data caching mechanisms are core technologies and are widely used to improve the processing power and response speed of the system. However, the existing load balancing strategies and caching mechanisms still have many shortcomings when facing the high-concurrency transaction requirements of the steel industry.

[0006] First, traditional load balancing strategies are difficult to achieve refined resource allocation in complex and ever-changing trading environments. Although the common consistent hashing algorithm can balance server loads well, it may still cause some nodes to be overloaded or have insufficient resource utilization when facing dynamic transaction requests. In addition, the efficiency and accuracy of load balancing strategies still need to be improved when dealing with distributed deployments across regions and multiple data centers, which directly affects the overall performance of the system and user experience.

[0007] Secondly, existing cache technologies mainly focus on single-level cache management, which often cannot meet the needs of high-frequency, large-volume transaction processing in the steel industry. In a high-concurrency environment, the hit rate of a single-level cache is low, and frequent cache failures will cause a large number of data requests to enter the database directly, which in turn causes excessive database load and a significant increase in system response time. Especially when processing transaction data, cache failures may also lead to data inconsistency and processing delays, seriously affecting the smooth progress of transactions and the stability of the system.

[0008] In order to meet the above challenges, a performance optimization technology that combines advanced load balancing algorithms with multi-level cache mechanisms is urgently needed to effectively improve the processing capacity and stability of distributed trade management systems in high-concurrency trading environments. By designing smarter load balancing strategies and multi-level cache management, the system can better adapt to the complex trading needs of the steel industry, achieve efficient data processing and fast transaction response, thereby improving the overall operational efficiency and market competitiveness of the enterprise. Summary of the invention

[0009] The purpose of this invention is to provide a performance optimization method for a distributed trade management system in a high-concurrency trading environment, which is particularly suitable for large-scale, high-frequency trading scenarios in the steel industry. This method aims to solve the performance bottleneck problem faced by the distributed trade management system in the steel industry under high-concurrency conditions by designing a hybrid load balancing technology and an efficient caching mechanism.

[0010] The present invention proposes a load balancing strategy based on a consistent hashing algorithm, which can dynamically adjust the load of each node in the system when the transaction flow fluctuates greatly, ensuring the optimal utilization of resources. In addition, the present invention also greatly improves the data access speed of the system and significantly reduces the response delay by designing distributed cache technology and multi-level cache management strategy, combined with data pre-fetching technology.

[0011] The technical solution of the present invention is tailored for the steel industry. By optimizing the stability and processing efficiency of the system under high-concurrency transaction conditions, it not only effectively improves the transaction processing speed and user experience, but also provides strong support for the digital transformation of the steel industry. The present invention has broad application prospects and can be widely used in other high-concurrency transaction scenarios including but not limited to the steel industry, such as financial trading platforms, large-scale e-commerce systems, etc.

[0012] The technical solution of the present invention comprises the following main steps:

[0013] System initialization and configuration:

[0014] Deploy the infrastructure of the distributed trade management system and build a distributed network environment that supports high-concurrency transactions. By designing a consistent hashing algorithm, design and configure a hybrid load balancing module, it can dynamically adjust the load distribution of each node in the system to cope with fluctuations in transaction traffic.

[0015] During the system initialization phase, an efficient cache mechanism is also configured to ensure fast data access through distributed cache and multi-level cache technology under high system load. The design of the multi-level cache management strategy includes the first-level cache (L1) for storing recently frequently accessed data and the second-level cache (L2) for storing batch pre-fetched data.

[0016] Implementation of load balancing strategy:

[0017] The present invention realizes dynamic adjustment of system load through consistent hashing algorithm. Specifically, when the system faces high concurrent transactions, the load balancing module automatically adjusts the workload of each node according to the real-time monitoring data to ensure that the transaction requests can be evenly distributed among multiple nodes.

[0018] During the load balancing process, the system will also automatically detect and avoid node bottlenecks caused by excessive load, ensure the balance of processing power among nodes, and prevent the overall performance of the system from degrading due to overload of a certain node.

[0019] Application of efficient caching mechanism:

[0020] The present invention designs a distributed cache technology in the system, and reduces the delay problem caused by data access by distributing the cache management among the nodes of the system. In the specific implementation process, by combining the multi-level cache strategy with the data pre-fetching technology, the system can predict the data that may be accessed in the future according to the user's access behavior and load it into the cache in advance, thereby improving the cache hit rate and reducing data access delay.

[0021] Through the multi-level caching strategy, the system can effectively distinguish the data access frequencies at different levels, reasonably allocate cache resources, further optimize the data reading speed, and reduce the direct access pressure on the underlying database.

[0022] Through the above implementation steps, the technical solution of the present invention systematically improves the overall performance of the distributed trade management system in the steel industry in a high-concurrency transaction environment, ensuring the stability and response speed of the system when processing large-scale, high-frequency transactions.

[0023] The performance optimization method of the distributed trade management system for high-concurrency transactions proposed in this invention significantly improves the processing capability and operation efficiency of the system in a high-concurrency transaction environment through a series of innovative technical means and strategy designs. The specific technical effects and advantages are as follows:

[0024] Improve the system's load balancing capabilities:

[0025] Through the application of consistent hashing algorithm, the system can dynamically adjust the load distribution when processing high-concurrency transactions to avoid system performance bottlenecks caused by overloading of a certain node. This load balancing strategy not only improves the overall processing capacity of the system, but also reduces the processing time of transaction requests, ensuring the system's response speed when facing sudden traffic.

[0026] Accelerate data access and reduce response delays:

[0027] The present invention significantly improves the speed of data access by combining distributed cache technology with multi-level cache management strategy. Through pre-fetching and cache hierarchical management of access data, the system achieves a high cache hit rate, thereby reducing the need for direct access to the database and reducing the delay of data response.

[0028] The multi-level cache design not only enables a quick response to frequently accessed data, but also provides an effective pre-processing strategy for access to infrequently used data. This optimization of the cache mechanism enables the system to maintain efficient operation even under high load conditions.

[0029] Ensure the stability of the system in a high-concurrency environment:

[0030] By comprehensively applying load balancing, distributed caching and multi-level caching strategies, the present invention effectively improves the stability of the system in a high-concurrency environment. The system can automatically adapt to changes in transaction flow, dynamically adjust the workload of each node, and reduce direct pressure on the database through a caching mechanism, avoiding system crashes or performance degradation caused by high-concurrency access.

[0031] In addition, the optimization scheme of the present invention ensures the security and reliability of the system when processing large-scale transactions through reasonable resource allocation and scheduling, providing a solid technical guarantee for large-scale trade management in the steel industry.

[0032] Improve user experience and transaction efficiency:

[0033] The technical solution of the present invention not only makes a breakthrough in system performance, but also significantly improves the user's transaction experience. Since the system can quickly respond to user requests, it reduces transaction failures or poor experience caused by delays, thereby improving overall user satisfaction.

[0034] Efficient data processing and caching mechanisms make the transaction process smoother, reduce user waiting time, improve the efficiency of the entire transaction chain, and ultimately bring higher operational benefits to the enterprise.

[0035] To sum up, the present invention provides a comprehensive performance optimization solution for the distributed trade management system in the steel industry in a high-concurrency transaction environment through the load balancing strategy of the consistent hashing algorithm, the efficient distributed caching mechanism, and the organic combination of multi-level caching and data pre-fetching technology, ensuring the stable and efficient operation of the system in complex transaction scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 :The overall architecture flow chart of the distributed trade management system for high-concurrency transactions of the present invention shows the data flow between modules and the process of the system processing high-concurrency transaction requests.

[0039] Figure 2 : Schematic diagram of the application of consistent hashing algorithm in load balancing, showing the connection between multiple server nodes in the hash ring and the data distribution when the nodes change.

[0040] Figure 3 : Schematic diagram of the working principle of distributed caching technology, showing the collaborative process of multi-level caching and data prefetching and cache hit mechanisms.

[0041] Figure 4: Flowchart of the optimization feedback mechanism, showing the workflow of system performance monitoring and dynamic adjustment strategy. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0044] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0045] In a distributed trade management system with high concurrent transactions, system performance and reliability have always been crucial issues. When faced with a large number of concurrent requests, how to effectively allocate system resources, reduce response delays, and improve the overall throughput of the system has become the core goal of system design. Traditional distributed systems often experience performance bottlenecks and system instability when faced with high loads, which not only affects the user experience, but also seriously restricts the system's scalability.

[0046] To this end, the present invention proposes a performance optimization method based on hybrid load balancing and efficient caching mechanism, which is specifically aimed at the system bottleneck problem in a high-concurrency transaction environment. The method optimizes the load balancing strategy by designing a consistent hashing algorithm to ensure the reasonable allocation of system resources and load balancing. At the same time, the distributed caching technology is adopted to significantly speed up data access speed and reduce system response delay by combining multi-level caching with data pre-fetching, thereby improving the overall performance of the system.

[0047] The technical solution of the present invention can not only ensure the stable operation of the system under high concurrency conditions, but also significantly improve the efficiency of transaction processing and user experience. Through the integration of these innovative technologies, the system has demonstrated excellent performance and reliability when dealing with large-scale concurrent requests, providing an effective solution for distributed trade management systems.

[0048] Next, various embodiments of the present invention will be described in detail, including an efficient distributed transaction management method based on hybrid load balancing, an efficient data access method based on a multi-level cache mechanism, a transaction data processing and storage method based on a distributed architecture, and a system performance monitoring and adaptive optimization mechanism. These embodiments will be combined with the accompanying drawings to fully demonstrate the technical advantages of the present invention and its application scenarios.

[0049] Example 1

[0050] This embodiment provides a distributed transaction management method based on hybrid load balancing, aiming to improve the response efficiency and stability of the system in a high-concurrency transaction environment. Figure 1 , the method comprises the following steps:

[0051] S101, the user interface module receives a transaction request from a user. The user interface module is used to process various requests from users, including transactions, queries and other operations. After receiving the user request, the request information is passed to the load balancing module.

[0052] S102, the load balancing module distributes the requests through the consistent hashing algorithm according to the characteristics of the requests and the resource usage of the current system. The algorithm can effectively distribute user requests evenly to multiple server nodes to avoid the overload problem of a single node. Through the consistent hashing algorithm, the system can achieve dynamic load balancing, thereby improving the processing capacity and stability of the overall system.

[0053] S103, the transaction management module is responsible for processing the requests assigned by the load balancing module. The transaction management module performs specific transaction operations, including reading, writing, and updating data. The module is also responsible for interacting with the data storage module to ensure the accuracy and consistency of transaction data.

[0054] S104, the data storage module is used to store and manage transaction data. The data storage module adopts a distributed architecture design to ensure high availability and reliability of data. When processing user requests, the transaction management module will perform corresponding operations on the data in the data storage module according to the request type.

[0055] S105, the distributed cache module is used to accelerate the data access process. This module combines the multi-level cache strategy and data pre-fetching technology to significantly improve the speed of data access. After receiving the request from the transaction management module, the distributed cache module first checks whether the required data exists in the cache. If so, the cached data is directly returned; if not, the data is obtained from the data storage module and cached.

[0056] Through the above steps, the system in this embodiment can achieve reasonable load distribution and improved data access speed when processing high-concurrency transaction requests, significantly reduce the system response time, and improve the overall system efficiency and user experience.

[0057] The innovation of this embodiment is that the load balancing strategy is optimized through the consistent hashing algorithm, and a distributed cache mechanism is designed, thereby effectively solving the system performance bottleneck problem in a high-concurrency transaction environment. This method not only improves the stability of the system under high-load conditions, but also greatly reduces response delays and enhances the scalability and maintainability of the system.

[0058] Example 2

[0059] This embodiment provides a distributed data management method based on multi-level cache and data pre-fetching technology, aiming to improve data access efficiency in a high-concurrency transaction environment. Figure 2 , the method comprises the following steps:

[0060] S201, the user request module receives and processes requests from users. This module is responsible for receiving various requests submitted by users, including data query, transaction operations, etc., and passing the requests to the consistent hashing algorithm module for further processing.

[0061] S202, the consistent hash algorithm module selects a suitable cache node for data access based on the characteristics of the request and the current cache state. The consistent hash algorithm can ensure that requests are evenly distributed among multiple cache nodes, avoiding the overload problem of a single node, thereby improving the balance of data access.

[0062] S203, the cache hit module is used to check whether the requested data already exists in the cache. If the data already exists in the cache, the data is directly read from the cache and returned to the user request module to speed up the data response speed.

[0063] S204, the cache miss module is responsible for processing data requests that do not exist in the cache. When a cache miss occurs, the system will obtain data from the distributed cache module according to a preset strategy and cache the data for subsequent requests.

[0064] S205, the distributed cache module uses a multi-level cache mechanism for data storage. The module includes a first-level cache and a second-level cache to improve the hit rate of data. The first-level cache is mainly used to store frequently accessed data and provides the fastest access speed; the second-level cache is used to store relatively less accessed data as a supplement to the first-level cache.

[0065] S206, the database storage module is responsible for storing and managing all persistent data. When data is not found in the cache, the system will retrieve the data from the database storage module and store it in the distributed cache module according to the cache strategy.

[0066] The key innovation of this embodiment is the design of a multi-level cache mechanism and data pre-fetching technology, which optimizes the efficiency of cache usage by storing data with different access frequencies in a hierarchical manner. In addition, cache requests are allocated through a consistent hashing algorithm to achieve dynamic load balancing of the cache, effectively improving the data access performance of the system in a high-concurrency environment.

[0067] To better describe how the consistent hashing algorithm works, it can be formulated as:

[0068]

[0069] Among them, h(K) represents the hash function value, K is the keyword of the request, a and b are the parameters of the hash function, m is a large prime number, and n is the total number of cache nodes. Through this formula, the request can be mapped to a specific cache node to achieve load balancing.

[0070] In addition, in order to optimize the cache strategy, a data prefetching technology is designed. The prefetching model is calculated using the following formula:

[0071] Prediction value = α × current access frequency + (1-α) × historical access frequency

[0072] Among them, a is a tuning parameter between 0 and 1, which is used to balance the impact of current access frequency and historical access frequency. Through this prefetching strategy, the system can intelligently determine which data is more likely to be accessed in the future, so as to load it into the cache in advance and reduce the latency of future requests.

[0073] By combining the above formulas and methods, this embodiment significantly improves the data processing efficiency of the system in a high-concurrency environment, reduces response time, and optimizes user experience.

[0074] Example 3

[0075] This embodiment provides an efficient data processing method based on distributed computing and data pre-fetching technology, aiming to solve the problem of large-scale data processing and storage in a high-concurrency environment. Figure 3, the method comprises the following steps:

[0076] S301, the data access request module is used to receive and manage all data access requests. The main function of this module is to classify various types of data access requests of users and pass them to the multi-level cache module for further processing.

[0077] S302, the multi-level cache module includes a first-level cache and a second-level cache. The first-level cache is used to store the most frequently accessed data to provide the fastest access speed; the second-level cache stores relatively less frequently accessed data as a supplement to the first-level cache. After receiving a data access request, the module first searches for data in the first-level cache, and directly returns if a hit is found; if a hit is found, the module continues to search in the second-level cache.

[0078] S302.1, Level 1 cache module: stores the most frequently accessed data and uses high-speed memory technology to quickly read data, ensuring that user requests can be responded to quickly.

[0079] S302.2, Level 2 cache module: stores data with low access frequency as a supplement to Level 1 cache. This module provides a relatively slow but still fast access speed through a larger capacity storage medium.

[0080] S303, the data processing module is responsible for processing the data of the cache miss request. This module uses distributed computing technology to distribute large-scale data processing tasks to multiple nodes for parallel execution to ensure data processing efficiency under high concurrent requests.

[0081] S304, the data pre-fetching module is used to predict and pre-load data that may be accessed to improve the cache hit rate. The module uses an adaptive algorithm to predict data access based on historical data access patterns and real-time analysis results, thereby reducing the delay of future requests.

[0082] S305, the database storage module is responsible for storing and managing all persistent data. When the data is not hit in the cache, the system will retrieve the data from the database storage module and store it in the multi-level cache module according to the preset cache strategy to optimize the processing speed of subsequent requests.

[0083] The key innovation of this embodiment is to combine distributed computing with data prefetching technology, improve the computing efficiency of the system in a high-concurrency environment through distributed processing, and significantly improve the response speed of data access through multi-level caching and data prefetching technology.

[0084] In order to better describe the application of distributed computing in data processing, this embodiment adopts the MapReduce model, and the specific formula is as follows:

[0085] Map(K,V)→LList(K,V)

[0086] Reduce(K′,LList(V′))→V′

[0087] The Map function is used to map the input key-value pair (K, V) to a new set of key-value pairs (K′, V′), while the Reduce function reduces the value list with the same key K′ to generate the final output result V′. Through this model, the system can efficiently process large-scale data and distribute computing tasks to multiple nodes for parallel execution, significantly improving the speed of data processing.

[0088] In addition, the data pre-fetching technology in this embodiment is described by the following formula:

[0089] P(x t+1 )=α×x t +(1-α)×x t-1

[0090] Among them, P(x t+1 ) represents the predicted value of data access at the next moment, x t is the access frequency at the current moment, x t-1 is the access frequency at the previous moment, and 9 is the adjustment parameter. Through this formula, the system can intelligently predict future data access trends and load the data that may be accessed into the cache in advance to reduce the latency of future requests.

[0091] This embodiment significantly improves the data processing efficiency and response speed of the system in a high-concurrency environment by combining the above formulas and methods, and is suitable for distributed systems that process large-scale and complex data.

[0092] Example 4

[0093] This embodiment proposes an efficient cache management method that combines dynamic load balancing and optimized feedback mechanism, aiming to improve the performance of distributed trade management system in a high-concurrency transaction environment. Figure 4 , the method comprises the following steps:

[0094] S401, the transaction request module is used to receive and manage all user transaction requests. Whenever the system receives a transaction request, the module will first parse the request and determine further processing strategies based on the request type and content.

[0095] S402, the load balancing module uses the consistent hashing algorithm to distribute transaction requests. Through the consistent hashing algorithm, the system can evenly distribute transaction requests to different cache nodes, thereby avoiding the overload of a certain node and ensuring the stable operation of the system under high concurrency. The algorithm can be specifically expressed as:

[0096] H(k)=(H(k)+i×hash step)modN

[0097] Among them, H(k) represents the hash value of the transaction request, i is the number of rehashing, hash step is the step length, and N is the total number of cache nodes. This algorithm can minimize the scope of data redistribution when nodes are added or deleted, thereby improving the scalability of the system.

[0098] S403, the distributed cache module is used to store and manage transaction data. This module maps the transaction requests assigned by the load balancing module to the corresponding cache nodes and stores the transaction data according to the preset cache strategy. Distributed cache improves the system's processing capacity through multi-node storage and reduces the load pressure of a single node.

[0099] S404, the data processing module receives the data returned from the cache node and processes it accordingly. This module is responsible for executing the transaction logic, updating the transaction status, and feeding back the results to the user interface module after the processing is completed. The operation of this module can ensure the system's rapid response to user requests and timely update the system status after the transaction is completed.

[0100] S405, the optimization feedback module is used to dynamically adjust the load balancing strategy and cache management strategy according to the system operation status and performance monitoring data. This module monitors the key performance indicators of the system in real time (such as CPU usage, memory occupancy, network latency, etc.) and performs feedback tuning on the system to ensure that the system can maintain optimal performance under various load conditions. The specific implementation of optimization feedback can be implemented using the following formula:

[0101]

[0102] Among them, ΔW represents the load adjustment weight, λ is the adjustment parameter, currentload is the current load, and optimalload is the optimal load value preset by the system. Through this feedback tuning mechanism, the system can adaptively adjust the load balancing and caching strategies to cope with dynamically changing transaction loads and maximize system performance.

[0103] The innovation of this embodiment lies in the deep integration of dynamic load balancing and optimization feedback mechanism, so that the system can dynamically adjust resource allocation and caching strategies according to actual operating conditions, thereby maintaining stable and efficient operation in a high-concurrency transaction environment.

[0104] In summary, this embodiment achieves performance optimization of the distributed trade management system in a high-concurrency transaction environment through the organic combination of consistent hashing algorithm, distributed cache and optimized feedback mechanism. This method not only improves the response speed and processing capacity of the system, but also enhances the adaptability and stability of the system, and is suitable for the performance optimization needs of various large-scale distributed systems.

[0105] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

[0106] Technical terms that need to be explained to help understand the present invention

[0107] (1) High-concurrency transaction environment: refers to the scenario where the system needs to process a large number of user requests and transaction operations at the same time. This environment places high demands on the system's processing capabilities, response speed, and stability.

[0108] (2) Distributed trade management system: refers to a system designed based on a distributed architecture, which is used to manage and process trade-related operations across multiple nodes or servers. The system uses distributed computing and storage technologies to achieve efficient management of large-scale data and transaction requests.

[0109] (3) Load Balancing: refers to evenly distributing workloads (such as transaction requests, data processing tasks, etc.) to multiple computing nodes or servers to avoid overloading a single node, thereby improving system performance and stability. The present invention uses a consistent hashing algorithm to achieve load balancing.

[0110] (4) Consistent Hashing Algorithm: A special hashing algorithm widely used in distributed systems. This algorithm can reduce the scope of data redistribution when nodes are added or deleted, thereby improving the scalability and fault tolerance of the system.

[0111] (5) Distributed Cache: refers to a caching mechanism that stores data in a distributed manner across multiple nodes. The purpose is to speed up data access, reduce database pressure, and improve the overall responsiveness of the system.

[0112] (6) Data Prefetching: An optimization technique that reduces data access latency by loading data into the cache before the data is actually requested. This technique is often used in conjunction with distributed caching to improve cache hit rates.

[0113] (7) Cache Hit Ratio: refers to the ratio of the number of successful accesses to the total number of accesses to the data items in the cache in the cache system. A higher cache hit ratio means that the system can respond to user requests faster and reduce the need to access the underlying storage system.

[0114] (8) Optimization Feedback Mechanism: refers to dynamically adjusting the system's load balancing strategy and cache strategy by real-time monitoring of system performance indicators to maintain the system's optimal performance under various load conditions. The optimization feedback mechanism in the present invention enables the system to adaptively respond to dynamic changes in transaction load by adjusting load balancing weights and cache strategies.

[0115] (9) Performance Monitoring Metrics: These are key data indicators used to evaluate system operation status and performance, such as CPU usage, memory usage, network latency, and cache hit rate. By monitoring these indicators in real time, performance bottlenecks in the system can be discovered and resolved in a timely manner.

[0116] (10) Feedback Tuning: A method of optimizing system performance based on real-time data. By analyzing and adjusting the actual operating conditions of the system, the system is ensured to maintain efficient and stable operation under changing load conditions.

Claims

1. A performance optimization method for a distributed trading system with high concurrent transactions, characterized in that: The method comprises: ··Use consistent hashing algorithm to optimize load balancing strategy, dynamically and evenly distribute high-concurrency transaction requests to multiple server nodes, ensuring load balance of each node and system stability; Design and apply distributed cache technology to build a multi-level cache mechanism, including a first-level cache for storing frequently accessed data and a second-level cache for storing pre-fetched data; Combined with data pre-fetching technology, it predicts the data that may be accessed in the future based on user access behavior and loads it into the cache in advance to improve the cache hit rate; Based on real-time performance monitoring data, dynamically adjust load balancing and caching strategies to ensure stable operation and efficient processing of the system under high concurrency conditions.

2. The method according to claim 1, characterized in that The consistent hashing algorithm only needs to relocate part of the data when the server nodes are increased or decreased, thereby reducing the impact of node changes on system load balancing.

3. The method according to claim 1, characterized in that The multi-level cache mechanism in the distributed cache technology improves the access speed of high-frequency data and reduces the access pressure on the back-end database through cache hierarchical management.

4. The method according to claim 1, characterized in that: The data pre-fetching technology adopts an adaptive algorithm to predict the user's access needs based on historical access records and real-time analysis.

5. The method according to claim 1, characterized in that: The real-time performance monitoring data includes key system performance indicators, such as CPU usage, memory occupancy, network latency, and cache hit rate.

6. The method according to claim 1, characterized in that The dynamic adjustment of the load balancing strategy includes adjusting the node weights in the consistent hash ring according to the real-time load conditions of the server nodes.

7. The method according to claim 1, characterized in that The dynamic adjustment of the cache strategy includes dynamically allocating cache resources and adjusting the cache update strategy according to the cache hit rate and data access frequency.

8. The method according to claim 1, characterized in that The method is applicable to large-scale, high-frequency trading scenarios in the steel industry, and improves the system's transaction processing efficiency and user experience.

9. The method according to claim 1, characterized in that: The performance optimization method of the system also includes: During the system initialization and configuration phase, deploy a distributed network environment that supports high-concurrency transactions, configure a hybrid load balancing module and an efficient caching mechanism.

10. The method according to claim 1, characterized in that The optimization method avoids system crash or performance degradation caused by high concurrent access by reducing system response time.

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