CN117076450A - Task request scheduling optimization method, device, computer equipment and medium - Google Patents

Abstract

The invention discloses an optimization method, device, computer equipment and medium for task request scheduling, and relates to the field of information processing technology. The method includes: creating an update table in advance; calculating the corresponding jobid of all task requests. hash value; calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the remainder to obtain the subscript position of the update table where the target task request is located; determine whether the subscript position is empty , if it is empty, assign a value of 1 to the subscript position, if not, return failure. The method of the present invention can effectively reduce the failure of some task requests due to the triggering of redis optimistic lock.

Description

An optimization method, device, computer equipment and medium for task request scheduling

Technical field

The invention relates to the field of information processing technology, and in particular to an optimization method, device, computer equipment and non-volatile computer-readable storage medium for task request scheduling.

Background technique

At present, with the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming into financial technology (Fintech). However, due to the security and real-time requirements of the financial industry, technology has also been put forward. higher requirement.

At present, the bank-structured business system has a large number of data consumption tasks to be performed every day, and the number of nodes in the cluster where the business system consumes data is limited. The business system has always been committed to using fewer nodes to pass reasonable Orchestrate and run as many tasks as possible, for example, by dividing the nodes in the cluster into several queues, and then dividing the tasks into several tasks by labeling. Different queues process tasks with different labels, but , when there are more and more tasks to be executed, there are also more and more queues in the business system. When the business peaks, the nodes in the queue are often fully occupied, resulting in the delay in executing high-priority tasks.

Currently, when counting the number of task requests for a certain shard, the optimistic locking mechanism of redis is currently used, by creating a redis key for the current shard and then using the watch command (after using the watch command, it will obtain the key value, and then monitor it. When we execute the task, we will update this value. When updating, first it will compare whether the key value to be updated is the same as the value obtained before. If so, , then continue with the following steps, if not, an error will be reported to the client and execution will not continue). Currently, there is generally no problem. However, when task requests are scheduled frequently, if a task request passes this check and the value is updated, other task requests that arrive at the same time as this task request will only report an error. Then retry, which will lead to a significant increase in the number of task requests and frequent error reports. For this reason, the existing technology needs to be optimized for this situation.

In summary, how to provide an optimization method, device, computer equipment and non-volatile computer-readable storage medium for task request scheduling to effectively reduce the failure of some task request requests due to redis optimistic lock triggering is It is currently a problem that needs to be solved urgently by those skilled in the art.

Contents of the invention

In view of the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide an optimization method, device, computer equipment and non-volatile computer-readable storage medium that can be used for task request scheduling in financial technology or other related fields, which can Effectively reduce the failure of some task requests due to redis optimistic lock triggering.

In order to achieve the above objects, the present invention adopts the following technical solutions:

An optimization method for task request scheduling, which includes:

Create an update table in advance;

Calculate the corresponding hash values of the jobids of all task requests;

Use the hash value to calculate the remainder of the length value of the update table, and subtract 1 from the calculated remainder to obtain the subscript position of the update table where the target task request is located;

Determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure.

In a further technical solution, the task request scheduling optimization method is provided, wherein the pre-created update table includes:

Pre-create an update table based on the target shard of the database.

In a further technical solution, in the optimization method of task request scheduling, an update table is created in advance based on the target sharding of the database, and the base value of the target sharding is initially 0.

In a further technical solution, the optimization method of task request scheduling, wherein the step of determining whether the subscript position is empty, and if it is empty, assigns a value of 1 to the subscript position, and if it is not empty, Return after failure, including:

Add up all the assigned items in the update table, and then add the base value to obtain the number of task requests for the target shard.

An optimization device for task request scheduling, which includes:

Create module, used to create an update table in advance;

The calculation module is used to calculate the corresponding hash value of the jobid of all task requests;

The remainder module is used to calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the remainder to obtain the subscript position of the update table where the target task request is located;

A judgment module is used to judge whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure.

In a further technical solution, the task request scheduling optimization device, wherein the pre-created update table includes:

Pre-create an update table based on the target shard of the database.

In a further technical solution, in the task request scheduling optimization device, an update table is created in advance based on the target fragments of the database, and the base value of the target fragments is initially 0.

In a further technical solution, the optimization device for task request scheduling, wherein the step of determining whether the subscript position is empty, and if it is empty, assigns a value of 1 to the subscript position, and if it is not empty, Return after failure, including:

Add up all the assigned items in the update table, and then add the base value to obtain the number of task requests for the target shard.

A computer device, wherein the computer 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 can be executed by the at least one processor. When the computer program is executed by the at least one processor, the optimization method for task request scheduling as described in any one of the above can be implemented.

A non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program. When the computer program is executed by at least one processor, it can implement any of the above. The optimization method of task request scheduling described above.

Compared with the existing technology, the present invention provides an optimization method, device, computer equipment and non-volatile computer-readable storage medium for task request scheduling, wherein the method includes: creating an update table in advance; Calculate the corresponding hash value of the jobid of the task request; calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the remainder to obtain the subscript position of the update table where the target task request is located; Determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure. The method of the present invention can effectively reduce the failure of some task requests due to the triggering of redis optimistic lock.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only These are some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic flowchart of a method for optimizing task request scheduling provided by an embodiment of the present invention.

Figure 2 is a functional module schematic diagram of an optimization device for task request scheduling provided by an embodiment of the present invention.

FIG. 3 is a schematic diagram of the hardware structure of the computer device provided by an embodiment of the present invention.

Figure 4 is a schematic diagram of the update table provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and effect of the present invention clearer and clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

In the description of the present invention, the words "including", "including", "having", "containing", etc. are all open terms, which mean including but not limited to. Reference to the terms "one embodiment," "a specific embodiment," "some embodiments," "such as," etc. in the description means that a particular feature, structure or characteristic described in connection with the embodiment or example is included in at least one of the present application in an embodiment or example. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in each embodiment is used to schematically illustrate the implementation of the present application. The sequence of steps is not limited and can be adjusted appropriately as needed.

Various non-limiting embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

At present, with the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming into financial technology (Fintech). However, due to the security and real-time requirements of the financial industry, technology has also been put forward. higher requirement.

At present, the bank-structured business system has a large number of data consumption tasks to be performed every day, and the number of nodes in the cluster where the business system consumes data is limited. The business system has always been committed to using fewer nodes to pass reasonable Orchestrate and run as many tasks as possible, for example, by dividing the nodes in the cluster into several queues, and then dividing the tasks into several tasks by labeling. Different queues process tasks with different labels, but , when there are more and more tasks to be executed, there are also more and more queues in the business system. When the business peaks, the nodes in the queue are often fully occupied, resulting in the delay in executing high-priority tasks.

Currently, when counting the number of task requests for a certain shard, the optimistic locking mechanism of redis is currently used, by creating a redis key for the current shard and then using the watch command (after using the watch command, it will obtain the key value, and then monitor it. When we execute the task, we will update this value. When updating, first it will compare whether the key value to be updated is the same as the value obtained before. If so, , then continue with the following steps, if not, an error will be reported to the client and execution will not continue). Currently, there is generally no problem. However, when task requests are scheduled frequently, if a task request passes this check and the value is updated, other task requests that arrive at the same time as this task request will only report an error. Then retry, which will lead to a significant increase in the number of task requests and frequent error reports. For this reason, the existing technology needs to be optimized for this situation.

In summary, how to provide an optimization method, device, computer equipment and non-volatile computer-readable storage medium for task request scheduling to effectively reduce the failure of some task request requests due to redis optimistic lock triggering is It is currently a problem that needs to be solved urgently by those skilled in the art.

Therefore, in order to solve the above problem, please refer to Figure 1. An embodiment of the present invention provides an optimization method for task request scheduling, wherein the method includes the steps:

S100. Create an update table in advance;

S200. Calculate the corresponding hash values of the jobids of all task requests;

S300. Calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the calculated remainder to obtain the subscript position of the update table where the target task request is located;

S400. Determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure.

Further, please refer to Figure 4, the optimization method of task request scheduling, wherein the step S100 is to create an update table in advance, including:

Pre-create an update table based on the target shard of the database.

Further, in the optimization method of task request scheduling, an update table is created in advance based on the target fragment of the database, and the base value of the target fragment is initially 0.

During specific implementation, in this embodiment, an update table is created in advance based on the target sharding of the database. Sharding is a type of database partitioning, which divides a large database into smaller, faster, and easier to manage For example, splitting the user database by geographical location is a common example. Users located on the East Coast are allocated to one server, and users located on the West Coast are allocated to another server. Secondly, the target sharding The base value is initially 0, and the base value is the statistical value of the number of task requests in the target shard at the current time.

Further, in the optimization method of task request scheduling, the step S200 is to calculate the corresponding hash values of the jobids of all task requests.

During specific implementation, in this embodiment, after an update table is created in advance based on the target sharding of the database, the corresponding hash values of the jobids of all task requests are calculated, where jobid represents the immutability of the job (task request). And the unique identifier is unique within the business system.

Further, the optimization method of task request scheduling, wherein the step S300 is to calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the calculated remainder to obtain the location of the target task request. Describes the subscript position of the update table.

During specific implementation, in this embodiment, after calculating the corresponding hash values of the jobids of all task requests, the remainder of the length value of the update table is calculated using the hash value, and the calculated remainder -1 is obtained. The subscript position of the update table where the target task request is located.

Further, in the optimization method of task request scheduling, the step S400 is to determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, then Return failure.

During specific implementation, in this embodiment, after obtaining the subscript position of the update table where the target task request is located, it is determined whether the subscript position is empty. If it is empty, a value of 1 is assigned to the subscript position. If If it is not empty, it means that it has been preempted by other task requests, and failure will be returned.

Further, in the optimization method of task request scheduling, the step S400 is to determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, then After returning failure, include:

Add up all the assigned items in the update table, and then add the base value to obtain the number of task requests for the target shard.

During specific implementation, in this embodiment, when it is necessary to obtain the number of task requests of the target shard, all the assigned items in the update table can be added together, and then the base value is added and calculated.

It can be seen from the above method embodiments that the optimization method for task request scheduling provided by the present invention creates an update table based on the target sharding of the database in advance, where the base value of the target shard is initially 0; and then all the Calculate the corresponding hash value of the jobid of the task request; then, use the hash value to calculate the remainder of the length value of the update table, and subtract 1 from the calculated remainder to obtain the subscript position of the update table where the target task request is located. ; Finally, determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, a failure is returned. At the same time, all assigned items in the update table can also be obtained. Add, and then add the base value to get the number of task requests for the target shard. In this way, the method of the present invention can effectively reduce the failure of some task requests due to the triggering of redis optimistic lock. Through the design of the update table, the retry of failed task requests can be effectively reduced, which greatly relieves the scheduling pressure of the business system.

It should be understood that although the present application provides method operation steps as described in the embodiments or flow charts, more or less operation steps may be included based on conventional or non-inventive efforts, and these operation steps are not necessarily implemented in accordance with Examples or flowcharts are executed in sequence. The sequence of steps listed in the embodiments or flowcharts is only one way of executing the sequence of many steps, and does not represent the only execution sequence. It should be noted that there does not necessarily exist a certain sequence between the above steps. Persons of ordinary skill in the art can understand based on the description of the embodiments of the present invention that in different embodiments, the above steps may have different execution orders. That is, it can be executed in parallel, executed interchangeably, and so on. Moreover, at least some of the steps in the embodiments or flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. These sub-steps or The execution order of the stages is not necessarily sequential, but may be performed in turn, alternately or synchronously with other steps or sub-steps of other steps or at least part of the stages.

Based on the above embodiment, please refer to Figure 2. Another embodiment of the present invention also provides an optimization device for task request scheduling, wherein the device includes:

Create module 11, used to create an update table in advance;

The calculation module 12 is used to calculate the corresponding hash values of the jobids of all task requests;

The remainder module 13 is used to calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the remainder to obtain the subscript position of the update table where the target task request is located;

The judgment module 14 is used to judge whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure.

Further, please refer to Figure 4, the optimization device for task request scheduling, wherein the pre-created update table includes:

Pre-create an update table based on the target shard of the database.

Further, in the task request scheduling optimization device, an update table is created in advance based on the target fragment of the database, and the base value of the target fragment is initially 0.

During specific implementation, in this embodiment, an update table is created in advance based on the target sharding of the database. Sharding is a type of database partitioning, which divides a large database into smaller, faster, and easier to manage For example, splitting the user database by geographical location is a common example. Users located on the East Coast are allocated to one server, and users located on the West Coast are allocated to another server. Secondly, the target sharding The base value is initially 0, and the base value is the statistical value of the number of task requests in the target shard at the current time.

Further, in the task request scheduling optimization device, the corresponding hash values are calculated from the jobids of all task requests.

During specific implementation, in this embodiment, after an update table is created in advance based on the target sharding of the database, the corresponding hash values of the jobids of all task requests are calculated, where jobid represents the immutability of the job (task request). And the unique identifier is unique within the business system.

Further, the optimization device for task request scheduling, wherein the remainder of the length value of the update table is calculated using the hash value, and the calculated remainder is subtracted by 1 to obtain the update table where the target task request is located. subscript position.

During specific implementation, in this embodiment, after calculating the corresponding hash values of the jobids of all task requests, the remainder of the length value of the update table is calculated using the hash value, and the calculated remainder -1 is obtained. The subscript position of the update table where the target task request is located.

Further, in the task request scheduling optimization device, the step is to determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure.

During specific implementation, in this embodiment, after obtaining the subscript position of the update table where the target task request is located, it is determined whether the subscript position is empty. If it is empty, a value of 1 is assigned to the subscript position. If If it is not empty, it means that it has been preempted by other task requests, and failure will be returned.

Further, the optimization device for task request scheduling, wherein the step of determining whether the subscript position is empty, and if it is empty, assigns a value of 1 to the subscript position, and if it is not empty, returns after failure. ,include:

Add up all the assigned items in the update table, and then add the base value to obtain the number of task requests for the target shard.

During specific implementation, in this embodiment, when it is necessary to obtain the number of task requests of the target shard, all the assigned items in the update table can be added together, and then the base value is added and calculated.

It can be seen from the above device embodiments that the optimization device for task request scheduling provided by the present invention creates an update table based on the target fragmentation of the database in advance, wherein the base value of the target fragmentation is initially 0; and then all the Calculate the corresponding hash value of the jobid of the task request; then, use the hash value to calculate the remainder of the length value of the update table, and subtract 1 from the calculated remainder to obtain the subscript position of the update table where the target task request is located. ; Finally, determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, a failure is returned. At the same time, all assigned items in the update table can also be obtained. Add, and then add the base value to get the number of task requests for the target shard. In this way, the device of the present invention can effectively reduce the failure of some task requests due to the triggering of redis optimistic lock. Through the design of the update table, the retry of failed task requests can be effectively reduced, which greatly relieves the scheduling pressure of the business system.

Based on the above embodiment, please refer to Figure 3. Another embodiment of the present invention further provides a computer device, wherein the computer device 10 includes:

The memory 120 and one or more processors 110 are introduced in FIG. 3 by taking one processor 110 as an example. The processor 110 and the memory 120 may be connected through a communication bus or other means. In FIG. 3 , the connection through a communication bus is taken as an example.

The processor 110 is used to complete various control logics of the computer device 10, which can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single chip microcomputer, an ARM ( Acorn RISCMachine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components. Also, processor 110 may be any conventional processor, microprocessor, or state machine. Processor 110 may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

As a non-volatile computer-readable storage medium, the memory 120 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the optimization method for task request scheduling in the embodiment of the present invention. computer program. The processor 110 executes various functional applications and data processing of the computer device 10 by running non-volatile software programs, instructions and units stored in the memory 120, that is, implementing the optimization method of task request scheduling in the above method embodiment. .

The memory 120 may include a program storage area and a data storage area, where the program storage area may store an operating device and an application program required for at least one function; the storage data area may store data created according to the use of the computer device 10 , etc. In addition, the memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 120 optionally includes memory located remotely relative to the processor 110 , and these remote memories may be connected to the computer device 10 through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

One or more units are stored in the memory 120. When executed by one or more processors 110, the optimization method of task request scheduling in any of the above method embodiments can be implemented. For example, the above-described figure can be implemented. Method steps S100 to S400 in 1.

Those skilled in the art can understand that the schematic diagram of the hardware structure shown in Figure 3 is only a schematic diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer equipment to which the solution of the present invention is applied. A specific computer Devices may include more components than those shown in the figures, or certain components may be combined or have different arrangements of components.

Based on the above embodiments, the present invention also provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and the computer program is executed by at least one processor When , the optimization method of task request scheduling in any of the above method embodiments can be implemented. For example, the method steps S100 to S400 in Figure 1 described above can be implemented.

By way of example, non-volatile storage media can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) as external cache memory. By way of illustration and not limitation, RAM may be provided in a form such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchl ink DRAM (SLDRAM) As well as many forms such as direct Rambus RAM (DRRAM). The disclosed memory components or memories of the operating environments described herein are intended to include one or more of these and/or any other suitable types of memory.

Another embodiment of the present invention provides a computer program product, the computer program product including a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions, when the program When the instructions are executed by the processor, the optimization method of task request scheduling in any of the above method embodiments can be implemented. For example, the method steps S100 to S400 in Figure 1 described above can be implemented.

The embodiments described above are only illustrative, in which the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place. Or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the part of the above technical solution that essentially contributes to the relevant technology can be embodied in the form of a software product. The computer software product can exist in a computer-readable storage medium, such as ROM/RAM, disk , optical disk, etc., including a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute various embodiments or methods of certain parts of the embodiments.

Among other things, conditional language such as "could", "could", "could" or "could" is generally intended to convey that unless otherwise specifically stated or otherwise understood within the context in which it is used, Certain embodiments can include (whereas other embodiments do not) certain features, elements, and/or operations. Accordingly, such conditional language is also generally intended to imply that features, elements, and/or operations are in any event required for one or more embodiments or that one or more embodiments must be included for use with or without input or prompts. Logic that determines whether such features, elements, and/or operations are included or will be performed in any particular implementation.

What has been described herein in this specification and the accompanying drawings includes examples that can provide an optimization method, apparatus, computer equipment, and non-volatile computer-readable storage media for task request scheduling. Of course, not every conceivable combination of elements and/or methods can be described for the purpose of describing the various features of the present disclosure, but it is recognized that many additional combinations and permutations of the disclosed features are possible. Therefore, it is obvious that various modifications can be made to the present disclosure without departing from the scope or spirit of the present disclosure, but all such various modifications should fall within the protection scope of the appended claims of the present invention. Additionally, or in the alternative, other embodiments of the disclosure may be apparent from a consideration of the specification and drawings, and practice of the disclosure as presented herein. It is intended that the examples set forth in the specification and drawings are to be considered in all respects as illustrative and not restrictive. Although specific terms are employed herein, they are used in a generic and descriptive sense and not for purpose of limitation.

Claims (10)

1. An optimization method for task request scheduling, which is characterized by including: Create an update table in advance; Calculate the corresponding hash values of the jobids of all task requests; Use the hash value to calculate the remainder of the length value of the update table, and subtract 1 from the calculated remainder to obtain the subscript position of the update table where the target task request is located; Determine whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure. 2. The optimization method for task request scheduling according to claim 1, characterized in that said pre-creating an update table includes: Pre-create an update table based on the target shard of the database. 3. The optimization method for task request scheduling according to claim 2, wherein an update table is created in advance based on the target fragmentation of the database, wherein the base value of the target fragmentation is initially 0. 4. The optimization method for task request scheduling according to claim 3, wherein the step is to determine whether the subscript position is empty, and if it is empty, assign a value of 1 to the subscript position; if it is not empty, , then return after failure, including: Add up all the assigned items in the update table, and then add the base value to obtain the number of task requests for the target shard. 5. An optimization device for task request scheduling, characterized by comprising: Create module, used to create an update table in advance; The calculation module is used to calculate the corresponding hash value of the jobid of all task requests; The remainder module is used to calculate the remainder of the length value of the update table through the hash value, and subtract 1 from the remainder to obtain the subscript position of the update table where the target task request is located; A judgment module is used to judge whether the subscript position is empty. If it is empty, assign a value of 1 to the subscript position. If it is not empty, return failure. 6. The optimization device for task request scheduling according to claim 5, characterized in that said pre-creating an update table includes: Pre-create an update table based on the target shard of the database. 7. The optimization device for task request scheduling according to claim 6, wherein an update table is created in advance based on the target fragment of the database, wherein the base value of the target fragment is initially 0. 8. The optimization device for task request scheduling according to claim 7, wherein the step is to determine whether the subscript position is empty, and if it is empty, assign a value of 1 to the subscript position; if it is not empty, , then return after failure, including: Add up all the assigned items in the update table, and then add the base value to obtain the number of task requests for the target shard. 9. A computer device, characterized in that the computer 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 can be executed by the at least one processor. When the computer program is executed by the at least one processor, the task request scheduling as described in any one of claims 1-4 can be implemented. optimization method. 10. A non-volatile computer-readable storage medium, characterized in that the non-volatile computer-readable storage medium stores a computer program. When the computer program is executed by at least one processor, the computer program can realize the following: Optimization method for task request scheduling according to any one of requirements 1-4.