Index routing method and apparatus, device, medium

Abstract

This application relates to an index routing method, apparatus, device, and medium. The method includes: responding to an e-commerce data search request and obtaining a corresponding merchant identifier and search scenario identifier; determining a target merchant index corresponding to the merchant identifier from a merchant index pool according to merchant index routing rules; determining one or more target e-commerce data clusters corresponding to the target merchant index; determining a target search scenario index corresponding to a search scenario identifier from a search scenario index pool corresponding to the target merchant index according to search scenario index routing rules; determining a search e-commerce data cluster containing the target search scenario index from each target e-commerce data cluster; and enabling the target search scenario index to process e-commerce data search requests in the search e-commerce data cluster to obtain e-commerce data search results. This application can use different performance clusters to process data searches based on the characteristics of merchants and search scenarios in e-commerce scenarios, thereby improving the processing efficiency of data search services.

Description

Technical Field

[0001] This application relates to the field of e-commerce search technology, and in particular to an index routing method and the corresponding apparatus, computer equipment, and computer-readable storage medium. Background Technology

[0002] In independent e-commerce platforms, a search engine built on Elasticsearch is typically used to provide e-commerce data search services for the independent websites of various merchants on the platform. However, existing e-commerce data search services on these platforms usually provide the same resources for e-commerce data searches to all merchants' independent websites. Yet, the amount of search data, search requests, and business complexity vary significantly among the independent websites on an independent e-commerce platform. This means that the search traffic of different independent websites within the platform differs considerably. The search data and search requests of more active, high-quality merchants' independent websites are often far greater than those of inactive, less popular merchants' independent websites. Therefore, if the e-commerce data search service provides the same resources for all merchants' independent websites, high-quality merchants' independent websites may experience slow response times due to insufficient resources, while less popular merchants' independent websites may suffer from wasted resources due to lower data and request volumes.

[0003] Furthermore, besides the resource issues arising from the merchant dimension in independent e-commerce platforms, the resources provided by e-commerce data search services also vary across different search scenarios. Search scenarios can be categorized into merchant search scenarios, buyer search scenarios, physical system search scenarios, and platform payment search scenarios. The amount of search data and search requests differs significantly across these scenarios. For example, the amount of search data in merchant search scenarios is generally higher than that in buyer search scenarios, while the number of search requests in buyer search scenarios is often higher than that in merchant search scenarios. Therefore, allocating system resources to different search scenarios can also lead to resource allocation issues in e-commerce data search services.

[0004] Given the shortcomings of traditional technologies, the applicant has long been engaged in research in related fields and has therefore taken a different approach to solve the industry problems in the field of e-commerce search technology. Summary of the Invention

[0005] The primary objective of this application is to solve at least one of the aforementioned problems by providing an index routing method and corresponding apparatus, computer equipment, and computer-readable storage medium.

[0006] To achieve the various objectives of this application, the following technical solution is adopted:

[0007] An index routing method provided for one of the purposes of this application includes the following steps:

[0008] In response to an e-commerce data search request, obtain the merchant identifier and search scenario identifier corresponding to the e-commerce data search request;

[0009] According to the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool. The merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes.

[0010] Identify one or more target e-commerce data clusters corresponding to the target merchant index, wherein the e-commerce data clusters include high-performance e-commerce data clusters, medium-performance e-commerce data clusters, and low-performance e-commerce data clusters;

[0011] According to the search scenario index routing rules, the target search scenario index corresponding to the search scenario identifier is determined from the search scenario index pool corresponding to the target merchant index. The search scenario index pool contains merchant search scenario indexes, buyer search scenario indexes and other e-commerce search scenario indexes.

[0012] From each of the target e-commerce data clusters, determine the search e-commerce data cluster corresponding to the target search scenario index, enable the target search scenario index in the search e-commerce data cluster to process the e-commerce data search request, and obtain the corresponding e-commerce data search results to respond to the e-commerce data search request.

[0013] In a further embodiment, before responding to an e-commerce data search request and obtaining the merchant identifier and search scenario identifier corresponding to the e-commerce data search request, the following steps are included:

[0014] A merchant index corresponding to multiple merchant types is constructed, and multiple search scenario indexes corresponding to search scenario types are constructed for each merchant index, thereby generating a merchant index pool composed of each of the merchant indexes, and a search scenario index pool composed of each of the search scenario indexes is constructed for each of the merchant indexes. The merchant types include high-quality merchants, ordinary merchants and unpopular merchants, and the search scenario types include merchant search scenarios, buyer merchant search scenarios and other e-commerce search scenarios.

[0015] The system obtains store traffic information from multiple merchants on an independent e-commerce platform. This store traffic information includes store product sales volume, store order volume, store visits, or store exposure. The system then calls a merchant user traffic analysis algorithm to determine the merchant type of each merchant user based on their store traffic information.

[0016] Based on the merchant type of each merchant user, a corresponding merchant index from the merchant index pool is allocated to each merchant user. The e-commerce data of each merchant user is stored in the e-commerce data cluster corresponding to its merchant index, and each merchant index is updated accordingly.

[0017] Determine the search scenario index pool corresponding to the merchant index of each merchant user, and then determine the search scenario index in each search scenario index pool.

[0018] In a further embodiment, after enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request, the following steps are included:

[0019] In response to an e-commerce data update request, obtain the merchant identifier and updated e-commerce data corresponding to the e-commerce data update request;

[0020] Based on the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool, and one or more e-commerce data clusters corresponding to the target merchant index are determined.

[0021] The updated e-commerce data is then distributed to each of the e-commerce data clusters, and the target merchant index is updated accordingly.

[0022] The search scenario index pool corresponding to the target merchant index is determined, and each search scenario index in the search scenario index pool is updated accordingly.

[0023] In a further embodiment, based on the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool, wherein the merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes, including the following steps:

[0024] Obtain the merchant identifier corresponding to the e-commerce data search request, and use a hash algorithm to calculate the hash feature code corresponding to the merchant identifier;

[0025] Determine the number of merchant indexes in the merchant index pool, perform a modulo operation between the hash feature code and the number of merchant indexes, and use the result as the index sequence number;

[0026] The merchant index corresponding to the index number in the merchant index pool is determined, and this merchant index is used as the target merchant index corresponding to the merchant identifier.

[0027] In a further embodiment, the process of enabling the target search scenario index to handle the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request includes the following steps:

[0028] Identify all shards in the e-commerce data cluster corresponding to the target search scenario index;

[0029] The e-commerce data search request is broadcast to the data nodes of each of the shards, driving each of the data nodes to search for the e-commerce data identifier and its score value corresponding to the search request.

[0030] Based on the scoring values ​​of each e-commerce data identifier, data operations are performed on each e-commerce data identifier to generate a corresponding e-commerce data identifier set;

[0031] From each of the data nodes, read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set, and generate an e-commerce data search result containing each of the e-commerce data.

[0032] In a further embodiment, enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster includes the following steps:

[0033] In response to the search request full load event of the e-commerce data cluster, determine the unloaded search scenario index in the search scenario index pool corresponding to the target merchant index;

[0034] The data nodes corresponding to all shards of the unloaded search scenario index in the search e-commerce data cluster are determined. The e-commerce data search request is broadcast to the data nodes of each shard, and each data node is driven to search out the e-commerce data identifier and its score value corresponding to the search e-commerce data search request.

[0035] Based on the scoring values ​​of each e-commerce data identifier, data operations are performed on each e-commerce data identifier to generate a corresponding e-commerce data identifier set;

[0036] From each of the data nodes, read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set, and generate an e-commerce data search result containing each of the e-commerce data.

[0037] In a further embodiment, after enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request, the following steps are included:

[0038] In response to merchant user re-evaluation events, obtain the merchant types that have been re-evaluated by multiple merchant users, as well as the new merchant index routing rules and the new search scenario index routing rules;

[0039] Reconstruct new merchant indexes corresponding to multiple merchant types, generate a new merchant index pool composed of each new merchant index, and reconstruct a corresponding new search scenario index pool for each new merchant index;

[0040] Based on the new merchant type of each merchant user, the corresponding new merchant index in the new merchant index pool is allocated to each merchant user. The e-commerce data of each merchant user is stored in the e-commerce data cluster corresponding to its new merchant index, and each new merchant index is updated accordingly. The new search scenario index in the new search scenario index pool corresponding to each new merchant index is also updated accordingly.

[0041] Replace the currently online merchant index pool and search scenario index pool with the new merchant index pool and the new search scenario index pool, respectively. Also replace the currently online merchant index routing rules and search scenario index routing rules with the new merchant index routing rules and the new search scenario index routing rules, respectively.

[0042] On the other hand, an index routing device provided to suit one of the purposes of this application includes: a search request response module, used to respond to e-commerce data search requests and obtain a merchant identifier and a search scenario identifier corresponding to the e-commerce data search request; a merchant index routing module, used to determine the target merchant index corresponding to the merchant identifier from a merchant index pool according to merchant index routing rules, wherein the merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes; and a data cluster determination module, used to determine one or more target e-commerce data clusters corresponding to the target merchant index, wherein the e-commerce data clusters include high-performance e-commerce data clusters, medium-performance e-commerce data clusters, and low-performance e-commerce data clusters. The system includes an e-commerce data cluster; a scenario index routing module, used to determine the target search scenario index corresponding to the search scenario identifier from the search scenario index pool corresponding to the target merchant index according to the search scenario index routing rules, wherein the search scenario index pool contains merchant search scenario indexes, buyer search scenario indexes, and other e-commerce search scenario indexes; and a search result response module, used to determine the search e-commerce data cluster corresponding to the target search scenario index from each of the target e-commerce data clusters, enable the target search scenario index in the search e-commerce data cluster to process the e-commerce data search request, and obtain the corresponding e-commerce data search results to respond to the e-commerce data search request.

[0043] In a further embodiment, the merchant index routing module includes:

[0044] The merchant identifier hash calculation submodule is used to obtain the merchant identifier corresponding to the e-commerce data search request, and use a hash algorithm to calculate the hash feature code corresponding to the merchant identifier.

[0045] The index sequence number calculation submodule is used to determine the number of merchant indexes in the merchant index pool, perform a modulo operation between the hash feature code and the number of merchant indexes, and use the calculation result as the index sequence number.

[0046] The target merchant index determination submodule is used to determine the merchant index corresponding to the index sequence number in the merchant index pool, and use the merchant index as the target merchant index corresponding to the merchant identifier.

[0047] In a further embodiment, the search result response module includes:

[0048] The data node determination submodule is used to determine all shards corresponding to the target search scenario index in the search e-commerce data cluster;

[0049] The e-commerce data identifier determination submodule is used to broadcast e-commerce data search requests to the data nodes of each of the shards, and drive each of the data nodes to search for the e-commerce data identifier and its score value corresponding to the search e-commerce data search request.

[0050] The identifier queue generation submodule is used to perform data operations on each of the e-commerce data identifiers based on the scoring value of each e-commerce data identifier, and generate a corresponding e-commerce data identifier set.

[0051] The search result generation submodule is used to read e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set from each of the data nodes, and generate e-commerce data search results containing each of the e-commerce data.

[0052] In a preferred embodiment, the search result response module further includes:

[0053] The request full load response submodule is used to respond to the search request full load event of the e-commerce data cluster and determine the unfulfilled search scenario index in the search scenario index pool corresponding to the target merchant index.

[0054] The e-commerce data identifier determination submodule is used to determine the data nodes corresponding to all shards of the index for the unfulfilled search scenario in the search e-commerce data cluster, broadcast the e-commerce data search request to the data nodes of each shard, and drive each data node to search out the e-commerce data identifier and its score value corresponding to the search e-commerce data search request.

[0055] The identifier queue generation submodule is used to perform data operations on each of the e-commerce data identifiers based on the scoring value of each e-commerce data identifier, and generate a corresponding e-commerce data identifier set.

[0056] The search result generation submodule is used to read e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set from each of the data nodes, and generate e-commerce data search results containing each of the e-commerce data.

[0057] In another aspect, a computer device provided for one of the purposes of this application includes a central processing unit and a memory, the central processing unit being configured to invoke and run a computer program stored in the memory to perform the steps of the index routing method described in this application.

[0058] In another aspect, a computer-readable storage medium is provided to suit another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the described index routing method, which, when invoked by a computer, performs the steps included in the method.

[0059] The technical solution of this application has many advantages, including but not limited to the following aspects:

[0060] This application constructs merchant indexes for different merchant types within an e-commerce data search service, and further constructs search scenario indexes for different search scenarios. This allows for the layered storage of e-commerce data from independent e-commerce platforms belonging to different merchants across e-commerce data clusters with varying performance. Based on the two dimensions of merchants and search scenarios, it performs data layering and search request routing for e-commerce data used in the e-commerce data search service. For independent e-commerce platforms with varying traffic levels, merchants are categorized, and high-quality merchant indexes, ordinary merchant indexes, and low-traffic merchant indexes are constructed for different traffic types and applied to e-commerce data clusters with varying performance. This allows for layered processing of e-commerce data within the e-commerce data search service, distinguishing search requests from independent websites with varying traffic levels and routing them to the corresponding performance e-commerce data clusters for processing. This ensures that high-quality merchant websites can utilize high-performance e-commerce data clusters to handle high-concurrency search requests, while low-traffic merchant websites can use lower-performance e-commerce data clusters. This application processes e-commerce data search requests, further categorizing them not only by merchant but also by search scenario. It constructs search scenario indexes for different search scenarios within an independent e-commerce platform to distribute search requests across these scenarios, ensuring stability and response speed for e-commerce data searches. Specifically, this application builds indexes and sets corresponding search request routing rules based on merchant and search scenario dimensions to distribute e-commerce data search requests from independent websites of merchants with varying traffic levels. It uses e-commerce data clusters with different performance levels to process these requests, creating a system for distributing search requests and allocating search performance resources for merchants with varying traffic levels. Furthermore, the application constructs search scenario indexes for different search scenarios within the independent e-commerce platform, broadcasting different e-commerce data search requests to e-commerce data clusters with different performance levels for processing. This satisfies the search performance requirements of different search scenarios, ensuring search efficiency and stability, and rationally allocating search computing resources within the e-commerce data search service. This maximizes the utilization of search computing resources and effectively improves the service stability of the e-commerce search data service. Attached Figure Description

[0061] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0062] Figure 1 The network architecture of the e-commerce platform exemplified in this application;

[0063] Figure 2 This is a flowchart illustrating a typical embodiment of the index routing method of this application;

[0064] Figure 3 This is a flowchart illustrating the specific implementation method of initializing and constructing the merchant index pool and search scenario index pool, as well as the e-commerce data storage of the e-commerce data cluster in this application.

[0065] Figure 4 This is a flowchart illustrating the specific implementation method of e-commerce data updating in the e-commerce data search service described in this application.

[0066] Figure 5 This is a flowchart illustrating the specific implementation method of determining the target merchant index corresponding to the merchant identifier based on the merchant index routing rules in this application.

[0067] Figure 6 This is a flowchart illustrating the specific implementation method of enabling target search scenario index processing of e-commerce data search requests in the e-commerce data cluster driven by this application.

[0068] Figure 7 This is a flowchart illustrating the specific implementation method in this application for scheduling unprocessed e-commerce data search requests from a fully loaded e-commerce data cluster to an idle e-commerce data cluster for processing.

[0069] Figure 8 This is a flowchart illustrating the specific implementation method for updating the merchant index pool and search scenario index pool in the e-commerce data search service, as well as the corresponding update of e-commerce data stored in the e-commerce data cluster in this application.

[0070] Figure 9 This is a schematic block diagram of the index routing device of this application;

[0071] Figure 10 This is a schematic diagram of the structure of a computer device used in this application. Detailed Implementation

[0072] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0073] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0074] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0075] like Figure 1 In the network architecture shown, the e-commerce platform 82 is deployed on the Internet to provide corresponding services to its users. Similarly, the devices 80 of the merchant users and the devices 81 of the consumer users of the e-commerce platform 82 are also connected to the Internet to use the services provided by the e-commerce platform.

[0076] An exemplary e-commerce platform 82 provides supply and demand matching of products and / or services to the general public through the Internet infrastructure. In e-commerce platform 82, products and / or services are provided as commodity information. For the sake of simplicity, the concepts of commodity and product are used in this application to refer to the products and / or services in e-commerce platform 82. Specifically, these may be physical products, digital products, tickets, service subscriptions, other offline services, etc.

[0077] In reality, various entities can access e-commerce platform 82 as users and utilize its online services to participate in the business activities facilitated by the platform. These entities can be natural persons, legal persons, or social organizations. Corresponding to the two types of entities in business activities—merchants and consumers—e-commerce platform 82 has two corresponding categories of users: merchant users and consumer users. Entities involved in the product distribution chain in business activities, including manufacturers, sellers, retailers, and logistics providers, can all use online services on e-commerce platform 82 as merchant users. Similarly, consumers in business activities, including actual or potential consumers, can use online services on e-commerce platform 82 as consumer users. In actual business activities, the same entity can operate as both a merchant user and a consumer user; this should be interpreted flexibly.

[0078] The infrastructure used to deploy the e-commerce platform 82 mainly includes the backend architecture and frontend devices. The backend architecture runs various online services through a service cluster, including middleware or frontend services for the platform, services for consumers, and services for merchants, to enrich and improve its service functions. The frontend devices mainly cover the terminal devices used by users as clients to access the e-commerce platform 82, including but not limited to various mobile terminals, personal computers, and point-of-sale devices. For example, merchant users can use their terminal device 80 to enter product information for their online stores or use the interfaces opened by the e-commerce platform to generate their product information; consumer users can use their terminal device 81 to access the webpage of the online store implemented by the e-commerce platform 82, trigger the shopping process by clicking the shopping button provided on the webpage, and call various online services provided by the e-commerce platform 82 during the shopping process to achieve the purpose of placing an order.

[0079] In some embodiments, the e-commerce platform 82 may be implemented via a processing facility including a processor and memory, which stores a set of instructions that, when executed, cause the e-commerce platform 82 to perform the e-commerce and support functions as described in this application. The processing facility may be part of a server, client, network infrastructure, mobile computing platform, cloud computing platform, fixed computing platform, or other computing platform, and may provide electronic components, merchant devices, payment gateways, application developers, marketing channels, transportation providers, customer devices, point-of-sale devices, etc., for the e-commerce platform 82.

[0080] E-commerce platform 82 can provide online services such as cloud computing services, Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Hosted Software as a Service, Mobile Backend as a Service (MBaaS), and Information Technology Management as a Service (ITMaaS). In some embodiments, the various functional components of e-commerce platform 82 can be implemented to operate on various platforms and operating systems. For example, for an online store, its administrator user enjoys the same or similar functions regardless of whether it is on iOS, Android, HomonyOS, or a web page.

[0081] E-commerce platform 82 enables merchants to create their own independent websites to run their online stores. It provides merchants with corresponding business management engine instances, allowing them to establish, maintain, and operate one or more online stores across these independent websites. The business management engine instance can be used for content management, task automation, and data management for one or more online stores. It can be configured through interfaces or built-in components to support various specific business processes in the online store, supporting business activities. Independent websites are the infrastructure of e-commerce platform 82, which offers cross-border services. Merchants can maintain their online stores relatively independently and centrally based on these independent websites. Independent websites typically have dedicated domain names and storage space, and different independent websites are relatively independent. E-commerce platform 82 can provide standardized or customized technical support for a large number of independent websites, allowing merchants to customize a business management engine instance that suits their needs and use it to maintain one or more online stores.

[0082] Online stores can be configured and maintained in the backend by merchant users logging into their Business Management Engine instance as administrators. Supported by the various online services provided by the e-commerce platform 82's infrastructure, merchant users can configure various functions within their online stores and view various data as administrators. For example, merchant users can manage various aspects of their online stores, such as viewing recent online store activities, updating the online store's product catalog, managing orders, recent visit activity, and total order activity. Merchant users can also view more detailed information about their business and visitors to their online store by obtaining reports or metrics, such as displaying a sales summary of the merchant's overall business, specific sales and engagement data from promotional sales and marketing channels, etc.

[0083] E-commerce platforms 82 can provide communication facilities and associated merchant interfaces for electronic communication and marketing. For example, they can utilize electronic messaging aggregation facilities to collect and analyze communication interactions between merchants, consumers, merchant devices, customer devices, point-of-sale devices, etc., aggregating and analyzing communications to increase the potential for product sales. For instance, a consumer may have product-related questions, which could lead to a dialogue between the consumer and the merchant (or an automated processor-based agent representing the merchant), where the communication facilities handle the interaction and provide the merchant with analysis on how to increase the probability of a sale.

[0084] In some embodiments, applications suitable for installation on terminal devices can be provided to serve the access needs of different users, enabling various users to access the e-commerce platform 82 by running the application on their terminal devices. Examples include the merchant backend module of online stores within the e-commerce platform 82. During the process of conducting business activities through these functions, the e-commerce platform 82 can implement various functions related to business activities as middleware or online services and expose corresponding interfaces. Then, toolkits corresponding to the interface access functions are embedded into the application to achieve functional expansion and task completion. The business management engine can include a series of basic functions and expose these functions to online services and / or applications via APIs. Online services and applications use the corresponding functions by remotely calling the corresponding APIs.

[0085] With the support of various components of the Business Management Engine instance, the e-commerce platform 82 can provide online shopping functionality, enabling merchants to connect with customers in a flexible and transparent manner. Consumers can select items online, create orders, provide delivery addresses in the orders, and complete payment confirmation. Merchants can then review and fulfill or cancel orders. The review component included with the Business Management Engine instance ensures compliant use of business processes, guaranteeing that orders are suitable for fulfillment before actual execution. Orders may sometimes be fraudulent and require verification (e.g., ID checks). Payment methods that require merchants to wait to ensure receipt of funds can mitigate this risk, and so on. Order risks may arise from fraud detection tools submitted by third parties through order risk APIs, etc. Before fulfillment, merchants may need to obtain or wait to receive payment information to mark the order as paid before preparing to deliver the product. Such situations can all be subject to appropriate review. The review process can be implemented by the fulfillment component. Merchants can leverage fulfillment components to review and adjust operations, and trigger related fulfillment services. These include: manual fulfillment services, used when merchants select and pack products into boxes, purchase shipping labels and enter tracking numbers, or simply mark items as fulfilled; custom fulfillment services, which can define email notifications; API fulfillment services, which can trigger third-party applications to create fulfillment records; legacy fulfillment services, which can trigger custom API calls from the Commerce Management Engine to third parties; and gift card fulfillment services, which can generate and activate gift cards. Merchants can use an order printer application to print shipping documents. The fulfillment process can be executed once items are packed and ready for shipment, tracked, delivered, and verified by the consumer.

[0086] It can be seen that the services provided by e-commerce platforms are based on products, and the corresponding product data is the foundational data of these platforms. Providing product information through this data and mining and utilizing it are fundamental to various technical services. This includes using user transaction data and product data from the e-commerce platform to provide basic services for the operation of a real-time streaming data processing system. Therefore, the real-time streaming data processing system of this application can run on any one or more servers within the e-commerce platform's cluster, enabling the use of various product data provided by the e-commerce platform to achieve various functions.

[0087] The index routing method of this application can be programmed into a computer program product and deployed on a client or server for execution. For example, in an exemplary application scenario of this application, it can be deployed on the server of an e-commerce customer service platform. In this way, the method can be executed by human-computer interaction with the process of the computer program product through a graphical user interface by accessing the interface opened after the computer program product is running.

[0088] Please see Figure 2 The index routing method of this application, in its typical embodiment, includes the following steps:

[0089] Step S11: Respond to the e-commerce data search request and obtain the merchant identifier and search scenario identifier corresponding to the e-commerce data search request.

[0090] The independent e-commerce platform of this application has multiple independent websites managed by the merchants themselves. Each independent website has a corresponding single online store. Each online store is created, operated, and maintained by the merchant. For example, the merchant can list products for sale on their independent website. The product information of the listed products will be stored as e-commerce data in the server of the independent e-commerce platform. The platform provides e-commerce data storage services to each merchant and e-commerce data search services using the index routing method described in this application.

[0091] The e-commerce data search service provides corresponding e-commerce data search functions for merchants and buyers on independent e-commerce platforms. For example, for merchants, the e-commerce data search service will provide them with e-commerce data such as store data, product data, order data, and logistics data on their independent websites. For buyers, after visiting an independent website on the platform, the e-commerce data search service will retrieve product data and provide them with product browsing pages. Alternatively, buyers can use the product search function provided by the e-commerce data search service on the independent website to search for product data matching the product search text. In addition, buyers can also query order data or logistics data corresponding to the products they purchased on the independent website. All kinds of e-commerce data are generally stored in various e-commerce data clusters of the e-commerce data search service, so as to retrieve the corresponding e-commerce data for merchants and buyers by searching each e-commerce data cluster.

[0092] In addition, the e-commerce data search service also provides e-commerce data search functions for other e-commerce businesses within independent e-commerce platforms, such as e-commerce logistics or e-commerce payment services. For example, e-commerce logistics services can use the e-commerce data search service to search for order data information from the corresponding merchant's independent website to generate corresponding logistics orders. E-commerce payment services can use the e-commerce data search service to search for order and logistics data information from the corresponding merchant's independent website to generate corresponding payment orders. Of course, besides other e-commerce businesses within the platform, third-party platforms can also use the e-commerce data search service to query e-commerce data from the independent websites of merchants within the independent e-commerce platform, such as third-party advertising platforms, third-party logistics platforms, or third-party payment platforms.

[0093] The e-commerce data search service is generally built on a search engine implemented using Elasticsearch. Elasticsearch is a full-text search engine based on Lucene, also known as a distributed search engine. It is developed in Java and is open source, featuring distributed HTTP Web and frameless JSON document support.

[0094] When merchant users, buyer users, other e-commerce businesses on independent e-commerce platforms, and third-party platforms use the e-commerce data search service to search for e-commerce data on a corresponding merchant user's independent website, a corresponding e-commerce data search request will be generated in their client. This request will then be pushed to the e-commerce data search service to drive it to search for and respond to the e-commerce data requested. For example, when a buyer user searches for products on their own independent website using the product search function, a corresponding e-commerce data search request will be generated. This request points to the merchant user on their own independent website and their search scenario. Here, the merchant user refers to the merchant on the independent website where the buyer user is searching for products, and the search scenario refers to the scenario in which the buyer user uses the e-commerce data search service. The merchant identifier and search scenario identifier corresponding to the e-commerce data search request are, respectively, the merchant identifier of the product user on the independent website where the buyer user is searching for products, and the search scenario identifier corresponding to the buyer user using the e-commerce data search service.

[0095] The merchant identifier generally refers to the merchant identity feature code corresponding to different merchant users in an independent e-commerce platform. That is, it is preset for merchant users registered on the platform to distinguish different merchant users. Of course, the merchant identifier can also be set for each merchant user in the e-commerce data search service, as long as the merchant identifier can distinguish different merchant users.

[0096] The search scenario identifier is set according to the user's identity or business scenario when using the e-commerce data search service. For example, when a merchant user uses the e-commerce data search service, the search scenario identifier of the generated e-commerce data search request will be characterized as a merchant search scenario; when a buyer user uses the e-commerce data search service, the search scenario identifier of the generated e-commerce data search request will be characterized as a buyer search scenario; when the platform's logistics business uses the e-commerce data search service, the search scenario identifier of the generated e-commerce data search request will be characterized as a logistics business search scenario; when a third-party platform uses the e-commerce data search service, the search scenario identifier of the generated e-commerce data search request will be characterized as a third-party business search scenario, and so on. Different user identities or business scenarios will result in different search scenario identifiers for the generated e-commerce data search requests.

[0097] In the search engine of the e-commerce data search service, e-commerce data from independent websites operated by merchants is categorized and stored in e-commerce data clusters of different performance levels based on the popularity of those independent websites. The e-commerce data search service uses merchant indexes corresponding to different merchant types for merchant index routing. When reading or writing e-commerce data from a specific merchant, the service determines the e-commerce data cluster where the merchant's data is stored through merchant index routing. Merchants are categorized into different merchant types based on their popularity, including high-quality merchants, ordinary merchants, and niche merchants. By statistically analyzing the independent website's traffic, sales volume, order volume, search requests, and e-commerce data volume, and weighting each independent website's business information, the service determines the merchant type for each merchant, thus classifying them into high-quality, ordinary, and niche merchants. This determines the e-commerce data cluster where each merchant's data is stored and the corresponding merchant index. The e-commerce data clusters are divided into high-quality, ordinary, and niche categories. The e-commerce data clusters are categorized into high-performance, medium-performance, and low-performance clusters. The high-performance cluster has higher computational performance than both the medium-performance and low-performance clusters, and the medium-performance cluster has higher computational performance than the low-performance cluster. Therefore, it can be understood that the high-performance cluster generally corresponds to a high-quality merchant index, the medium-performance cluster generally corresponds to a common merchant index, and the low-performance cluster generally corresponds to a niche merchant index. The medium-performance database typically backs up the e-commerce data of high-quality merchants in the high-performance cluster, and the low-performance database typically backs up the e-commerce data of both high-quality and common merchants in the high-performance and medium-performance clusters. This is to facilitate the construction of search scenario indexes and to prevent e-commerce data from becoming unusable due to cluster crashes.

[0098] In addition to a merchant index pool composed of merchant indexes corresponding to each of the aforementioned merchant types, the e-commerce data search service also has its own corresponding search scenario index pool. The search scenario index pool contains search scenario indexes corresponding to different search scenario types. These search scenario indexes are categorized into merchant search scenario indexes, buyer search scenario indexes, and other e-commerce search scenario indexes. Specifically, the merchant search scenario indexes serve search scenarios where merchants use the e-commerce data search service; the buyer search scenario indexes serve search scenarios where buyers use the e-commerce data search service; and the other e-commerce search scenario indexes serve search scenarios where other e-commerce businesses or third-party platforms within the platform use the e-commerce data search service. The merchant search scenario indexes, buyer search scenario indexes, and other e-commerce search scenario indexes in the search scenario index pools of different merchant indexes generally correspond to different e-commerce data clusters. For example, in the search scenario index pool of the high-quality merchant index, the merchant search scenario index is applied to the high-performance e-commerce data cluster storing the original e-commerce data of high-quality merchant users. The home search scenario index is applied to the medium-performance e-commerce data cluster storing backup e-commerce data of high-quality merchant users. The other e-commerce search scenario indexes are applied to the medium-performance e-commerce data cluster or the low-performance e-commerce data cluster storing backup e-commerce data of high-quality merchant users. In the search scenario index pool of the ordinary merchant index, the merchant search scenario index is applied to the medium-performance e-commerce data cluster storing the original e-commerce data of ordinary merchant users. The buyer search scenario index is applied to the low-performance e-commerce data cluster storing backup e-commerce data of ordinary merchant users. The other e-commerce search scenario indexes are applied to the medium-performance e-commerce data cluster or the low-performance e-commerce data cluster storing the original e-commerce data of ordinary merchant users or the backup e-commerce data of ordinary merchant users. In the search scenario index pool of the unpopular merchant index, the merchant search scenario index, the buyer search scenario index, and the other e-commerce search scenario indexes are generally applied to the low-performance e-commerce data cluster storing e-commerce data of unpopular merchant users. Of course, those skilled in the art can flexibly design the e-commerce data clusters corresponding to each search scenario index, which will not be elaborated here.

[0099] Step S12: Based on the merchant index routing rules, determine the target merchant index corresponding to the merchant identifier from the merchant index pool. The merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and infrequently used merchant indexes.

[0100] As mentioned above, the search engine for the e-commerce data search service is generally composed of a high-performance e-commerce data cluster, a medium-performance e-commerce data cluster, and a low-performance e-commerce data cluster in its physical cluster. It routes e-commerce data search requests to the corresponding e-commerce data clusters for processing through a merchant index pool consisting of a high-quality merchant index, a regular merchant index, and a less popular merchant index, as well as a search scenario index pool containing the merchant search scenario index, the buyer search scenario index, and other e-commerce search scenario indexes for each merchant index within the merchant index pool.

[0101] The merchant index routing rule is used to determine the merchant index corresponding to the e-commerce data search request in the merchant index pool. The merchant index routing rule has a merchant indexing algorithm, which is generally constructed based on a hash feature code algorithm or a consistent hashing algorithm, and a modulo algorithm applied to the hash feature code algorithm or the consistent hashing algorithm. The parameters specified by each algorithm are the merchant identifier corresponding to the e-commerce data search request and the number and index sequence number of the indexes in the merchant index pool, respectively. Specifically, after obtaining the e-commerce data search request that reads the response and obtaining the merchant identifier corresponding to the e-commerce data search request, the hash feature code corresponding to the merchant identifier is calculated using the hash modulo algorithm or the consistent hashing algorithm. Then, the number of merchant indexes in the merchant index pool is determined. The hash feature code and the number of merchant indexes are moduloed, and the calculation result is used as the index sequence number to determine the merchant index corresponding to the index sequence number in the merchant index pool. This merchant index is then used as the target merchant index corresponding to the merchant identifier.

[0102] The mathematical expression constructed by the hash feature code algorithm or the consistent hash algorithm and modulo algorithm in the merchant index routing rule is generally hash(user_id)%num, where user_id is the merchant identifier and num is the number of merchant indexes in the merchant index pool. For example, when the index numbers of the high-quality merchant index, the ordinary merchant index, and the unpopular merchant index in the merchant index pool are 1, 2, and 3 respectively, when the index number corresponding to the merchant identifier is determined to be 1 using the mathematical expression, the e-commerce data search request to which the merchant identifier belongs will correspond to the high-quality merchant index in the merchant index pool.

[0103] Step S13: Determine one or more target e-commerce data clusters corresponding to the target merchant index, wherein the e-commerce data clusters include high-performance e-commerce data clusters, medium-performance e-commerce data clusters, and low-performance e-commerce data clusters.

[0104] By using the merchant index routing rules, the merchant identifier of the current e-commerce data search request is determined. After the target merchant index in the merchant index pool is determined, one or more target e-commerce data clusters corresponding to the target merchant index in the search engine of the e-commerce data search service will be determined. As mentioned above, the e-commerce data clusters include the high-performance e-commerce data cluster, the medium-performance e-commerce data cluster, and the low-performance e-commerce data cluster.

[0105] The high-performance e-commerce data cluster is generally applied to the high-quality merchant index, the medium-performance e-commerce data cluster is generally applied to both the high-quality merchant index and the ordinary merchant index, and the low-performance e-commerce merchant cluster is generally applied to both the ordinary merchant index and the unpopular merchant index. Once the target merchant index corresponding to the merchant identifier of the current e-commerce data search request is determined, one or more e-commerce data clusters corresponding to the target merchant index will be identified as the target e-commerce data cluster. For example, if the target merchant index corresponding to the merchant identifier of the current e-commerce data search request is the high-quality merchant index, then the high-performance e-commerce data cluster and the medium-performance e-commerce data cluster applied to the high-quality merchant index will be identified as the target e-commerce data cluster corresponding to the e-commerce data search request.

[0106] Step S14: Based on the search scenario index routing rules, determine the target search scenario index corresponding to the search scenario identifier from the search scenario index pool corresponding to the target merchant index. The search scenario index pool contains merchant search scenario indexes, buyer search scenario indexes, and other e-commerce search scenario indexes.

[0107] After determining the merchant identifier of the current e-commerce data search request and the target merchant index corresponding to it in the merchant index pool, in addition to determining one or more target e-commerce data clusters corresponding to the target merchant index, the search scenario index pool corresponding to the search target merchant index will also be obtained, so as to determine the target search scenario index corresponding to the search scenario identifier of the e-commerce data search request in the search scenario index pool according to the search scenario index routing rules.

[0108] The search scenario index pool includes merchant search scenario indexes for merchants to search for e-commerce data, buyer search scenario indexes for buyers to search for e-commerce data, and other e-commerce search scenario indexes for other e-commerce businesses on the platform or third-party platforms to search for e-commerce data.

[0109] The search scenario index routing rules include a search scenario indexing algorithm, which is generally constructed based on a hash modulo algorithm or a consistent hashing algorithm, as well as a modulo algorithm applied to the hash modulo algorithm or the consistent hashing algorithm. The parameters specified by each algorithm are the search scenario identifier corresponding to the e-commerce data search request and the number and index sequence number of the indexes in the search scenario index pool, respectively. Specifically, after obtaining the e-commerce data search request that received the read response and obtaining the search scenario identifier corresponding to the e-commerce data search request, the hash modulo algorithm or the consistent hashing algorithm is used to calculate the hash feature code corresponding to the search scenario identifier, thereby determining the number of indexes of the search scenario in the search scenario index pool. The hash feature code and the number of indexes are then moduloed, and the calculation result is used as the index sequence number to determine the search scenario index corresponding to the index sequence number in the search scenario index pool. This search scenario index is then used as the target search scenario index corresponding to the search scenario identifier.

[0110] The e-commerce data indexed in each search scenario index within the search scenario index pool refers to the e-commerce merchants of the independent websites of the merchants in the merchant index corresponding to the search scenario index. In other words, the e-commerce data indexed in each search scenario index within the same search scenario index pool is generally the same. Although the performance of the e-commerce data clusters corresponding to each search scenario index is different, and performance resources can already be allocated based on the amount of search data and search requests in a search scenario, to better adapt to the search needs between different search scenarios, search scenario indexes acting on different search scenarios are generally customized. For example, the merchant search scenario index acting on e-commerce data searches by merchant users will be allocated more computing resources and a more frequent index refresh cycle to meet the data throughput and lower data latency requirements of the search scenario acting on merchant users. Conversely, the buyer search scenario index acting on e-commerce data searches by buyer users will use more data shards and a longer index refresh time compared to other e-commerce search scenario indexes to meet the needs of handling a large number of search requests and faster search request response speeds in the search scenario acting on buyer users. Designers in the art can flexibly customize each search scenario index according to the search scenario, which will not be elaborated further.

[0111] Step S15: From each of the target e-commerce data clusters, determine the search e-commerce data cluster corresponding to the target search scenario index, and enable the target search scenario index in the search e-commerce data cluster to process the e-commerce data search request, and obtain the corresponding e-commerce data search results to respond to the e-commerce data search request.

[0112] The routing determines the target merchant index corresponding to the merchant identifier of the current e-commerce data search request, and determines one or more target e-commerce data clusters corresponding to the target merchant index, as well as the search scenario identifier of the e-commerce data search request. After determining the target search scenario index corresponding to the search scenario index pool of the target merchant index, the routing determines the target e-commerce data cluster corresponding to the target search scenario index from each of the target e-commerce data clusters, so as to process the search e-commerce data cluster corresponding to the e-commerce data search request.

[0113] After identifying the e-commerce data cluster corresponding to the target search scenario index, the coordination node of the e-commerce search service's search engine will determine the data nodes corresponding to all shards of the target search scenario index within the e-commerce data cluster. The currently responding e-commerce data search request will then be broadcast to each of these data nodes. Each data node will then search for the corresponding e-commerce data identifiers and determine the scores for each e-commerce data item based on the e-commerce data search statements in the search request. Based on the scores of each e-commerce data identifier, any one or more data operations, such as merging, sorting, and pagination, will be performed on each e-commerce data identifier to generate an e-commerce data identifier set storing all the search e-commerce data identifiers. Finally, the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set will be read from each data node to generate e-commerce data search results containing all the e-commerce data.

[0114] The system generates corresponding e-commerce data search results and pushes these results to the client that made the corresponding e-commerce data search request. Alternatively, it pushes the search results to the business server of the independent e-commerce platform, where the business server further processes them. For example, if a buyer searches for product data on an independent website, the business server can sort and recommend products within the search results before pushing them to the buyer's client corresponding to the search request.

[0115] In one embodiment, since the indexes in each search scenario index pool within the same search scenario index pool generally contain the same e-commerce data, when the e-commerce data cluster corresponding to the current target search scenario index becomes overloaded due to an excessive number of e-commerce data search requests, an unloaded search scenario index corresponding to the e-commerce data cluster in the search scenario index pool where the target search scenario index is located will be scheduled. The e-commerce data cluster corresponding to the unloaded search scenario index will then be used to process the e-commerce data search requests that the e-commerce data cluster corresponding to the target search scenario index cannot handle. This addresses the problem of e-commerce data search service crashes caused by excessive request volume in a certain search scenario. By scheduling search scenario indexes to use unloaded e-commerce data clusters to process e-commerce data search requests that cannot be processed due to cluster overload, the stability of e-commerce data search services in other search scenarios within the e-commerce independent website platform is ensured.

[0116] The above typical embodiments and their variations fully disclose the implementation scheme of the index routing method of this application. However, various variations of the method can still be derived by changing and expanding some technical means. Other embodiments are briefly described below:

[0117] Please see Figure 3 In a further embodiment, before responding to an e-commerce data search request and obtaining the merchant identifier and search scenario identifier corresponding to the e-commerce data search request, the following steps are included:

[0118] Step S06: Construct merchant indexes corresponding to multiple merchant types, and construct multiple search scenario indexes corresponding to search scenario types for each merchant index, thereby generating a merchant index pool composed of the various merchant indexes, and constructing a search scenario index pool composed of the respective search scenario indexes for each merchant index. The merchant types include high-quality merchants, ordinary merchants, and niche merchants, and the search scenario types include merchant search scenarios, buyer-merchant search scenarios, and other e-commerce search scenarios.

[0119] When initializing the merchant index and search scenario index in the search engine of an independent e-commerce platform to build the corresponding merchant index pool and the search scenario index pool corresponding to each merchant index, firstly, the merchant indexes corresponding to different merchant types in the search engine are initialized and built. The merchant types include high-quality merchants, ordinary merchants, and niche merchants. The merchant index of the merchant type is the high-quality merchant index, the merchant index of the merchant type is the ordinary merchant index, and the merchant index of the merchant type is the niche merchant index. Then, the merchant index pool composed of the high-quality merchant index, ordinary merchant index, and niche merchant index is initialized and generated.

[0120] After constructing the merchant index pool, corresponding search scenario index pools will be initialized for the high-quality merchant index, ordinary merchant index and unpopular merchant index in the merchant index pool. The search scenario index pool contains search scenario indexes with different search scenario types, such as merchant search scenario index with search scenario type of merchant search scenario, buyer search scenario index with search scenario type of buyer search scenario, and other e-commerce search scenario index with search scenario type of other e-commerce search scenario.

[0121] Step S07: Obtain store traffic information for multiple merchant users on the independent e-commerce platform. This store traffic information includes store product sales volume, store order volume, store visits, and store exposure. Then, invoke the merchant user traffic analysis algorithm to determine the merchant type for each merchant user based on their store traffic information.

[0122] During the initialization and construction of the merchant index pool and the search scenario index pool corresponding to each merchant index in the merchant index pool, the store traffic information of each merchant user in the independent website e-commerce platform will be obtained, and the merchant user traffic analysis algorithm will be called to determine the merchant type of each merchant user based on the store traffic information of each merchant user, and then determine the corresponding merchant index of each merchant user in the merchant index pool based on the merchant type of each merchant user.

[0123] Step S08: Based on the merchant type of each merchant user, allocate the corresponding merchant index from the merchant index pool to each merchant user, store the e-commerce data of each merchant user in the e-commerce data cluster corresponding to its merchant index, and update each merchant index accordingly.

[0124] After completing the initial construction of the merchant index pool and the search scenario index pool corresponding to each merchant index in the merchant index pool, and determining the merchant type corresponding to each merchant user in the independent website e-commerce platform, the merchant index corresponding to each merchant user in the merchant index pool will be determined according to the merchant type of each merchant user.

[0125] After determining the merchant index corresponding to any merchant user, the e-commerce data in the independent website corresponding to the merchant user will be obtained and stored in the e-commerce data cluster corresponding to the merchant index. After storing each e-commerce data in the e-commerce dataset, the merchant index will be updated accordingly.

[0126] Step S09: Determine the search scenario index pool corresponding to the merchant index of each merchant user, and then determine the search scenario indexes in each search scenario index pool accordingly.

[0127] After determining the merchant index corresponding to any merchant user, the e-commerce data in the independent website corresponding to the merchant user is stored in the e-commerce data cluster corresponding to the merchant index. In addition to updating the merchant index, the search scenario indexes in the search scenario index pool of the merchant index are also updated accordingly.

[0128] In this embodiment, merchant users on independent e-commerce platforms with different traffic levels are categorized, and high-quality merchant indexes, ordinary merchant indexes, and niche merchant indexes are constructed to serve e-commerce data clusters with different performance levels. Furthermore, for independent websites with different traffic levels, e-commerce data is processed in a tiered manner within the e-commerce data search service. In addition to the merchant dimension, further categorization is performed at the search scenario dimension, constructing search scenario indexes corresponding to different search scenarios on the independent e-commerce platform. This allows for the distribution of search requests across different search scenarios on the independent website, thereby ensuring the stability of e-commerce data searches and the request response rate for each search scenario.

[0129] Please see Figure 4 In a further embodiment, after enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request, the following steps are included:

[0130] Step S16: Respond to the e-commerce data update request and obtain the merchant identifier and updated e-commerce data corresponding to the e-commerce data update request.

[0131] For independent e-commerce platforms, merchants on the platform will list new products on their independent websites, generating corresponding product data information. In addition, each e-commerce order generated on the independent website will generate corresponding order data information, and after an e-commerce order is shipped, corresponding logistics data information will also be generated. Therefore, the newly generated e-commerce data needs to be written to the e-commerce data search service or deleted from the e-commerce data search service. When a merchant's independent website generates new e-commerce data, it will push a corresponding e-commerce data update request to the e-commerce data search service to drive the e-commerce data search service to write the newly generated e-commerce data to the corresponding e-commerce data cluster and update the index.

[0132] After responding to an e-commerce data update request, the e-commerce data search service will obtain the merchant identifier corresponding to the e-commerce data update request and the updated e-commerce data to be written.

[0133] Step S17: Based on the merchant index routing rules, determine the target merchant index corresponding to the merchant identifier from the merchant index pool, and determine one or more e-commerce data clusters corresponding to the target merchant index.

[0134] After obtaining the merchant identifier corresponding to the current e-commerce data update request, the target merchant index corresponding to the merchant identifier will be determined from the merchant index pool according to the merchant index routing rules, and one or more e-commerce data clusters that the target merchant index is applied to will be determined.

[0135] Step S18: Update the e-commerce data to each of the e-commerce data clusters, and update the target merchant index accordingly.

[0136] After determining one or more e-commerce data clusters that the target merchant index corresponding to the merchant identifier of the current e-commerce data update request is applied to, the e-commerce data update request is written to each of the e-commerce data clusters for updating, and the target merchant index is updated accordingly.

[0137] Step S19: Determine the search scenario index pool corresponding to the target merchant index, and update each search scenario index in the search scenario index pool accordingly.

[0138] After updating the e-commerce data of the current response e-commerce data update request to each e-commerce data cluster, in addition to updating the target merchant index corresponding to the merchant identifier of the e-commerce data update request, it will also update each search scenario index in the search scenario index pool corresponding to the target merchant index.

[0139] In this embodiment, when any merchant user's independent website generates new e-commerce data on the independent website e-commerce platform, the merchant index corresponding to the merchant user will be determined according to the merchant routing rules. Then, the new e-commerce data will be written to the e-commerce data cluster corresponding to the merchant index. After that, the merchant index and each search scenario index in its search scenario index pool will be updated accordingly to ensure that the e-commerce data search service can search for new e-commerce data and to ensure the integrity and real-time performance of the search data of the e-commerce data search service.

[0140] Please see Figure 5 In a further embodiment, based on the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool, wherein the merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes, including the following steps:

[0141] Step S121: Obtain the merchant identifier corresponding to the e-commerce data search request, and use a hash algorithm to calculate the hash feature code corresponding to the merchant identifier.

[0142] The merchant index routing rules contain a merchant index algorithm. The merchant index algorithm is generally constructed based on a hash algorithm or a consistent hash algorithm, as well as a modulo algorithm applied to the hash modulo algorithm or the consistent hash algorithm. The mathematical expression constructed by the hash modulo algorithm or the consistent hash algorithm and the modulo algorithm is generally hash(user_id)%num, where user_id is the merchant identifier and num is the number of merchant indexes in the merchant index pool.

[0143] To obtain the merchant identifier corresponding to the current e-commerce data search request, a hash algorithm will be used to calculate the hash feature code corresponding to the merchant identifier.

[0144] Step S122: Determine the number of merchant indexes in the merchant index pool, perform a modulo operation between the hash feature code and the number of merchant indexes, and use the result as the index sequence number.

[0145] After determining the hash feature code corresponding to the merchant identifier, the number of merchant indexes in the merchant index pool of the e-commerce data search service will be determined. Then, the hash feature code and the number of merchant indexes will be moduloed to calculate the corresponding calculation result, and the calculation result will be used as the index sequence number.

[0146] Step S123: Determine the merchant index corresponding to the index sequence number in the merchant index pool, and use this merchant index as the target merchant index corresponding to the merchant identifier:

[0147] For example, when the index numbers of the high-quality merchant index, the ordinary merchant index, and the unpopular merchant index in the merchant index pool are 1, 2, and 3 respectively, when the calculation result indicates that the index number is 1, the e-commerce data search request to which the merchant identifier belongs will correspond to the high-quality merchant index in the merchant index pool.

[0148] In this embodiment, the merchant identifier corresponding to the e-commerce data search request is determined by the merchant index algorithm in the merchant index routing rules. The corresponding merchant index in the merchant index pool is used to route the e-commerce data search request to the corresponding merchant index, so as to divert e-commerce data search requests from independent websites of merchants with different traffic popularity. E-commerce data clusters with different performance are used to process e-commerce data search requests from independent websites of merchants with different traffic popularity, thus forming a search request diversion and search performance resource allocation for merchants with different traffic popularity.

[0149] Please see Figure 6 In a further embodiment, the process of enabling the target search scenario index to handle the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request includes the following steps:

[0150] Step S151: Determine all shards in the e-commerce data cluster corresponding to the target search scenario index:

[0151] Once the target search scenario index corresponding to the current e-commerce data search request is determined, and after determining the target search scenario index in the e-commerce data cluster, all shards corresponding to the target search scenario index in the e-commerce data cluster will be determined.

[0152] Step S152: Broadcast the e-commerce data search request to each of the data nodes in the shards, driving each data node to search for the e-commerce data identifier and its score value corresponding to the e-commerce data search request.

[0153] The system identifies all shards in the e-commerce data cluster corresponding to the target search scenario index, broadcasts the current e-commerce data search request to the data nodes of each shard, drives each data node to search for the corresponding e-commerce data based on the search text of the e-commerce data search request, and determines the e-commerce data identifier and score value corresponding to each e-commerce data.

[0154] Step S153: Based on the scoring values ​​of each e-commerce data identifier, perform data operations on each e-commerce data identifier to generate a corresponding e-commerce data identifier set:

[0155] After determining the e-commerce data identifier and score value corresponding to each e-commerce data in the search, the e-commerce data identifier will be subjected to any one or more data operations such as merging, sorting, and pagination based on the score value of each e-commerce data identifier, thereby generating a corresponding e-commerce data identifier set.

[0156] Step S154: From each of the data nodes, read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set, and generate an e-commerce data search result containing each of the e-commerce data.

[0157] Based on the e-commerce data identifiers in the e-commerce data identifier set, the data nodes that store the e-commerce data corresponding to each e-commerce data identifier are accessed to obtain the e-commerce data corresponding to each e-commerce data identifier from each data node, and then generate e-commerce data search results with each e-commerce data.

[0158] In this embodiment, by constructing search scenario indexes for different search scenarios in the independent e-commerce platform, different e-commerce data search requests are broadcast to e-commerce data clusters with different performance levels for processing, so as to meet the search performance required for different search scenarios and ensure the search efficiency and stability of different search scenarios.

[0159] Please see Figure 7In a further embodiment, enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster includes the following steps:

[0160] Step S151': In response to the search request full load event of the e-commerce data cluster, determine the unloaded search scenario indexes in the search scenario index pool corresponding to the target merchant index:

[0161] The aforementioned search request overload event refers to an event triggered when an e-commerce data cluster is overloaded due to an excessive number of simultaneous e-commerce data search requests, making it unable to process subsequent e-commerce data search requests. When a search request overload event is triggered in any e-commerce data cluster, the e-commerce data cluster in the search scenario index pool corresponding to the target merchant index of the other e-commerce search requests to be processed is identified as an unloaded search scenario index. This unloaded search scenario index is then used to process the other e-commerce search requests.

[0162] Step S152': Determine the data nodes corresponding to all shards of the incomplete search scenario index in the e-commerce data cluster, broadcast the e-commerce data search request to the data nodes of each shard, and drive each data node to search for the e-commerce data identifier and its score value corresponding to the e-commerce data search request.

[0163] After identifying the index of the unloaded search scenario that helps handle e-commerce data search requests that cannot be processed by the fully loaded e-commerce data cluster, the unprocessable e-commerce data search requests are broadcast to the data nodes corresponding to all shards of the unloaded search scenario index in the search e-commerce data cluster, driving each data node to search for the e-commerce data identifier and its score value corresponding to the search e-commerce data search request.

[0164] Step S153': Based on the scoring values ​​of each e-commerce data identifier, perform data operations on each e-commerce data identifier to generate a corresponding e-commerce data identifier set:

[0165] After determining the e-commerce data identifier and score value corresponding to each e-commerce data in the search, the e-commerce data identifier will be subjected to any one or more data operations such as merging, sorting, and pagination based on the score value of each e-commerce data identifier, thereby generating a corresponding e-commerce data identifier set.

[0166] Step S154': Read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set from each of the data nodes, and generate an e-commerce data search result containing each of the e-commerce data:

[0167] Based on the e-commerce data identifiers in the e-commerce data identifier set, the data nodes that store the e-commerce data corresponding to each e-commerce data identifier are accessed to obtain the e-commerce data corresponding to each e-commerce data identifier from each data node, and then generate e-commerce data search results with each e-commerce data.

[0168] In this embodiment, by scheduling the search scenario index, the e-commerce data search requests that cannot be processed due to cluster overload are handled using an unloaded e-commerce data cluster, thus ensuring the stability of e-commerce data search services in other search scenarios on the e-commerce independent website platform.

[0169] Please see Figure 8 In a further embodiment, after enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request, the following steps are included:

[0170] Step S16': Respond to the merchant user re-evaluation event, obtain the merchant types for multiple merchant users who have re-evaluated, as well as the new merchant index routing rules and the new search scenario index routing rules:

[0171] The traffic popularity of independent websites of different merchants on independent e-commerce platforms often changes. Therefore, an originally unpopular independent website may become a high-traffic, high-quality independent website, while a high-traffic, high-quality independent website may become an unpopular independent website. In addition, there will be a constant stream of new merchants registering on independent e-commerce platforms to open independent websites for e-commerce sales.

[0172] Therefore, independent e-commerce platforms need to periodically reassess the merchant types that affect traffic popularity for each merchant user. This is to allocate high-performance e-commerce data clusters for e-commerce data storage and retrieval to independent websites that become high-quality merchants, low-performance e-commerce data clusters for e-commerce data storage and retrieval to independent websites that become low-volume merchants, or allocate corresponding performance e-commerce data clusters for e-commerce data storage and retrieval to independent websites of newly registered merchants.

[0173] Independent e-commerce platforms periodically re-evaluate the business information of each merchant's independent website, including website traffic, product sales, order volume, search requests, and e-commerce data volume. This re-evaluates the merchant type of each user and also compiles business information for new merchants to determine their new merchant type. Correspondingly, new merchant index routing rules and new search scenario index routing rules are set to ensure that the merchant's e-commerce data can be stored in the e-commerce data cluster corresponding to their newly evaluated merchant type and routed to the corresponding merchant index and search scenario index.

[0174] Step S17': Reconstruct new merchant indexes corresponding to multiple merchant types, generate a new merchant index pool composed of each new merchant index, and reconstruct a corresponding new search scenario index pool for each new merchant index:

[0175] In response to the merchant user re-evaluation event, a new merchant index corresponding to each merchant type will be rebuilt to initialize the construction of a new merchant index pool for each merchant type, and to initialize the new search scenario index corresponding to each new merchant index in the new merchant index pool, thereby constructing a new search scenario index pool corresponding to each merchant index.

[0176] Step S18': Based on the new merchant type of each merchant user, allocate the corresponding new merchant index from the new merchant index pool to each merchant user, store the e-commerce data of each merchant user in the e-commerce data cluster corresponding to its new merchant index, update each new merchant index accordingly, and update the new search scenario index in the new search scenario index pool corresponding to each new merchant index accordingly.

[0177] After completing the new merchant index pool and its corresponding new search scenario index pool, the new merchant index in the new merchant index pool will be allocated to each merchant user according to the re-evaluated merchant type. Then, the e-commerce data in each merchant user's independent website will be stored in the e-commerce data cluster corresponding to its new merchant index, and the new merchant index in the new merchant index pool and the new search scenario index in the new search scenario index pool corresponding to each new merchant index will be updated accordingly.

[0178] Step S19': Replace the currently online merchant index pool and search scenario index pool with the new merchant index pool and the new search scenario index pool, respectively; and replace the currently online merchant index routing rules and search scenario index routing rules with the new merchant index routing rules and the new search scenario index routing rules, respectively.

[0179] After storing the e-commerce data of each merchant user in the e-commerce data cluster corresponding to their new merchant index, and updating the new merchant index in the new merchant index pool and the new search scenario index in the new search scenario index pool, the merchant index pool and search scenario index pool currently online in the e-commerce data search service are replaced with the new merchant index pool and the new search scenario index pool, respectively. Furthermore, the merchant index routing rules and search scenario index routing rules online in the e-commerce data search service are replaced with the new merchant index routing rules and the new search scenario index routing rules, respectively.

[0180] In this embodiment, by reassessing the merchant types of each merchant user in the independent e-commerce platform, the new merchant index pool and the new search scenario index pool are updated for each merchant user. This ensures that the e-commerce data of newly popular merchants can be stored in a high-performance e-commerce data cluster for search services, while the e-commerce data of merchants whose popularity has decreased is transferred from the high-performance e-commerce data cluster to a lower-performance e-commerce data cluster for search services, thus ensuring the allocation of search computing resources for e-commerce data search services.

[0181] Please see Figure 9 This application provides an index routing device to fulfill one of its objectives. This device is a functional embodiment of the index routing method of this application. On another level, this index routing device, also fulfilling one of its objectives, includes: a search request response module 11, used to respond to e-commerce data search requests and obtain the merchant identifier and search scenario identifier corresponding to the e-commerce data search request; a merchant index routing module 12, used to determine the target merchant index corresponding to the merchant identifier from a merchant index pool according to merchant index routing rules, wherein the merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes; and a data cluster determination module 13, used to determine one or more target e-commerce data clusters corresponding to the target merchant index, wherein the e-commerce data clusters include... It includes high-performance e-commerce data clusters, medium-performance e-commerce data clusters, and low-performance e-commerce data clusters; a scenario index routing module 14, used to determine the target search scenario index corresponding to the search scenario identifier from the search scenario index pool corresponding to the target merchant index according to the search scenario index routing rules, wherein the search scenario index pool contains merchant search scenario indexes, buyer search scenario indexes, and other e-commerce search scenario indexes; and a search result response module 15, used to determine the search e-commerce data cluster corresponding to the target search scenario index from each of the target e-commerce data clusters, enable the target search scenario index in the search e-commerce data cluster to process the e-commerce data search request, and obtain the corresponding e-commerce data search results to respond to the e-commerce data search request.

[0182] In one embodiment, the merchant index routing module 12 includes: a merchant identifier hash calculation submodule, used to obtain the merchant identifier corresponding to the e-commerce data search request, and use a hash algorithm to calculate the hash feature code corresponding to the merchant identifier; an index sequence number calculation submodule, used to determine the number of merchant indexes in the merchant index pool, perform a modulo operation between the hash feature code and the number of merchant indexes, and use the calculation result as the index sequence number; and a target merchant index determination submodule, used to determine the merchant index corresponding to the index sequence number in the merchant index pool, and use the merchant index as the target merchant index corresponding to the merchant identifier.

[0183] In one embodiment, the search result response module 15 includes: a data node determination submodule, used to determine all shards corresponding to the target search scenario index in the e-commerce data cluster; an e-commerce data identifier determination submodule, used to broadcast the e-commerce data search request to the data nodes of each shard, driving each data node to search for the e-commerce data identifier and its score value corresponding to the search request; an identifier queue generation submodule, used to perform data operations on each e-commerce data identifier according to the score value of each e-commerce data identifier, and generate a corresponding e-commerce data identifier set; and a search result generation submodule, used to read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set from each data node, and generate an e-commerce data search result containing each e-commerce data.

[0184] In another embodiment, the search result response module 15 further includes: a request full-load response submodule, used to respond to the search request full-load event of the e-commerce data cluster and determine the unfulfilled search scenario index in the search scenario index pool corresponding to the target merchant index; an e-commerce data identifier determination submodule, used to determine the data nodes corresponding to all shards of the unfulfilled search scenario index in the search e-commerce data cluster, broadcast the e-commerce data search request to the data nodes of each shard, and drive each data node to search for the e-commerce data identifier and its score value corresponding to the search e-commerce data search request; an identifier queue generation submodule, used to perform data operations on each e-commerce data identifier according to the score value of each e-commerce data identifier to generate a corresponding e-commerce data identifier set; and a search result generation submodule, used to read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set from each data node and generate an e-commerce data search result containing each e-commerce data.

[0185] To address the aforementioned technical problems, embodiments of this application also provide computer equipment. For example... Figure 10 The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, the processor can implement an index routing method. The processor of the computer device provides computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the index routing method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 10The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0186] In this embodiment, the processor is used to execute... Figure 9 The system defines the specific functions of each module and its submodules. The memory stores the program code and various data required to execute these modules or submodules. The network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / submodules in the index routing device of this application. The server can call the server's program code and data to execute the functions of all submodules.

[0187] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the index routing method of any embodiment of this application.

[0188] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0189] In summary, this application can use different performance clusters to process data search based on the characteristics of merchants and search scenarios in e-commerce, thereby improving the processing efficiency of data search services.

[0190] Those skilled in the art will understand that the steps, measures, and solutions in the various operations, methods, and processes discussed in this application can be alternated, modified, combined, or deleted. Furthermore, other steps, measures, and solutions in the various operations, methods, and processes discussed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and solutions in the prior art that are similar to those in the open-source operations, methods, and processes of this application can also be alternated, modified, rearranged, decomposed, combined, or deleted.

[0191] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An index routing method, characterized in that, Includes the following steps: In response to an e-commerce data search request, obtain the merchant identifier and search scenario identifier corresponding to the e-commerce data search request; According to the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool. The merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes. Identify one or more target e-commerce data clusters corresponding to the target merchant index, wherein the e-commerce data clusters include high-performance e-commerce data clusters, medium-performance e-commerce data clusters, and low-performance e-commerce data clusters; According to the search scenario index routing rules, the target search scenario index corresponding to the search scenario identifier is determined from the search scenario index pool corresponding to the target merchant index. The search scenario index pool contains merchant search scenario indexes, buyer search scenario indexes and other e-commerce search scenario indexes. From each of the target e-commerce data clusters, determine the search e-commerce data cluster corresponding to the target search scenario index, enable the target search scenario index in the search e-commerce data cluster to process the e-commerce data search request, and obtain the corresponding e-commerce data search results to respond to the e-commerce data search request.

2. The method of claim 1, wherein, Before responding to an e-commerce data search request and obtaining the merchant identifier and search scenario identifier corresponding to the e-commerce data search request, the following steps are included: A merchant index corresponding to multiple merchant types is constructed, and multiple search scenario indexes corresponding to search scenario types are constructed for each merchant index, thereby generating a merchant index pool composed of each of the merchant indexes, and a search scenario index pool composed of each of the search scenario indexes is constructed for each of the merchant indexes. The merchant types include high-quality merchants, ordinary merchants and unpopular merchants, and the search scenario types include merchant search scenarios, buyer merchant search scenarios and other e-commerce search scenarios. The system obtains store traffic information from multiple merchants on an independent e-commerce platform. This store traffic information includes store product sales volume, store order volume, store visits, or store exposure. The system then calls a merchant user traffic analysis algorithm to determine the merchant type of each merchant user based on their store traffic information. Based on the merchant type of each merchant user, a corresponding merchant index from the merchant index pool is allocated to each merchant user. The e-commerce data of each merchant user is stored in the e-commerce data cluster corresponding to its merchant index, and each merchant index is updated accordingly. Determine the search scenario index pool corresponding to the merchant index of each merchant user, and then determine the search scenario index in each search scenario index pool.

3. The method of claim 1, wherein, After enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request, the process includes the following steps: In response to an e-commerce data update request, obtain the merchant identifier and updated e-commerce data corresponding to the e-commerce data update request; Based on the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool, and one or more e-commerce data clusters corresponding to the target merchant index are determined. The updated e-commerce data is then distributed to each of the e-commerce data clusters, and the target merchant index is updated accordingly. The search scenario index pool corresponding to the target merchant index is determined, and each search scenario index in the search scenario index pool is updated accordingly.

4. The method of claim 1, wherein, Based on the merchant index routing rules, the target merchant index corresponding to the merchant identifier is determined from the merchant index pool, which contains high-quality merchant indexes, ordinary merchant indexes, and infrequently used merchant indexes. The process includes the following steps: Obtain the merchant identifier corresponding to the e-commerce data search request, and use a hash algorithm to calculate the hash feature code corresponding to the merchant identifier; Determine the number of merchant indexes in the merchant index pool, perform a modulo operation between the hash feature code and the number of merchant indexes, and use the result as the index sequence number; The merchant index corresponding to the index number in the merchant index pool is determined, and this merchant index is used as the target merchant index corresponding to the merchant identifier.

5. The method of claim 1, wherein, The process of enabling the target search scenario index to handle the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request includes the following steps: Identify all shards in the e-commerce data cluster corresponding to the target search scenario index; The e-commerce data search request is broadcast to the data nodes of each of the shards, driving each of the data nodes to search for the e-commerce data identifier and its score value corresponding to the search request. Based on the scoring values ​​of each e-commerce data identifier, data operations are performed on each e-commerce data identifier to generate a corresponding e-commerce data identifier set; From each of the data nodes, read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set, and generate an e-commerce data search result containing each of the e-commerce data.

6. The method of claim 1, wherein, Enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster includes the following steps: In response to the search request full load event of the e-commerce data cluster, determine the unloaded search scenario index in the search scenario index pool corresponding to the target merchant index; The data nodes corresponding to all shards of the unloaded search scenario index in the search e-commerce data cluster are determined. The e-commerce data search request is broadcast to the data nodes of each shard, and each data node is driven to search out the e-commerce data identifier and its score value corresponding to the search e-commerce data search request. Based on the scoring values ​​of each e-commerce data identifier, data operations are performed on each e-commerce data identifier to generate a corresponding e-commerce data identifier set; From each of the data nodes, read the e-commerce data corresponding to all e-commerce data identifiers in the e-commerce data identifier set, and generate an e-commerce data search result containing each of the e-commerce data.

7. The method of claim 1, wherein, After enabling the target search scenario index to process the e-commerce data search request in the search e-commerce data cluster and obtaining the corresponding e-commerce data search results to respond to the e-commerce data search request, the process includes the following steps: In response to merchant user re-evaluation events, obtain the merchant types that have been re-evaluated by multiple merchant users, as well as the new merchant index routing rules and the new search scenario index routing rules; Reconstruct new merchant indexes corresponding to multiple merchant types, generate a new merchant index pool composed of each new merchant index, and reconstruct a corresponding new search scenario index pool for each new merchant index; Based on the new merchant type of each merchant user, the corresponding new merchant index in the new merchant index pool is allocated to each merchant user. The e-commerce data of each merchant user is stored in the e-commerce data cluster corresponding to its new merchant index, and each new merchant index is updated accordingly. The new search scenario index in the new search scenario index pool corresponding to each new merchant index is also updated accordingly. Replace the currently online merchant index pool and search scenario index pool with the new merchant index pool and the new search scenario index pool, respectively. Also replace the currently online merchant index routing rules and search scenario index routing rules with the new merchant index routing rules and the new search scenario index routing rules, respectively.

8. An indexing routing device, characterized by include: The search request response module is used to respond to e-commerce data search requests and obtain the merchant identifier and search scenario identifier corresponding to the e-commerce data search request. The merchant index routing module is used to determine the target merchant index corresponding to the merchant identifier from the merchant index pool according to the merchant index routing rules. The merchant index pool contains high-quality merchant indexes, ordinary merchant indexes, and unpopular merchant indexes. The data cluster determination module is used to determine one or more target e-commerce data clusters corresponding to the target merchant index, wherein the e-commerce data clusters include high-performance e-commerce data clusters, medium-performance e-commerce data clusters and low-performance e-commerce data clusters. The scenario index routing module is used to determine the target search scenario index corresponding to the search scenario identifier from the search scenario index pool corresponding to the target merchant index according to the search scenario index routing rules. The search scenario index pool contains merchant search scenario indexes, buyer search scenario indexes and other e-commerce search scenario indexes. The search result response module is used to determine the search e-commerce data cluster corresponding to the target search scenario index from each of the target e-commerce data clusters, enable the target search scenario index in the search e-commerce data cluster to process the e-commerce data search request, and obtain the corresponding e-commerce data search results to respond to the e-commerce data search request.

9. A computer device comprising a central processing unit and a memory, characterized in that, The central processing unit is used to invoke and run a computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores, in the form of computer-readable instructions, a computer program implemented according to any one of claims 1 to 7, which, when invoked by a computer, executes the steps included in the corresponding method.

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