Business request data query method and device, equipment, medium and product

Abstract

The invention discloses a business request data query method and device, equipment, a medium and a product. The method comprises the following steps: performing request analysis on at least one obtained target service request to obtain a service request condition in the target service request; if the service request condition exists in the pre-constructed data partition mapping table, acquiring a cold and hot data identifier corresponding to the service request condition from the data partition mapping table; and according to the cold and hot data identifier, determining a target data partition and querying the target service request data from the target data partition. According to the scheme of the embodiment, the mapping relation between the service request condition and the cold and hot data identifiers is obtained according to the pre-constructed data partition mapping table; and the target data partition is determined according to the cold and hot data identifier, and the target service request data is queried, so that the data access performance in the distributed database is improved, and the storage efficiency and the storage resource utilization rate of the distributed database are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, medium, and product for querying business request data. Background Technology

[0002] With the explosive growth of data volume, distributed databases are playing an increasingly important role in data storage and management. However, data access patterns often exhibit distinct hot and cold characteristics, meaning some data is accessed frequently while others are accessed less frequently. Storing cold and hot data in the same database storage partition will severely impact the access and storage performance of the distributed database.

[0003] In existing technologies, the management of hot and cold data in distributed databases typically relies on manual rules or simple access frequency statistics. However, this approach lacks automatic sensing capabilities, cannot dynamically identify the data access status in the database in real time, and struggles to adapt to rapid changes in data access patterns. This results in extremely low efficiency in reading data from the database and low storage efficiency and utilization of database resources.

[0004] Therefore, how to improve data access performance in distributed databases and enhance storage efficiency and resource utilization based on the hot and cold characteristics of data in distributed databases has become a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, device, medium, and product for querying business request data, which enables partitioned querying and storage of business request data in a distributed database based on the hot and cold characteristics of the data, thereby improving the access performance of the distributed database and enhancing its storage efficiency and resource utilization.

[0006] According to one aspect of the present invention, a method for querying business request data is provided, comprising:

[0007] The at least one target business request is parsed to obtain the business request conditions in the target business request;

[0008] If the business request condition exists in the pre-built data partitioning mapping table, then the cold and hot data identifiers corresponding to the business request condition are obtained from the data partitioning mapping table.

[0009] Based on the hot and cold data identifiers, the target data partition is determined and the target service request data is queried from the target data partition.

[0010] According to another aspect of the present invention, a device for querying business request data is provided, comprising:

[0011] The business request parsing module is used to parse at least one target business request obtained to obtain the business request conditions in the target business request.

[0012] The hot and cold data identifier determination module is used to obtain the hot and cold data identifier corresponding to the business request condition from the data partition mapping table if the business request condition exists in the pre-built data partition mapping table.

[0013] The business request data acquisition module is used to determine the target data partition based on the hot and cold data identifier and query the target business request data from the target data partition.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the service request data query method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the query method for business request data as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the method for querying business request data as described in any embodiment of the present invention.

[0020] The technical solution of this invention involves parsing at least one target business request to obtain the business request conditions within the target business request. If the business request conditions exist in a pre-built data partition mapping table, the corresponding hot and cold data identifiers are retrieved from the data partition mapping table. Based on the hot and cold data identifiers, the target data partition is determined, and the target business request data is queried from the target data partition. This embodiment improves data access performance in the distributed database and enhances storage efficiency and storage resource utilization.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a method for querying business request data according to Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a method for querying business request data according to Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a business request data query device provided in Embodiment 3 of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the query method for business request data according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 The flowchart of a business request data query method provided in Embodiment 1 of the present invention is applicable to situations where the data access performance of the database is poor and the storage resource utilization is low due to the cold and hot characteristics of the data stored in the distributed database. The method can be executed by a business request data query device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0031] like Figure 1 As shown, the method includes:

[0032] S110. Parse at least one target business request to obtain the business request conditions in the target business request.

[0033] The target business request can be a transaction request that performs data processing operations on data stored in a distributed database. The specific data processing operations can be read, write, delete, or modify operations on the data stored in the database.

[0034] Specifically, the business request conditions refer to the requirements that must be met when executing an initiated business request. These can include time requirements, resource requirements, dependencies, and user requirements. In detail, when executing a target business request, the request information of the target business request can be obtained and parsed to determine the requirements that must be met to execute the target business request.

[0035] S120. If there are business request conditions in the pre-built data partition mapping table, then obtain the cold and hot data identifiers corresponding to the business request conditions from the data partition mapping table.

[0036] The data partition mapping table can be a data storage table used to record business request conditions and the hot / cold data identifiers associated with those conditions. The hot / cold data identifiers can be unique identifiers used to identify and record the data partition to which data stored in the database belongs. Specifically, they can include hot data identifiers, cold data identifiers, and candidate data identifiers. Data partitions can include hot data partitions, cold data partitions, and candidate data partitions. A specific data partition can be obtained based on the hot / cold data identifiers corresponding to the data stored in that partition. These hot / cold data identifiers can be pre-set by technical personnel based on the hot / cold characteristics of the data stored in the distributed database. It should be noted that candidate data identifiers can be used to mark business request data in the database whose hot / cold characteristics are unclear; specifically, they can be newly added business request data in the database.

[0037] Specifically, by identifying the hot and cold characteristics of data stored in the database, we can obtain hot and cold data identification information. Then, we can associate this hot and cold data identification information with the corresponding business request conditions to obtain a data partitioning mapping table. For example, if the database stores hot data A, cold data B, and candidate data C, where the business request condition corresponding to hot data A can be condition a, the business request condition corresponding to cold data B can be condition b, and the business request condition corresponding to candidate data C can be condition c, then we can associate condition a with the hot data identifier, condition b with the cold data identifier, and condition c with the candidate data identifier to construct the data partitioning mapping.

[0038] Furthermore, when the business request conditions of the target business request are obtained, these conditions can be matched against the data partition mapping table. If the business request condition exists in the data partition mapping table, the corresponding hot / cold data identifier can be obtained from the table. For example, continuing the previous example, if the business request condition in the target business request is condition b, it can be determined that the business request condition exists in the data partition mapping table, and that the corresponding hot / cold data identifier is a cold data identifier. This embodiment does not impose specific limitations on this.

[0039] S130. Based on the hot and cold data identifiers, determine the target data partition and query the target business request data from the target data partition.

[0040] The target data partition can be the data storage area for the business request data required to execute the target business request. Specifically, after obtaining the hot / cold data identifier corresponding to the business request, the data storage area to which the business request data required to execute the target business request belongs can be directly determined based on the hot / cold data identifier, and the corresponding business request data can be retrieved from the data partition. For example, continuing the previous example, if business condition b is determined to be a cold data identifier, then the target data partition is determined to be a cold data distribution, and business request data B can be retrieved from the cold data partition.

[0041] Optionally, the target data partition is determined based on the hot and cold data identifiers, including: obtaining the hot and cold data partition category value in the hot and cold data identifiers; determining whether the hot and cold data partition category value is equal to a preset hot and cold data partition category threshold; if so, the hot data partition and the candidate data partition are determined as the target partitions.

[0042] The hot and cold data partition category value can be a field value recorded in the hot and cold data identifier, which indicates the data storage partition corresponding to the data marked by the hot and cold data identifier. For example, the hot and cold data partition category value in the hot data identifier can be 1, the hot and cold data partition category value in the cold data identifier can be 2, and the hot and cold data partition category value in the candidate data identifier can be 3. This embodiment does not impose specific restrictions on this.

[0043] Specifically, the cold / hot data partition category value in the obtained cold / hot data identifier can be compared with a set cold / hot data partition category value threshold to determine if the data partition category value and the data partition category threshold are equal. If they are equal, the cold / hot data identifier can be determined to correspond to a hot data partition and a candidate data partition, and the hot data partition and candidate data partition can be identified as the target data partition for querying the target business request data. For example, the cold / hot data partition category threshold can be set to 1. If the data partition category value in the data identifier is 1, the hot data partition and candidate data partition can be identified as the target data partition, and the target business request data can be obtained by querying the stored hot data partition and candidate data partition. If the data partition category value in the data identifier is not 1, the hot data partition, cold data partition, and candidate data partition are scanned to obtain the target business request data.

[0044] The technical solution of this invention involves parsing at least one target business request to obtain the business request conditions within the target business request. If the business request conditions exist in a pre-built data partition mapping table, the corresponding hot and cold data identifiers are retrieved from the data partition mapping table. Based on the hot and cold data identifiers, the target data partition is determined, and the target business request data is queried from the target data partition. This embodiment improves data access performance in the distributed database and enhances storage efficiency and storage resource utilization.

[0045] Example 2

[0046] Figure 2 This is a flowchart of a method for querying business request data according to Embodiment 2 of the present invention. This embodiment further optimizes the above-mentioned method for querying business request data based on the previous embodiment.

[0047] Furthermore, after the step "determine the target data partition based on the hot and cold data identifiers and query the target business request data from the target data partitions," add the step "obtain the historical business access counts of the corresponding historical business request data in the data partitions under the historical time period; determine the hot and cold weight values ​​corresponding to each historical business request data based on the historical access counts; and partition each historical business request data based on the hot and cold data weight values." This improves the method for querying business request data. Figure 2 As shown, the method includes:

[0048] S210. Parse at least one target business request to obtain the business request conditions in the target business request.

[0049] S220. If there are business request conditions in the pre-built data partition mapping table, then obtain the cold and hot data identifiers corresponding to the business request conditions from the data partition mapping table.

[0050] S230. Based on the hot and cold data identifiers, determine the target data partition and query the target business request data from the target data partition.

[0051] S240. Obtain the historical business access count of the corresponding historical business request data in the data partition under the historical time period.

[0052] Historical business access counts refer to the number of times data stored in the database has been accessed through different business requests. This can be obtained by querying the database or analyzing business logs. Specifically, it can be obtained by tracking the number of requests to access data in the database over a historical time period, such as the number of requests within the past week or month. For example, if the database contains historical business request data D, historical business request data E, and historical business request data F, a query can reveal that historical business request data D was accessed 5 times, historical business request data E was accessed 8 times, and historical business request data F was accessed 10 times.

[0053] S250. Based on the number of historical accesses, determine the historical hot and cold weight values ​​corresponding to each historical business request data.

[0054] The historical hot / cold weight value can be specifically derived from the access frequency of business request data in the database, resulting in an access score for each business request data. Specifically, it can be calculated based on the number of accesses to business request data in the database and the daily score decay value. For example, continuing the previous example, a base access score of 1 point can be set, meaning that each time a business request queries the data, the access score for that data is increased by 1. Alternatively, a daily score decay value of 10 points can be set, meaning that the access score for each business request data is decreased by 10 points daily. Therefore, the access score for each business request data would be calculated as follows: if historical business request data 1 was accessed 5 times in the past week, then the access score for historical business request data 1 would be -45 points.

[0055] S260. Based on the weight values ​​of historical hot and cold data, partition the data of each historical business request.

[0056] Specifically, corresponding data storage partitions can be set in the database based on different access scores. Furthermore, based on the data access scores corresponding to each historical business data, the historical business data can be stored in the corresponding data storage partition. For example, business request data with an access score of not less than 50 can be stored in the hot data partition, and business request data with an access score of less than -50 can be stored in the cold data partition. This embodiment does not impose specific restrictions on this.

[0057] It should be noted that when partitioning data for each business request based on a data access score, this score is the real-time access score for each business request. This access score can increase or decrease over time, meaning that a hot data partition can store business request data with an access score below 50, and a cold data partition can store business requests with an access score greater than -50. Furthermore, within a fixed time period, such as during the off-peak business hours from 1 AM to 2 AM daily, the real-time access scores of historical business request data stored in the database can be retrieved, and based on these scores, the historical business access data can be migrated to the corresponding data partitions.

[0058] Optionally, the data partitioning includes hot data partitioning and cold data partitioning; based on the historical hot and cold data weight values, the historical business request data is partitioned, including: determining the table memory usage of the data partitioning mapping table; if the table memory usage is greater than a preset threshold, then the hot and cold data weight values ​​corresponding to each historical business request data in the hot data partition are compared with a preset first hot and cold data weight threshold to obtain a first weight value comparison result; based on the first weight value comparison result, a first historical business request data is selected from each historical business request data in the hot data partition, and the first historical business request data is migrated to the cold data partition.

[0059] Specifically, the table memory utilization rate can be the proportion of memory space occupied by the data partition mapping table. For example, if a pre-built data partition mapping table can store 100 data records, then when the number of data records for business query conditions and hot / cold data identifiers stored in the data partition mapping table is 95, the table memory utilization rate of the data partition mapping table can be determined to be 95%.

[0060] The first hot / cold data weight threshold can be used to determine whether the access score of each historical service request data stored in the hot data partition still meets the access score judgment value for storing the service request data in the hot data partition. The first historical service request data can be historical service request data whose access score does not meet the first hot / cold data weight threshold. Specifically, when the table memory occupancy rate of the data partition mapping table exceeds a set threshold, such as exceeding 90%, the access score of each historical service request data in the hot data partition is compared with the preset first access score threshold to obtain the first weight value comparison result. Furthermore, based on the first weight value comparison result, historical service request data in the hot data partition with access scores less than the first hot / cold data weight threshold can be migrated to the cold data partition. For example, if the first hot / cold data weight threshold is set to -50 points, historical service request data in the hot data partition with access scores lower than -50 points can be migrated to the cold data partition. This embodiment does not impose specific restrictions on this.

[0061] Optionally, the data partitioning also includes candidate data partitioning; partitioning each historical business request data according to the cold and hot data weight values ​​also includes: comparing the cold and hot data weight values ​​corresponding to each historical business request data in the cold data partition and the candidate data partition with a pre-set second cold and hot data weight threshold to obtain a second weight value comparison result; selecting second historical business request data from each historical business request data in the cold data partition and the candidate data partition according to the second weight value comparison result, and migrating the second historical business request data to the hot data partition.

[0062] The second cold / hot data weight threshold can be used to determine whether the access scores of each historical service request data stored in the cold data partition and the candidate data partition meet the access score judgment value for migrating the service request data to the hot data partition. The second historical service request data can be historical service request data whose access scores meet the second cold / hot data weight threshold. Further, the access scores of the cold data and the historical service request data in the candidate data partition can be compared with a pre-set second access score threshold to obtain a second weight value comparison result. Then, based on the second weight value comparison result, historical service request data in the cold data partition and the candidate data partition whose access scores are greater than the second cold / hot data weight threshold can be migrated to the hot data partition. For example, if the second cold / hot data weight threshold is set to 50 points, then historical service request data in the cold data partition and the candidate data partition whose access scores are higher than 50 points can be migrated to the hot data partition. This embodiment does not impose specific limitations on this.

[0063] Optionally, after selecting second historical business request data from the historical business request data in the cold data partition and candidate data partition according to the comparison result of the second weight value, and migrating the second historical business request data to the hot data partition, the method further includes: obtaining the business request conditions corresponding to each historical business request data in the candidate data partition; comparing the business request conditions corresponding to each historical business request data in the candidate data partition with the business request conditions stored in the data partition mapping table to obtain the business request condition comparison result; and selecting third historical business request data from the historical business request data in the candidate data partition according to the business request condition comparison result, and migrating the third historical business request data to the cold data partition.

[0064] The third type of historical business request data can be historical business request data for which transaction request conditions are not stored in the data partition mapping table. Specifically, after migrating some historical business request data from the candidate data partition to the hot data partition based on the second cold and hot data weight threshold, the business request conditions corresponding to each historical business request data in the candidate data partition are obtained. These business request conditions are then compared with the transaction request conditions stored in the data partition mapping table to obtain the request condition comparison result. Based on this comparison result, the transaction request conditions not stored in the data partition mapping table are determined. Subsequently, the historical transaction request data corresponding to the transaction request conditions not stored in the data partition mapping table are migrated to the cold data partition.

[0065] The technical solution of this invention involves obtaining the historical access counts of historical business request data corresponding to historical business request data in a data partition under a historical time period; determining the hot / cold weight value corresponding to each historical business request data based on the historical access counts; and partitioning each historical business request data according to the hot / cold data weight values. This embodiment can calculate the hot / cold weight value of each historical business request data in real time based on the obtained historical access counts of historical business request data in each data partition; and migrate each historical business request data to the corresponding data partition according to the hot / cold weight value. By monitoring the changes in the hot / cold characteristics of stored data in the database in real time, intelligent partitioning of business request data is achieved, improving the data access performance of the distributed database and further enhancing the storage efficiency and storage resource utilization of the distributed database.

[0066] Example 3

[0067] Figure 3This is a schematic diagram of a business request data query device provided in Embodiment 3 of the present invention. The business request data query device provided in this embodiment of the present invention is applicable to situations where the data access performance of a distributed database is poor and the utilization rate of storage resources is low due to the existence of hot and cold characteristics in the data stored in the database. This business request data query device can be implemented in hardware and / or software, such as... Figure 3 As shown, it specifically includes: a business request parsing module 310, a hot / cold data identification and determination module 320, and a business request data acquisition module 330. Among them,

[0068] The business request parsing module 310 is used to parse at least one target business request to obtain the business request conditions in the target business request.

[0069] The hot and cold data identification determination module 320 is used to obtain the hot and cold data identification corresponding to the business request condition from the data partition mapping table if the business request condition exists in the pre-built data partition mapping table.

[0070] The business request data acquisition module 330 is used to determine the target data partition based on the hot and cold data identifier and query the target business request data from the target data partition.

[0071] This embodiment can obtain the mapping relationship between business request conditions and hot / cold data identifiers based on a pre-built data partition mapping table; then, based on the business request conditions in the target business request, the corresponding hot / cold data identifiers are obtained, the target data partition is determined through the hot / cold data identifiers, and the target business request data is retrieved from the target data partition. This improves the data access performance in the distributed database and enhances the storage efficiency and storage resource utilization of the distributed database.

[0072] Optionally, the business request data acquisition module 330 includes:

[0073] The target data partitioning determination unit is used to obtain the cold and hot data partitioning category value in the cold and hot data identifier;

[0074] Determine whether the cold and hot data partition category value is equal to the preset cold and hot data partition category threshold;

[0075] If so, then determine the hot data score and candidate data partition as the target data partition.

[0076] Optionally, the device may also include a data partitioning module;

[0077] Optionally, the data partitioning module includes:

[0078] The business access count determination unit is used to obtain the historical business access count of the corresponding historical business request data in the data partition under the historical time period after determining the target data partition according to the hot and cold data identifier and querying the target business request data from the target data partition;

[0079] The hot and cold weight value calculation unit is used to determine the hot and cold weight value corresponding to each of the historical service request data based on the historical access count.

[0080] The data partitioning unit is used to partition each of the historical service request data according to the hot and cold data weight values.

[0081] Optionally, the data partition includes hot data partition and cold data partition; the data partitioning unit is specifically used to determine the table memory usage of the data partition mapping table;

[0082] If the table memory usage rate is greater than a preset threshold, the cold and hot data weight values ​​corresponding to each historical business request data in the hot data partition are compared with the preset first cold and hot data weight threshold to obtain the first weight value comparison result.

[0083] Based on the first weight value comparison result, the first historical service request data is selected from each of the historical service request data in the hot data partition, and the first historical service request data is migrated to the cold data partition.

[0084] Optionally, the data partition further includes a candidate data partition; the data partitioning unit is specifically used to compare the cold and hot data weight values ​​corresponding to each of the historical service request data in the cold data partition and the candidate data partition with a pre-set second cold and hot data weight threshold to obtain a second weight value comparison result.

[0085] Based on the comparison result of the second weight value, the second historical service request data is selected from the historical service request data in the cold data partition and the candidate data partition, and the second historical service request data is migrated to the hot data partition.

[0086] Optionally, the data partitioning unit is further configured to select second historical service request data from each of the historical service request data in the cold data partition and the candidate data partition according to the comparison result of the second weight value, and migrate the second historical service request data to the hot data partition, and then obtain the service request conditions corresponding to each historical service request data in the candidate data partition.

[0087] The business request conditions corresponding to each historical business request data in the candidate data partition are compared with the business request conditions stored in the data partition mapping table to obtain the business request condition comparison results.

[0088] Based on the comparison results of the business request conditions, a third historical business request data is selected from each of the historical business request data in the candidate data partition, and the third historical business request data is migrated to the cold data partition.

[0089] The business request data query device provided in the embodiments of the present invention can execute the business request data query method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] Example 4

[0091] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0092] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0093] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0094] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the query method for business request data.

[0095] In some embodiments, the method for querying service request data may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the service request data query method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the service request data query method by any other suitable means (e.g., by means of firmware).

[0096] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0101] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for querying business request data, characterized in that, include: The at least one target business request is parsed to obtain the business request conditions in the target business request; If the business request condition exists in the pre-built data partitioning mapping table, then the cold and hot data identifiers corresponding to the business request condition are obtained from the data partitioning mapping table. Based on the hot and cold data identifiers, the target data partition is determined and the target service request data is queried from the target data partition.

2. The method according to claim 1, characterized in that, The step of determining the target data partition based on the hot and cold data identifier includes: Obtain the cold / hot data partition category value from the cold / hot data identifier; Determine whether the cold and hot data partition category value is equal to the preset cold and hot data partition category threshold; If so, then determine the hot data score and candidate data partition as the target data partition.

3. The method according to claim 1, characterized in that, After determining the target data partition based on the hot and cold data identifiers and querying the target service request data from the target data partitions, the method further includes: Retrieve the historical business access counts for the corresponding historical business request data within the data partition under the historical time period; Based on the historical access count, determine the hot / cold weight value corresponding to each of the historical service request data; Based on the weight values ​​of the hot and cold data, the historical service request data is partitioned.

4. The method according to claim 3, characterized in that, The data partitioning includes hot data partitioning and cold data partitioning; the step of partitioning the historical service request data according to the hot and cold data weight values ​​includes: Determine the table memory usage of the data partition mapping table; If the table memory usage rate is greater than a preset threshold, the cold and hot data weight values ​​corresponding to each historical business request data in the hot data partition are compared with the preset first cold and hot data weight threshold to obtain the first weight value comparison result. Based on the first weight value comparison result, the first historical service request data is selected from each of the historical service request data in the hot data partition, and the first historical service request data is migrated to the cold data partition.

5. The method according to claim 3, characterized in that, The data partitioning also includes candidate data partitioning; the step of partitioning each of the historical service request data according to the hot and cold data weight values ​​further includes: The cold and hot data weight values ​​corresponding to each historical service request data in the cold data partition and the candidate data partition are compared with the preset second cold and hot data weight threshold to obtain the second weight value comparison result. Based on the comparison result of the second weight value, the second historical service request data is selected from the historical service request data in the cold data partition and the candidate data partition, and the second historical service request data is migrated to the hot data partition.

6. The method according to claim 5, characterized in that, After selecting second historical service request data from the historical service request data in the cold data partition and the candidate data partition according to the comparison result of the second weight value, and migrating the second historical service request data to the hot data partition, the method further includes: Obtain the business request conditions corresponding to each historical business request data in the candidate data partition; The business request conditions corresponding to each historical business request data in the candidate data partition are compared with the business request conditions stored in the data partition mapping table to obtain the business request condition comparison results. Based on the comparison results of the business request conditions, a third historical business request data is selected from each of the historical business request data in the candidate data partition, and the third historical business request data is migrated to the cold data partition.

7. A device for querying business request data, characterized in that, include: The business request parsing module is used to parse at least one target business request obtained to obtain the business request conditions in the target business request. The hot and cold data identifier determination module is used to obtain the hot and cold data identifier corresponding to the business request condition from the data partition mapping table if the business request condition exists in the pre-built data partition mapping table. The business request data acquisition module is used to determine the target data partition based on the hot and cold data identifier and query the target business request data from the target data partition.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the query method for business request data according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for querying business request data as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a method for querying business request data according to any one of claims 1-6.

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