System and method for quickly responding to service iteration

Abstract

The invention belongs to the technical field of software development and operation and maintenance, and discloses a system for quickly responding to service iteration, which consists of a data source management module, a redis cache module, a configuration center module and an SPEL analysis and Naporn module. By introducing the configuration center system, centralized management and dynamic adjustment of business data output rules are achieved, the output rules are migrated to the configuration level from codes, the flexibility and maintainability of the system are greatly improved, when the rules are changed, only the configuration center needs to be updated, the codes do not need to be modified, the iteration speed is increased, and meanwhile the efficiency is improved. The historical record changing function of the configuration center not only facilitates tracking of rule changes and analysis of potential influences of the rule changes on services, but also can quickly roll back to a stable version when system instability or data glide is caused by configuration updating, so that risks are effectively reduced, and the mechanism ensures smooth proceeding of service iteration and provides powerful support for continuous optimization.

Description

Technical Field

[0001] The present invention belongs to the field of software development and operation and maintenance technology, and specifically relates to a system and method for quickly responding to business iterations. Background Art

[0002] The system and method for rapid response to business iteration is a technical system that ensures that the system can quickly respond to changes in business needs through efficient design, development, testing and deployment processes, flexible system architecture and strong data support. This method emphasizes continuous integration, continuous delivery (CI / CD) and agile development principles to achieve rapid iteration, high-quality delivery and continuous optimization. The current technical system is highly dependent on the ability of designers, especially deep business insights and system design capabilities, which have become the key to project success. However, this dependence limits the flexibility and response speed of the system, especially in the face of rapidly changing and unpredictable business needs. In reality, business iterations often require fine management to avoid conflicts with existing systems, but idealized stable iteration scenarios are rare. In addition, the differences in details and understanding deviations in the iteration process, even in the case of splitting, are prone to hidden dangers. The delayed manifestation of these hidden dangers poses a threat to system stability, increases operation and maintenance risks and costs, and has a potential adverse impact on enterprise operations. Therefore, improving the flexibility and automation of the technical system and reducing human dependence have become important directions for coping with complex and changing business challenges. Summary of the invention

[0003] The purpose of the present invention is to provide a system and method for quickly responding to business iterations, so as to solve the problems raised in the above background technology.

[0004] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a system for quickly responding to business iterations, which is composed of a data source management module, a redis cache module, a configuration center module, a SPEL parsing module and a Nashorn module.

[0005] Preferably, the specific steps of the method for using the system for quickly responding to business iterations are as follows:

[0006] S1: Requirements analysis and data source planning: clarify the data types, sources and business purposes required by the system;

[0007] S2: Introduce data source management: implement the data source management module to centrally manage all data sources;

[0008] S3: Hotspot data identification and Redis cache design: Identify hotspot data and design Redis cache strategy.

[0009] S4: Introducing Redis cache: Integrate Redis cache service to improve system performance;

[0010] S5: Configuration center module design and implementation: build a configuration center to centrally manage data conversion rules;

[0011] S6: Introducing SPEL parsing and Nashorn: Providing flexible rule parsing capabilities;

[0012] S7: User request processing and system iteration: Achieve efficient processing of user requests and support rapid system iteration.

[0013] Preferably, the demand analysis and data source planning in S1 refers to the data types processed by the system including user information, transaction records, and external service interface data. According to the business type, the data is split into user data module, transaction data module and external data source module, and the method of obtaining each type of data is planned.

[0014] Preferably, the introduction of data source management in S2 refers to the development of a data source management module, which covers the comprehensive management of data sources, including registering new data sources, configuring connection parameters, establishing secure connections and implementing access control policies. The module has built-in intelligent selection logic, which can parse the data source identifier in the business request, automatically match and locate the correct data source.

[0015] Preferably, the specific steps of identifying hotspot data in S3 and designing Redis cache are as follows:

[0016] Step 1: Analyze system access logs

[0017] Log collection: ensure that the system can record detailed access logs, including access time, request path, request parameters, and key information of response data;

[0018] Log analysis: Use log analysis tools to parse and count the collected logs to identify which data is frequently accessed, i.e., hot data;

[0019] Hot data identification: Filter out hot data items in the system based on access frequency and request volume indicators to provide a basis for the formulation of cache strategies;

[0020] Step 2: Design the key-value pair structure of Redis cache

[0021] Key design: According to the characteristics of hot data, design a reasonable key name structure to ensure the uniqueness and readability of the key. The key name contains key information such as business ID and data ID.

[0022] Value design: Determine the storage format of cached values, such as JSON, serialized objects, so that they can be stored in Redis and read quickly;

[0023] Structural optimization: Consider using Redis data structure to optimize storage and query efficiency;

[0024] Step 3: Design expiration strategy and data consistency maintenance mechanism

[0025] Expiration policy: Set a reasonable expiration time for cached data to prevent cached data from occupying memory resources for a long time. The expiration time is adjusted according to the data update frequency and access popularity factors;

[0026] Data consistency maintenance:

[0027] Active update: When the data source data changes, the cache update operation is actively triggered to ensure the consistency of the cache data with the data source data;

[0028] Passive failure: Use the expiration mechanism of Redis to automatically invalidate expired cache data, and then reload the latest data through business logic;

[0029] Asynchronous verification: Design asynchronous tasks to regularly verify the consistency between cache data and data source data, and repair them in time if inconsistencies are found;

[0030] Cache breakdown and avalanche prevention: Adopt reasonable cache strategies to prevent cache breakdown and avalanche problems.

[0031] Preferably, the introduction of Redis cache in S4 is to integrate the Redis cache service into the system, implement the cache strategy of the data access layer, try to read data from the Redis cache, if the cache does not hit, turn to the data source for query, and store the query result in Redis for subsequent use.

[0032] Preferably, the specific steps of designing and implementing the configuration center module in S5 are as follows:

[0033] Step 1: Choose the right configuration center solution

[0034] Evaluation requirements: Evaluate the applicability of SpringCloudConfig and Apollo configuration center solutions based on system scale, team familiarity, community activity, and future scalability factors;

[0035] Select a solution: For example, choose Apollo as the configuration center solution, which supports multi-environment and multi-cluster configuration management, provides interface-based configuration modification and release, and a real-time push mechanism;

[0036] Step 2: Design the configuration item storage structure

[0037] Define configuration items: clarify the configuration items required by the system, including database connection information, third-party service API keys, and application feature switches;

[0038] Storage structure design: In the Apollo configuration center, create an independent namespace for each application or component and design a hierarchical configuration structure;

[0039] Support for dynamicity: Embed dynamic logic in configuration values ​​using SPEL expressions or JavaScript code snippets;

[0040] Step 3: Implement a monitoring and automatic application mechanism for configuration changes and build a history recording function

[0041] Configuration monitoring: Integrate the client library of the configuration center in the client application to monitor configuration changes;

[0042] Automatic application: When configuration changes are detected, the configuration instance in the application is automatically updated through a callback mechanism or event notification to ensure that the application behavior is consistent with the latest configuration;

[0043] History function: Configuration centers (such as Apollo) usually have built-in version management and history functions to track the change history of configuration items.

[0044] Preferably, the introduction of SPEL parsing and Nashorn in S6 refers to improving the flexibility and scalability of the system, introducing the SPEL library to parse simple expression rules, and quickly implementing configuration-driven logic changes. For scenarios involving complex business logic, the NashornJavaScript engine is used to execute JavaScript code to support richer data processing and business rule implementation.

[0045] Preferably, the specific steps of user request processing and system iteration in S7 are as follows:

[0046] Step 1: User request processing and data source location

[0047] Receiving user requests: The system first receives a request from the user, which contains the data identifier that needs to be queried or processed;

[0048] Data source location: Use the data source management module to calculate and determine the data source that the request should access based on the information in the request;

[0049] Step 2: Cache reading and data source query

[0050] Redis cache attempt: After determining the data source, the system attempts to read the required data from the Redis cache. The Redis cache module provides fast key-value pair storage and retrieval capabilities, significantly improving data access speed;

[0051] Data source query: If the Redis cache does not hit, the system turns to query the actual data source; Step 3: Dynamic parsing and data processing in the configuration center

[0052] Configuration reading: Read the processing rules related to the current request from the configuration center. These rules may exist in the form of SPEL expressions or JavaScript codes;

[0053] Dynamic parsing: Use the SPEL parser or Nashorn module to dynamically parse these rules and process the data obtained from the data source according to the rules;

[0054] Step 4: Result return and configuration change management

[0055] Result return: return the processed data results to the user;

[0056] Configuration change management: When the data source output rules or data processing logic need to be changed, update the SPEL expression or JavaScript code in the configuration center; History recording and rollback: Use the history recording function of the configuration center to trace the history of configuration changes. When it is necessary to undo the changes or roll back to the old version, restore the old rules through the configuration center to ensure the stability and predictability of the system.

[0057] The beneficial effects of the present invention are as follows:

[0058] 1. The present invention realizes the centralized management and dynamic adjustment of business data output rules by introducing the configuration center system, and migrates the output rules from the code level to the configuration level, which greatly improves the flexibility and maintainability of the system. When the rules change, only the configuration center needs to be updated without modifying the code, which speeds up the iteration speed. At the same time, the change history recording function of the configuration center is not only convenient for tracking rule changes and analyzing their potential impact on the business, but also can quickly roll back to a stable version when the configuration update causes system instability or data decline, effectively reducing the risk. This mechanism ensures the smooth progress of business iteration and provides strong support for continuous optimization.

[0059] 2. The present invention significantly enhances system performance by introducing the Redis cache module, especially for hot data access. By storing data that does not change frequently but is frequently read in Redis, the system throughput is greatly improved. Redis's automatic expiration mechanism ensures that when business data is updated, the old data will naturally become invalid in the cache, and then the latest data will be read from the data source. This ensures efficient access while maintaining the ultimate consistency of the data, providing reliable and efficient data access support for the business system.

[0060] 3. The present invention realizes the dynamic parsing capability of business output rules by introducing SPEL and Nashorn modules. The SPEL module is good at processing simple and fast rule calculations and is easy to maintain; while in the face of complex business iterations, the Nashorn module supports JavaScript code writing, providing more powerful logic processing capabilities and flexibility. This combination strategy not only improves the system's response speed to business changes, but also ensures the accuracy and scalability of rule parsing, laying a solid foundation for the continuous optimization and upgrading of business systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the overall use method of the present invention;

[0062] Figure 2 This is a flowchart of the rapid iteration system of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0064] like Figure 1 to Figure 2 As shown, an embodiment of the present invention provides 1. a system for quickly responding to business iterations, characterized in that it is composed of a data source management module, a redis cache module, a configuration center module, a SPEL parsing and a Nashorn module.

[0065] The specific steps of the method for using the system for quickly responding to business iterations are as follows:

[0066] S1: Requirements analysis and data source planning: clarify the data types, sources and business purposes required by the system;

[0067] S2: Introduce data source management: implement the data source management module to centrally manage all data sources;

[0068] S3: Hotspot data identification and Redis cache design: Identify hotspot data and design Redis cache strategy.

[0069] S4: Introducing Redis cache: Integrate Redis cache service to improve system performance;

[0070] S5: Configuration center module design and implementation: build a configuration center to centrally manage data conversion rules;

[0071] S6: Introducing SPEL parsing and Nashorn: Providing flexible rule parsing capabilities;

[0072] S7: User request processing and system iteration: Achieve efficient processing of user requests and support rapid system iteration.

[0073] Among them, the demand analysis and data source planning in S1 refers to the data types processed by the system, including user information, transaction records, and external service interface data. According to the business type, the data is divided into user data module, transaction data module and external data source module, and the method of obtaining each type of data is planned. For example, user data and transaction data are obtained through database queries, and external service interface data are obtained through API calls. The data source management module architecture is preliminarily designed.

[0074] The benefits of demand analysis and data source planning are: it clarifies the types of data that the system needs to process, and improves the maintainability and scalability of the system through modular design (such as user data, transaction data, and external data sources). Planning data acquisition methods (such as database queries and API calls) ensures the accuracy and timeliness of data. At the same time, the preliminary design of the data source management module architecture lays a solid foundation for subsequent system development and promotes the ability to quickly respond to business iterations.

[0075] Among them, the introduction of data source management in S2 refers to the development of a data source management module, which covers the comprehensive management of data sources, including registering new data sources, configuring connection parameters, establishing secure connections and implementing access control policies. The module has built-in intelligent selection logic, which can parse the data source identifier in the business request, automatically match and locate the correct data source, ensure the efficiency and accuracy of data access, and provide stable and reliable data support for the system.

[0076] The benefits of introducing the data source management module are: it fully covers the management process of the data source, from registration, configuration to secure connection and access control, ensuring the reliability and security of the data source. Its intelligent selection logic can automatically match business requests to the correct data source, improve data access efficiency and accuracy, provide solid data support for the system, enhance the flexibility and response speed of the system, and promote the rapid advancement of business iteration and innovation.

[0077] The specific steps of identifying hot data in S3 and designing Redis cache are as follows:

[0078] Step 1: Analyze system access logs

[0079] Log collection: ensure that the system can record detailed access logs, including access time, request path, request parameters, and key information of response data;

[0080] Log analysis: Use log analysis tools (such as ELKStack, Splunk, etc.) to parse and count the collected logs and identify which data is frequently accessed, i.e., hot data;

[0081] Hot data identification: Filter out hot data items in the system based on access frequency, request volume and other indicators to provide a basis for the formulation of cache strategies;

[0082] Step 2: Design the key-value pair structure of Redis cache

[0083] Key design: According to the characteristics of hot data, design a reasonable key name structure to ensure the uniqueness and readability of the key. The key name contains key information such as business ID and data ID.

[0084] Value design: Determine the storage format of cached values, such as JSON, serialized objects, etc., so that they can be stored in Redis and read quickly;

[0085] Structural optimization: Consider using Redis data structures (such as hash tables, lists, sets, etc.) to optimize storage and query efficiency;

[0086] Step 3: Design expiration strategy and data consistency maintenance mechanism

[0087] Expiration policy: Set a reasonable expiration time for cached data to prevent cached data from occupying memory resources for a long time. The expiration time is adjusted according to the data update frequency and access popularity factors;

[0088] Data consistency maintenance:

[0089] Active update: When the data source data changes, the cache update operation is actively triggered to ensure the consistency of the cache data with the data source data;

[0090] Passive failure: Use the expiration mechanism of Redis to automatically invalidate expired cache data, and then reload the latest data through business logic;

[0091] Asynchronous verification: Asynchronous tasks can be designed to periodically verify the consistency between cache data and data source data, and repair them in time if inconsistencies are found;

[0092] Cache breakdown and avalanche prevention: Adopt reasonable cache strategies (such as setting hot data to never expire, using Bloom filters to filter empty queries, etc.) to prevent cache breakdown and avalanche problems.

[0093] The benefits of hot data identification and Redis cache design are: by accurately identifying hot data and optimizing cache strategies, the system response speed is significantly improved and the database pressure is reduced. Reasonable design of key-value pair structure and expiration strategy ensures efficient data access and effective memory utilization. At the same time, maintain data consistency mechanism and prevent cache problems to ensure stable system operation and improve user experience and business continuity.

[0094] The introduction of Redis cache in S4 is to integrate the Redis cache service into the system and implement the cache strategy of the data access layer: first try to read data from the Redis cache, if the cache does not hit, turn to the data source for query, and store the query results in Redis for subsequent use. In this way, the system response speed is improved and the burden on the data source is reduced.

[0095] The benefit of introducing Redis cache is that it significantly improves the system response speed. By caching hot data, it reduces direct queries to the data source, thereby greatly reducing the burden on the data source. This caching strategy can effectively reduce database access pressure, improve system concurrent processing capabilities, reduce data access latency, and optimize user experience. It is an important means to improve system performance and scalability.

[0096] The specific steps for designing and implementing the configuration center module in S5 are as follows:

[0097] Step 1: Choose the right configuration center solution

[0098] Evaluation requirements: Evaluate the applicability of configuration center solutions such as SpringCloudConfig and Apollo based on factors such as system scale, team familiarity, community activity, and future scalability;

[0099] Select a solution: For example, choose Apollo as the configuration center solution, which supports multi-environment and multi-cluster configuration management, provides interface-based configuration modification and release, and a real-time push mechanism;

[0100] Step 2: Design the configuration item storage structure

[0101] Define configuration items: clarify the configuration items required by the system, including database connection information, third-party service API keys, and application feature switches;

[0102] Storage structure design: In the Apollo configuration center, create an independent namespace for each application or component and design a hierarchical configuration structure, such as server.port or datasource.url in application.properties.

[0103] Support for dynamicity: Use SPEL expressions or JavaScript snippets (if supported by the configuration center) to embed dynamic logic in configuration values, such as conditional configuration based on environment variables;

[0104] Step 3: Implement a monitoring and automatic application mechanism for configuration changes and build a history recording function

[0105] Configuration monitoring: Integrate the client library of the configuration center in the client application to monitor configuration changes; for example, the Apollo client supports automatic monitoring of configuration changes and pushes them to the application.

[0106] Automatic application: When configuration changes are detected, the configuration instance in the application is automatically updated through a callback mechanism or event notification to ensure that the application behavior is consistent with the latest configuration;

[0107] History function: Configuration centers (such as Apollo) usually have built-in version management and history functions, which can track the change history of configuration items. When building, make sure these functions are enabled and configured correctly so that configuration changes can be traced back when needed. If the configuration center does not support direct history function, you can consider implementing it through integrated version control systems or custom logging mechanisms.

[0108] The benefit of the design and implementation of the configuration center module is that it centrally manages the system's configuration items, improving the flexibility and manageability of the configuration. By monitoring configuration changes in real time and automatically applying them, it ensures that the application behavior is synchronized with the latest configuration, reducing operation and maintenance costs. At the same time, the built-in version management and history record functions facilitate tracking configuration changes, enhancing the traceability and stability of the system. These advantages together improve the maintainability and scalability of the system.

[0109] The introduction of SPEL parsing and Nashorn in S6 refers to improving the flexibility and scalability of the system. The SPEL library is introduced to parse simple expression rules and quickly implement configuration-driven logic changes. For scenarios involving complex business logic, the Nashorn JavaScript engine (or other suitable alternatives based on the technology stack and environment) is used to execute JavaScript code, thereby supporting richer data processing and business rule implementation.

[0110] The benefit of introducing SPEL parsing and Nashorn (or other JavaScript engines) is that it significantly enhances the flexibility and scalability of the system. SPEL quickly implements configuration-driven logic changes and simplifies configuration management. JavaScript engines such as Nashorn support the execution of complex business logic, improve the richness of data processing and business rule implementation, enable the system to respond to changing business needs more flexibly, and reduce development and maintenance costs.

[0111] The specific steps of user request processing and system iteration in S7 are as follows:

[0112] Step 1: User request processing and data source location

[0113] Receiving user requests: The system first receives a request from the user, which contains the data identifier that needs to be queried or processed;

[0114] Data source location: Using the data source management module, calculate and determine the data source that the request should access based on the information in the request (such as business type, user ID, etc.); this may involve parsing request parameters, querying routing tables, or executing other logic to determine the correct data source.

[0115] Step 2: Cache reading and data source query

[0116] Redis cache attempt: After determining the data source, the system attempts to read the required data from the Redis cache. The Redis cache module provides fast key-value pair storage and retrieval capabilities, significantly improving data access speed;

[0117] Data source query: If the Redis cache misses (i.e. the required data is not found in the cache), the system turns to querying the actual data source; this may involve a database query, API call, or other type of data retrieval operation.

[0118] Step 3: Dynamic parsing and data processing in the configuration center

[0119] Configuration reading: Read the processing rules related to the current request from the configuration center. These rules may exist in the form of SPEL expressions or JavaScript codes;

[0120] Dynamic parsing: Use the SPEL parser or Nashorn module (if supported by the Java environment) to dynamically parse these rules and process the data obtained from the data source according to the rules; this step allows the system to dynamically adjust the data processing logic according to the rule changes in the configuration center without restarting.

[0121] Step 4: Result return and configuration change management

[0122] Result return: Return the processed data results to the user; this includes formatting the data into a format that the user can understand and sending it to the user through an appropriate response method (such as HTTP response, WebSocket message, etc.);

[0123] Configuration change management: When data source output rules or data processing logic need to be changed, update the SPEL expression or JavaScript code in the configuration center; the system detects these changes by listening to change events in the configuration center or regularly polling the configuration center, and automatically applies new rules without restarting.

[0124] History and rollback: Use the history function of the configuration center to trace the history of configuration changes. When you need to undo changes or roll back to an old version, restore the old rules through the configuration center to ensure the stability and predictability of the system. This step allows the team to safely test and deploy new configuration changes without affecting the processing of current user requests.

[0125] The benefits of introducing the above steps of user request processing and system iteration are: greatly improving the system's response speed, flexibility, and maintainability. Through Redis caching and efficient data source management, fast response and load optimization of data access are achieved. The dynamic parsing function of the configuration center allows flexible adjustment of data processing logic and rapid response to changes in business needs without restarting the system. At the same time, the configuration change management and history record functions enhance the stability and predictability of the system, facilitate troubleshooting and rollback, and provide strong guarantees for the long-term stable operation of the system.

[0126] Redis: is an open source (BSD licensed), in-memory data structure storage system that can be used as a database, cache, and message middleware. It supports multiple types of data structures.

[0127] JAVA: Java is a widely used computer programming language with cross-platform, object-oriented, and generic programming features. It is widely used in enterprise-level Web application development and mobile application development.

[0128] JVM: Java Virtual Machine (Java Virtual Machine, abbreviated as JVM), a virtual machine that can execute Java bytecode, implemented as a stack structure machine. It was first developed and implemented by Sun Microsystems. It is part of the Java platform and can execute software programs written in Java.

[0129] SPEL: Expression Language Spring Expression Language (SpEL for short) is an expression language that supports runtime query and operation of object graphs. The syntax is similar to EL expressions, but provides additional features such as explicit method calls and basic string template functions.

[0130] Nashorn: It is a new JavaScript implementation developed by Oracle. It is highly compatible with the ECMAScript5 standard and is implemented in Java, running on the JVM, while keeping the code relatively clean.

[0131] JDK: is a software development kit for Java developers from Sun (acquired by Oracle).

[0132] JavaScript: (referred to as "JS") is a lightweight, interpreted or just-in-time compiled programming language with function priority. Although it is famous as a scripting language for developing web pages, it is also used in many non-browser environments. JavaScript is a dynamic scripting language based on prototype programming and multiple paradigms, and supports object-oriented, imperative, declarative, and functional programming paradigms.

[0133] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0134] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for rapid response to business iterations, characterized by: The system consists of a data source management module, a redis cache module, a configuration center module, a SPEL parsing module, and a Nashorn module.

2. A method for quickly responding to service iterations, characterized in that: Based on the system of claim 1, the specific steps of the method are as follows: S1: Requirements analysis and data source planning: clarify the data types, sources and business purposes required by the system; S2: Introduce data source management: implement the data source management module to centrally manage all data sources; S3: Hotspot data identification and Redis cache design: Identify hotspot data and design Redis cache strategies; S4: Introducing Redis cache: Integrate Redis cache service to improve system performance; S5: Configuration center module design and implementation: build a configuration center to centrally manage data conversion rules; S6: Introducing SPEL parsing and Nashorn: Providing flexible rule parsing capabilities; S7: User request processing and system iteration: Achieve efficient processing of user requests and support rapid system iteration.

3. A method for quickly responding to service iterations according to claim 2, characterized in that: The demand analysis and data source planning in S1 refers to the data types processed by the system, including user information, transaction records, and external service interface data. According to the business type, the data is divided into user data module, transaction data module, and external data source module, and the method of obtaining each type of data is planned.

4. A method for quickly responding to service iterations according to claim 2, characterized in that: The introduction of data source management in S2 refers to the development of a data source management module, which covers the comprehensive management of data sources, including registering new data sources, configuring connection parameters, establishing secure connections, and implementing access control policies. The module has built-in intelligent selection logic that can parse the data source identifier in the business request, automatically match and locate the correct data source.

5. A method for quickly responding to service iterations according to claim 2, characterized in that: The specific steps of identifying hot data in S3 and designing Redis cache are as follows: Step 1: Analyze system access logs Log collection: ensure that the system can record detailed access logs, including access time, request path, request parameters, and key information of response data; Log analysis: Use log analysis tools to parse and count the collected logs to identify which data is frequently accessed, i.e., hot data; Hot data identification: Filter out hot data items in the system based on access frequency and request volume indicators to provide a basis for the formulation of cache strategies; Step 2: Design the key-value pair structure of Redis cache Key design: According to the characteristics of hot data, design a reasonable key name structure to ensure the uniqueness and readability of the key. The key name contains key information such as business ID and data ID. Value design: Determine the storage format of cached values, such as JSON, serialized objects, so that they can be stored in Redis and read quickly; Structural optimization: Consider using Redis data structure to optimize storage and query efficiency; Step 3: Design expiration strategy and data consistency maintenance mechanism Expiration policy: Set a reasonable expiration time for cached data to prevent cached data from occupying memory resources for a long time. The expiration time is adjusted according to the data update frequency and access popularity factors; Data consistency maintenance: Active update: When the data source data changes, the cache update operation is actively triggered to ensure the consistency of the cache data with the data source data; Passive failure: Use the expiration mechanism of Redis to automatically invalidate expired cache data, and then reload the latest data through business logic; Asynchronous verification: Design asynchronous tasks to regularly verify the consistency between cache data and data source data, and repair them in time if inconsistencies are found; Cache breakdown and avalanche prevention: Adopt reasonable cache strategies to prevent cache breakdown and avalanche problems.

6. A method for quickly responding to service iterations according to claim 2, characterized in that: The introduction of Redis cache in S4 is to integrate the Redis cache service into the system, implement the cache strategy of the data access layer, try to read data from the Redis cache, if the cache does not hit, turn to the data source for query, and store the query results in Redis for subsequent use.

7. A method for quickly responding to service iterations according to claim 2, characterized in that: The specific steps for designing and implementing the configuration center module in S5 are as follows: Step 1: Choose the right configuration center solution Evaluation requirements: Evaluate the applicability of SpringCloud Config and Apollo configuration center solutions based on system scale, team familiarity, community activity, and future scalability factors; Select a solution: For example, choose Apollo as the configuration center solution, which supports multi-environment and multi-cluster configuration management, provides interface-based configuration modification and release, and a real-time push mechanism; Step 2: Design the configuration item storage structure Define configuration items: clarify the configuration items required by the system, including database connection information, third-party service API keys, and application feature switches; Storage structure design: In the Apollo configuration center, create an independent namespace for each application or component and design a hierarchical configuration structure; Support dynamicity: Use SPEL expressions or JavaScript code snippets to embed dynamic logic in configuration values; Step 3: Implement a monitoring and automatic application mechanism for configuration changes and build a history recording function Configuration monitoring: Integrate the client library of the configuration center in the client application to monitor configuration changes; Automatic application: When configuration changes are detected, the configuration instance in the application is automatically updated through a callback mechanism or event notification to ensure that the application behavior is consistent with the latest configuration; History function: Configuration centers (such as Apollo) usually have built-in version management and history functions to track the change history of configuration items.

8. A method for quickly responding to service iterations according to claim 2, characterized in that: The introduction of SPEL parsing and Nashorn in S6 is to improve the flexibility and scalability of the system. The SPEL library is introduced to parse simple expression rules and quickly implement configuration-driven logic changes. For scenarios involving complex business logic, the NashornJavaScript engine is used to execute JavaScript code to support richer data processing and business rule implementation.

9. A method for quickly responding to service iterations according to claim 2, characterized in that: The specific steps of user request processing and system iteration in S7 are as follows: Step 1: User request processing and data source location Receiving user requests: The system first receives a request from the user, which contains the data identifier that needs to be queried or processed; Data source location: Use the data source management module to calculate and determine the data source that the request should access based on the information in the request; Step 2: Cache reading and data source query Redis cache attempt: After determining the data source, the system attempts to read the required data from the Redis cache. The Redis cache module provides fast key-value pair storage and retrieval capabilities, significantly improving data access speed; Data source query: If the Redis cache does not hit, the system turns to query the actual data source; Step 3: Dynamic parsing and data processing in the configuration center Configuration reading: Read the processing rules related to the current request from the configuration center. These rules may exist in the form of SPEL expressions or JavaScript codes; Dynamic parsing: Use the SPEL parser or Nashorn module to dynamically parse these rules and process the data obtained from the data source according to the rules; Step 4: Result return and configuration change management Result return: return the processed data results to the user; Configuration change management: When the data source output rules or data processing logic need to be changed, update the SPEL expression or JavaScript code in the configuration center; History recording and rollback: Use the history recording function of the configuration center to trace the history of configuration changes. When it is necessary to undo the changes or roll back to the old version, restore the old rules through the configuration center to ensure the stability and predictability of the system.

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