An intelligent global index management system

Abstract

The present invention discloses an intelligent global index management system, including an intelligent index controller: used for decision-making and regulation; in terms of hardware, a server architecture is adopted, equipped with a multi-core processor, providing solid hardware support for rapid data processing; at the software level, strategy formulation and optimization algorithms are integrated to deeply analyze data characteristics and access pattern information; the beneficial effects of the present invention are: a distributed node layout is adopted, and each node cooperates with each other to jointly undertake index management tasks; when the system scale is expanded or the load increases, new nodes can be easily added to achieve seamless expansion without affecting the normal operation of the system; through in-depth research on data structures and algorithms, combined with hardware characteristics, efficient index construction, update and query algorithms are developed, which greatly shortens the operation time and improves the response speed of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of index management, and in particular relates to an intelligent global index management system. Background Art

[0002] In today's digital age, the explosive growth of data has posed severe challenges to large-scale cluster storage products. As a core component in the field of data management, the development of indexing technology is closely related to the overall evolution of information technology. From the early simple data structures to today's complex distributed architectures, indexing technology has been continuously innovated to adapt to the growing data scale and diversified application needs.

[0003] In a large-scale data storage environment, efficient and accurate retrieval and access to data has become a key task; the core role of indexing technology is to significantly reduce the time and resource consumption of data retrieval by establishing data mapping relationships; in this process, a series of key technologies have emerged and been applied.

[0004] Efficient index structure design is the primary link; traditional linear indexes are inefficient when processing large-scale data, while tree index structures such as B-trees and B+ trees greatly improve retrieval performance by optimizing data storage and access paths; in addition, rapid index construction and update technology is also crucial; as data is continuously added, modified, and deleted, the index needs to be able to reflect data changes in a timely and accurate manner to ensure the accuracy of retrieval results; at the same time, index storage and optimization technology also directly affects system performance and resource utilization efficiency; through reasonable storage layout and compression algorithms, the storage space occupied by the index can be reduced and the utilization rate of storage devices can be improved.

[0005] In the prior art, common index management solutions mainly include centralized index management and simple partitioning of distributed indexes. Centralized index management stores all index information in a central node. This architecture may show certain advantages in a small-scale data environment, such as simple management and relatively short query paths. However, when faced with large-scale cluster storage and high concurrent access, its limitations become increasingly obvious. First, single point failure has become a prominent problem. Since all index information depends on the central node, once the node fails, the index service of the entire system will be paralyzed, resulting in the inability to retrieve and access data normally, seriously affecting business continuity. Secondly, performance bottlenecks are also an unavoidable problem. A large number of index requests are concentrated on the central node, and its processing capacity can easily reach its limit, resulting in increased response delays and a significant decrease in retrieval efficiency.

[0006] The simple partitioning method of distributed indexes has alleviated the single point failure and performance bottleneck problems of centralized architectures to a certain extent; it distributes index data on multiple nodes according to certain rules; however, this method still has many shortcomings. Unbalanced data distribution is one of the common problems. Some partitions may carry too much index data, while other partitions are relatively idle, resulting in inefficient utilization of system resources; in addition, in scenarios where data is frequently updated, the update efficiency of the index is often difficult to meet the requirements; due to the imperfect coordination mechanism between partitions, update operations may be delayed or inconsistent, thereby affecting the accuracy of data retrieval; at the same time, the complexity of cross-partition queries also poses a huge challenge to the system; when the data to be queried spans multiple partitions, complex coordination and data integration operations are required, which not only increases the time cost of the query, but also may lead to inaccurate query results.

[0007] Disadvantages of existing technology:

[0008] High risk of single point failure: In the centralized index management architecture, the central node bears the responsibility of index management for the entire system. Once the node encounters hardware failure, software error, network interruption, etc., the entire index service will stop instantly, triggering a chain reaction, causing various data operations that rely on the index to stagnate. This single point failure risk is extremely fatal in business scenarios that require extremely high system reliability, such as financial transactions and medical data management.

[0009] Performance bottleneck: In a centralized architecture, all index requests are aggregated to the central node for processing. With the rapid growth of data volume and frequent concurrent access requests, the processing capacity of the central node will soon reach its limit. This will cause the request response time to be significantly extended and the system throughput to drop sharply, seriously affecting the efficiency of data retrieval and user experience.

[0010] Unbalanced data distribution: In the simple partitioning method of distributed index, due to unreasonable partitioning rules or dynamic changes in data characteristics, it is easy for some partitions to be overloaded while other partitions are idle. This not only leads to a waste of system resources, but also seriously restricts the overall performance, because the overloaded partitions will become slow in processing requests, thus affecting the response speed of the entire system.

[0011] Low index update efficiency: In an environment where data changes frequently, the existing index update mechanism often cannot keep up with the pace of data changes in a timely manner. This may be caused by communication delays between partitions, synchronization difficulties in update operations, or inefficient update algorithms. Untimely index updates will lead to inaccurate data retrieval results, which in turn affects the correctness of business decisions.

[0012] Cross-partition query is complex: When the data to be queried is distributed across multiple partitions, the existing cross-partition query mechanism requires a lot of coordination and data integration work; this includes transmitting query requests between different partitions, merging query results, and handling possible consistency issues; these complex operations not only increase the query time overhead, but also easily introduce errors, reducing the accuracy and reliability of the query. Summary of the invention

[0013] The purpose of the present invention is to provide an intelligent global index management system to improve the reliability of index management and eliminate single points of failure; enhance the performance of index processing and speed up data retrieval; achieve balanced distribution of index data and optimize system resource utilization; improve the efficiency of index updates and ensure the accuracy of data retrieval; simplify the process of cross-partition queries and reduce the complexity of queries.

[0014] To achieve the above object, the present invention provides the following technical solution: an intelligent global index management system, comprising

[0015] Intelligent index controller: used for decision-making and regulation; in terms of hardware, it adopts server architecture and is equipped with multi-core processors to provide solid hardware support for rapid data processing; in terms of software, it integrates strategy formulation and optimization algorithms to deeply analyze data features and access pattern information; through data mining, pattern recognition and intelligent analysis technology, it captures hidden laws and trends in the data, thereby dynamically generating and optimizing global index strategies;

[0016] Distributed index nodes: In terms of hardware, they are built on high-performance index processing chips, equipped with cache mechanisms and data compression algorithms, to achieve data storage and retrieval within limited resources; in terms of software, they run customized index management programs, execute policy instructions issued by the intelligent index controller, and process local data indexing tasks;

[0017] Data perception module: Insight into the dynamic changes of data; the hardware components include high-precision sensors that can accurately capture every slight change in data in real time. The data analysis software uses cutting-edge machine learning algorithms and deep data mining technology to extract key information from the data through detailed analysis of the original data, and pass this valuable intelligence to the intelligent index controller;

[0018] Synchronization mechanism: The key link to ensure the coordination and consistency of the entire system, built on network protocols and data synchronization algorithms.

[0019] As a preferred technical solution of the present invention, the intelligent index controller optimizes the algorithm through in-depth analysis of data features and access patterns to intelligently select the most suitable index structure, partitioning method and storage strategy to achieve the best retrieval performance.

[0020] As a preferred technical solution of the present invention, the distributed index node cache mechanism can store and provide commonly used index data in an instant, reduce the number of accesses to the main storage, and reduce the response time; the data compression algorithm compresses the storage space of the index data without losing data accuracy, thereby improving the utilization rate of storage resources; at the software level, the customized index management program performs index construction, update and query operations according to the instructions of the intelligent index controller.

[0021] As a preferred technical solution of the present invention, the data analysis software of the data perception module uses machine learning algorithms and data mining techniques to conduct in-depth analysis and refinement of the raw data collected by the sensor; valuable information is extracted from the raw data through pattern recognition, cluster analysis and trend prediction methods. After this information is sorted and formatted, it is passed to the intelligent index controller to provide a key basis for its decision-making.

[0022] As a preferred technical solution of the present invention, valuable information is extracted from the original data, including data access hot spots, frequently updated areas and potential change trends.

[0023] As a preferred technical solution of the present invention, the synchronization mechanism adopts RDMA communication at the network protocol level to build a high-speed channel for data transmission; the data synchronization algorithm integrates innovative strategies based on version control and conflict resolution to ensure that data can be merged and synchronized accurately when multiple nodes are updating at the same time.

[0024] As a preferred technical solution of the present invention, the version control mechanism assigns a unique version number to each data item. When data is synchronized between nodes, it can be determined which data needs to be updated by comparing the version numbers.

[0025] As a preferred technical solution of the present invention, the conflict resolution algorithm intelligently judges and selects the final effective modification when multiple nodes modify the same data item at the same time, thereby ensuring the consistency and accuracy of the data.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] With a distributed node layout, each node cooperates with each other and jointly undertakes index management tasks; when the system scale expands or the load increases, new nodes can be easily added to achieve seamless expansion without affecting the normal operation of the system;

[0028] By deeply studying data structures and algorithms and combining hardware characteristics, we developed efficient index building, updating, and querying algorithms, which greatly shortened the operation time and improved the system's response speed.

[0029] Real-time monitoring of data characteristics and access patterns, dynamic adjustment of partition boundaries and data distribution, avoiding data skew and resource waste, and giving full play to the performance advantages of the system;

[0030] Utilizing high-speed networks and optimized synchronization protocols, we ensure real-time synchronization of index data between nodes, eliminating the risks brought by data inconsistency and improving the accuracy of data retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] Example 1

[0034] See also Figure 1 , is the first embodiment of the present invention, which provides an intelligent global index management system, including

[0035] Intelligent index controller: It is like the wisdom center of the entire system, with excellent decision-making and control capabilities. From the hardware perspective, it adopts a high-performance server architecture, equipped with advanced multi-core processors, large-capacity cache and extremely fast storage devices, providing solid hardware support for the rapid processing of massive data. At the software level, it incorporates complex and sophisticated strategy formulation and optimization algorithms, which can deeply analyze massive data features and access pattern information. Through data mining, pattern recognition, intelligent analysis and other technical means, it can keenly capture the hidden laws and trends in the data, thereby dynamically generating and optimizing the global index strategy.

[0036] Distributed index nodes: Like elite combat units scattered across the network, each node has independent and powerful processing capabilities. In terms of hardware, they are built on dedicated high-performance index processing chips that have powerful parallel processing capabilities and ultra-low latency response characteristics. At the same time, they are equipped with efficient caching mechanisms and advanced data compression algorithms, which can achieve fast data storage and retrieval within limited resources. In terms of software, they run customized index management programs that can accurately execute policy instructions issued by the intelligent index controller and efficiently process local data indexing tasks.

[0037] Data perception module: It is like a sharp and accurate reconnaissance antenna, which can always see the dynamic changes of data; the hardware composition includes high-precision sensors, which can capture every slight change of data in real time and accurately, whether it is adding, modifying or deleting operations, and can capture them without omission; working with it is powerful data analysis software, which uses cutting-edge machine learning algorithms and deep data mining technology; through the detailed analysis of raw data, it can accurately extract key information such as data access frequency, hot spots, change trends, etc., and quickly pass this valuable intelligence to the intelligent index controller;

[0038] Synchronization mechanism: As the key link to ensure the coordination and consistency of the entire system, it is built on efficient network protocols and advanced data synchronization algorithms. At the network protocol level, it adopts low-latency, high-bandwidth cutting-edge communication technologies such as RDMA (Remote Direct Memory Access) to build a high-speed channel for extremely fast data transmission; the data synchronization algorithm integrates innovative strategies based on version control and conflict resolution to ensure that when multiple nodes are updating at the same time, data can be merged and synchronized accurately, effectively avoiding data inconsistency and potential conflicts;

[0039] Shape, structure and function of each component:

[0040] Intelligent index controller: Its hardware structure is like a well-designed intelligent fortress; multi-core processors are like multiple powerful engines, working together to process massive amounts of data and complex computing tasks in parallel; large-capacity cache is like an efficient buffer pool, which can quickly store and read frequently used data, greatly reducing the latency of data access; ultra-fast storage devices are like a huge and fast warehouse, which can quickly store and retrieve a large amount of policy data and intermediate results; in terms of software, the policy formulation and optimization algorithm is like a sophisticated navigation system that can accurately find the best path in the ocean of data; through in-depth analysis of data characteristics and access patterns, the algorithm can intelligently select the most suitable index structure, partitioning method and storage strategy to achieve the best retrieval performance;

[0041] Distributed index node: Its hardware form is like a well-crafted sharp sword; the dedicated index processing chip is its core, with a highly parallel processing architecture, which can handle multiple index operation requests at the same time, greatly improving the processing speed; the efficient cache mechanism is like a fast-response buffer, which can store and provide commonly used index data in an instant, reducing the number of accesses to the main storage, thereby significantly reducing the response time; the advanced data compression algorithm is like an excellent space magician, which can greatly compress the storage space of index data without losing data accuracy, thereby improving the utilization rate of storage resources; at the software level, the customized index management program is like a precise executor, strictly following the instructions of the intelligent index controller, and efficiently performing index construction, update and query operations to ensure the accuracy and timeliness of local index data;

[0042] Data perception module: Its hardware is like a high-precision detector; the sensor has extremely high sensitivity and precision, and can accurately capture every subtle change in the data, even extremely small data fluctuations can be accurately perceived; the data analysis software is like an intelligent analyst, using complex machine learning algorithms and data mining technology to conduct in-depth analysis and refinement of the raw data collected by the sensor; through pattern recognition, cluster analysis and trend prediction methods, it extracts valuable information from massive amounts of raw data, such as data access hotspots, frequently updated areas, and potential change trends; after this information is carefully organized and formatted, it is quickly transmitted to the intelligent index controller to provide key basis for its decision-making;

[0043] Synchronization mechanism: Its structure is like a solid and efficient information highway; advanced network protocols such as RDMA provide low-latency, high-bandwidth data transmission channels to ensure that data can flow quickly between nodes; synchronization algorithms based on version control and conflict resolution are like a set of sophisticated traffic rules to ensure that update operations between nodes can be carried out in an orderly manner during data transmission; the version control mechanism assigns a unique version number to each data item. When data is synchronized between nodes, it can be quickly determined which data needs to be updated by comparing version numbers; conflict resolution algorithms can intelligently judge and select the final effective modification when multiple nodes modify the same data item at the same time, ensuring data consistency and accuracy.

[0044] Positional relationship, connection relationship, operation relationship, and functional relationship between components:

[0045] The intelligent index controller is located at the core of the system, like a shining sun, and is the decision-making center of the entire system. It is closely connected to each distributed index node through a high-speed, low-latency network link, forming a star-shaped topology. This connection relationship is like the sun radiating light to the planets, ensuring that the intelligent index controller can quickly and efficiently distribute the carefully formulated indexing strategies to each distributed index node, and receive the information they feedback in a timely and accurate manner. The data perception module is like a microscopic detector scattered in every corner, tightly embedded in each storage node, forming a symbiotic relationship with the local distributed index nodes. ; At the operational level, when the data perception module keenly captures any subtle changes in the data, it will quickly and accurately transmit these key information to the intelligent index controller through high-speed network links; and the distributed index nodes build a close cooperative relationship through the synchronization mechanism; when performing indexing operations, they will strictly follow the instructions issued by the intelligent index controller, work together, and jointly complete key tasks such as index construction, update and query; when a distributed index node completes the local operation, it will quickly propagate the updated data to other nodes through the synchronization mechanism, thereby ensuring that the index data in the entire system always maintains a high degree of consistency and accuracy;

[0046] Working principle, process / workflow and steps:

[0047] Working Principle: When the system starts, it is like a grand technological feast kicks off. First, the intelligent index controller quickly starts a comprehensive and in-depth data collection journey, like an experienced and sharp-eyed explorer, collecting information on data features and access patterns from various storage nodes. This process is like a comprehensive, high-precision deep scan of the entire data universe. Then, with its powerful computing power and complex and sophisticated algorithm system, it conducts in-depth analysis and fine mining of the massive amount of information collected, like a wise and insightful strategist, who can see the potential rules and trends from the complex data, and then carefully formulate the initial indexing strategy. The generation of this strategy is like drawing a blueprint for the entire system that is accurate to every detail. Then, the intelligent index controller quickly and efficiently distributes the carefully formulated indexing strategy to each distributed indexing node, like a decisive and authoritative person. The powerful commander issued clear and firm combat orders to his soldiers. During the continuous operation of the system, the data perception module always maintains a high degree of alertness and keen perception, like a tireless and vigilant sentinel, constantly monitoring the real-time changes of data and the dynamic adjustment of access modes. Once any abnormal or critical change information is found, it will immediately and quickly feed it back to the intelligent index controller, just like a front-line scout delivering urgent and critical military intelligence to the headquarters. After receiving these real-time feedbacks, the intelligent index controller will quickly and flexibly adjust the index strategy dynamically, and quickly synchronize the updated strategy to each distributed index node through an efficient synchronization mechanism, ensuring that the entire system can always accurately adapt to the ever-changing data environment and access needs, just like a well-trained, responsive, and powerful elite force, always maintaining strong adaptability and excellent combat effectiveness in the complex and ever-changing data battlefield.

[0048] Process / Workflow and Steps:

[0049] Data collection: The high-precision sensors in the data perception module sample data in real time with ultra-high frequency and accuracy, just like a super-precise scanner that does not miss any tiny corner of the data world. At the same time, the advanced data analysis software that works with it uses complex and efficient algorithms to finely screen and pre-process the sampled raw data. By removing noise, invalid information and abnormal data points, the truly critical data features and access pattern information are extracted. These carefully processed and refined data information is encapsulated into a standardized and normalized data format, and then quickly transmitted to the intelligent index controller through a high-speed, low-latency network link, providing a solid and reliable data foundation for subsequent strategy formulation.

[0050] Strategy formulation: After receiving massive amounts of data from the data perception module, the intelligent index controller quickly loads it into the internal large-capacity cache and memory for further in-depth analysis and processing; it uses complex machine learning models and optimization algorithms to conduct multi-level and multi-angle mining and analysis of these data, trying to find out the potential laws and trends hidden deep in the data; based on these in-depth analysis results, the intelligent index controller carefully formulates the initial index strategy, including but not limited to key decisions such as the structure design of the index, the partitioning method of the data, and the selection of the storage location; in order to ensure the effectiveness and optimality of the strategy, the generated strategy will be comprehensively evaluated and finely optimized through simulation and verification algorithms to ensure that it can perform at its best in actual applications and meet the various strict requirements of the system;

[0051] Policy distribution: The indexing policies that have been carefully optimized and verified are distributed to each distributed indexing node by the intelligent indexing controller in the form of highly compressed and efficiently transmitted data packets through high-speed network links. Each data packet contains detailed and precise policy instructions and related parameter configuration information to ensure that the distributed indexing nodes can accurately understand and execute the policies. After receiving the policy data packets, the distributed indexing nodes will use their internal parsing and verification mechanisms to carefully interpret and verify them to ensure that they can correctly understand and execute the policy instructions issued by the intelligent indexing controller, thereby ensuring the coordinated operation and efficient performance of the entire system.

[0052] Indexing operations: Distributed indexing nodes quickly carry out key operations such as index building, updating, and querying based on the precise indexing strategies they receive. During the indexing process, local data is carefully organized and efficiently stored in strict accordance with the structure and algorithm specified by the strategy. When data changes, the index content can be adjusted in real time according to the strategy to ensure the accuracy and timeliness of the index.

[0053] Example 2

[0054] See also Figure 1 , which is the second embodiment of the present invention, is based on the previous embodiment, except that:

[0055] For the intelligent indexing controller, an alternative is to adopt a distributed hash table (DHT)-based architecture, in which the distribution and coordination of indexing strategies are achieved through hash calculations and routing mechanisms between distributed nodes;

[0056] Regarding distributed index nodes, consider using all-flash arrays based on solid-state drives (SSDs) to replace traditional disk storage;

[0057] For the data perception module, an alternative approach could be a monitoring method based on a rule engine, which detects data changes through a set of pre-set rules;

[0058] As for the synchronization mechanism, an asynchronous synchronization method based on a message queue can be adopted, and the message queue can buffer the synchronization data.

[0059] Although the embodiments of the present invention have been shown and described, as detailed above, it will be understood 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 the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent global index management system, characterized in that: include Intelligent index controller: used for decision-making and regulation; in terms of hardware, it adopts server architecture and is equipped with multi-core processors to provide solid hardware support for rapid data processing; in terms of software, it integrates strategy formulation and optimization algorithms to deeply analyze data features and access pattern information; through data mining, pattern recognition and intelligent analysis technology, it captures hidden laws and trends in the data, thereby dynamically generating and optimizing global index strategies; Distributed index nodes: In terms of hardware, they are built on high-performance index processing chips, equipped with cache mechanisms and data compression algorithms, to achieve data storage and retrieval within limited resources; in terms of software, they run customized index management programs, execute policy instructions issued by the intelligent index controller, and process local data indexing tasks; Data perception module: Insight into the dynamic changes of data; the hardware components include high-precision sensors that can accurately capture every slight change in data in real time. The data analysis software uses cutting-edge machine learning algorithms and deep data mining technology to extract key information from the data through detailed analysis of the original data, and pass this valuable intelligence to the intelligent index controller; Synchronization mechanism: The key link to ensure the coordination and consistency of the entire system, built on network protocols and data synchronization algorithms.

2. The intelligent global index management system according to claim 1, characterized in that: The intelligent index controller uses an optimization algorithm to intelligently select the most suitable index structure, partitioning method and storage strategy through in-depth analysis of data features and access patterns to achieve the best retrieval performance.

3. The intelligent global index management system according to claim 1, characterized in that: The distributed index node cache mechanism can store and provide commonly used index data in an instant, reduce the number of visits to the main storage, and reduce the response time; the data compression algorithm compresses the storage space of index data without losing data accuracy, thereby improving the utilization rate of storage resources; at the software level, the customized index management program performs index construction, update and query operations according to the instructions of the intelligent index controller.

4. The intelligent global index management system according to claim 1, characterized in that: The data analysis software of the data perception module uses machine learning algorithms and data mining technology to conduct in-depth analysis and refinement of the raw data collected by the sensor; through pattern recognition, cluster analysis and trend prediction methods, valuable information is extracted from the raw data. After this information is sorted and formatted, it is passed to the intelligent index controller to provide key basis for its decision-making.

5. The intelligent global index management system according to claim 4, characterized in that: Extract valuable information from raw data, including data access hotspots, frequently updated areas, and potential change trends.

6. The intelligent global index management system according to claim 1, characterized in that: The synchronization mechanism uses RDMA communication at the network protocol level to build a high-speed channel for data transmission; The data synchronization algorithm integrates innovative strategies based on version control and conflict resolution to ensure that data can be merged and synchronized accurately when multiple nodes are updating at the same time.

7. The intelligent global index management system according to claim 6, characterized in that: The version control mechanism assigns a unique version number to each data item. When data is synchronized between nodes, the version numbers can be compared to determine which data needs to be updated.

8. The intelligent global index management system according to claim 6, characterized in that: When multiple nodes modify the same data item at the same time, the conflict resolution algorithm intelligently judges and selects the final effective modification to ensure data consistency and accuracy.

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