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

Abstract

The application discloses a task request scheduling optimization method and device, computer equipment and a medium, and relates to the technical field of information processing. The method comprises the following steps: an update table is created in advance; the hash value corresponding to the jobid of all task requests is calculated; the length value of the update table is divided by the hash value to obtain the subscript position of the target task request in the update table by subtracting 1 from the remainder; and it is judged whether the subscript position is empty. If the subscript position is empty, the subscript position is assigned a value of 1; if the subscript position is not empty, the result is returned as a failure. The method can effectively reduce the situation that part of the task requests fail due to the triggering of the redis optimistic lock.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information processing, and in particular relates to a task request scheduling optimization method and device, computer equipment and a nonvolatile computer readable storage medium. BACKGROUND

[0002] At present, with the development of computer technology, more and more technologies are applied in the financial field, and the traditional financial industry is gradually changing to Fintech, but due to the security and real-time requirements of the financial industry, higher requirements are also put forward for technology.

[0003] At present, the business system of the bank structure has a large number of consumption data tasks to be executed every day, and the number of nodes in the cluster of the business system consumption data is limited. The business system has always been committed to running more tasks with fewer nodes through reasonable arrangement, for example, by dividing the nodes in the cluster into several queues, and then dividing the tasks into several tasks through tagging. Different queues process tasks with different labels, but when the number of tasks to be executed increases, the number of queues of the business system also increases. When the business peak occurs, the nodes in the queue are often in a fully occupied state, resulting in that the tasks with high priority cannot be executed in time.

[0004] At present, when the number of task requests of a certain shard is counted, the optimistic lock mechanism of redis is used, a redis key is created for the current shard, and then the watch command is used to realize it (after using the watch command, it will get the value of the key, and then monitor it. When we execute the task, we will update the value. When updating, it will first compare whether the key value to be updated now is the same as the value obtained before, if they are the same, continue the next step, if they are not the same, report an error to the client and do not continue to execute). Currently, there is no problem in general, but when the task request scheduling is frequent, if a task request passes this check and updates the value, other task requests that come at the same time as the task request will only report an error and then retry, which will cause a large increase in the number of task requests and frequent errors. Therefore, the prior art needs to be optimized for this situation.

[0005] In summary, how to provide a task request scheduling optimization method, device, computer equipment and nonvolatile computer readable storage medium to effectively reduce the situation that part of the task request request fails due to the redis optimistic lock trigger is a problem that needs to be solved by the technical personnel in the field at present. SUMMARY

[0006] In view of the deficiencies of the prior art described above, the purpose of the present application is to provide a task request scheduling optimization method, device, computer equipment and non-volatile computer readable storage medium which can be used in the field of financial technology or other related fields, and can effectively reduce the situation of partial task request request failure caused by redis optimistic lock triggering.

[0007] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0008] A task request scheduling optimization method, comprising:

[0009] Pre-creating an update table;

[0010] Calculating the hash value corresponding to the jobid of all task requests;

[0011] Obtaining the index position of the target task request in the update table by taking the remainder of the length value of the update table with the hash value and subtracting 1 from the remainder;

[0012] Judging whether the index position is empty, if empty, assigning 1 to the index position, if not empty, returning failure.

[0013] In a further technical solution, the task request scheduling optimization method, wherein the pre-created update table comprises:

[0014] Pre-creating an update table based on the target shard of the database.

[0015] In a further technical solution, the task request scheduling optimization method, wherein the pre-created update table based on the target shard of the database, wherein the base value of the target shard is initially 0.

[0016] In a further technical solution, the task request scheduling optimization method, wherein after judging whether the index position is empty, if empty, assigning 1 to the index position, if not empty, returning failure, comprising:

[0017] Adding all assigned items in the update table, and then adding the base value to obtain the number of task requests of the target shard.

[0018] A task request scheduling optimization device, comprising:

[0019] A creating module for pre-creating an update table;

[0020] A calculating module for calculating the hash value corresponding to the jobid of all task requests;

[0021] A remainder module is configured to perform a remainder operation on a length value of the update table by using the hash value, and subtract 1 from the remainder to obtain an index position of the update table where the target task request is located.

[0022] A judging module is configured to judge whether the index position is empty. If the index position is empty, the index position is assigned a value of 1. If the index position is not empty, a failure is returned.

[0023] In a further technical solution, the task request scheduling optimization device, wherein the update table is created in advance, includes:

[0024] An update table is created in advance based on a target shard of a database.

[0025] In a further technical solution, the task request scheduling optimization device, wherein the update table is created in advance based on a target shard of a database, and a base value of the target shard is initially 0.

[0026] In a further technical solution, the task request scheduling optimization device, wherein after judging whether the index position is empty, if the index position is empty, the index position is assigned a value of 1, and if the index position is not empty, a failure is returned, includes:

[0027] All assigned items in the update table are added, and then a base value is added to obtain a number of task requests of the target shard.

[0028] A computer device, wherein the computer device includes at least one processor; and,

[0029] A memory connected in communication with the at least one processor; wherein,

[0030] The memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, can implement the task request scheduling optimization method according to any one of the above.

[0031] A non-volatile computer readable storage medium, wherein the non-volatile computer readable storage medium stores a computer program, and the computer program, when executed by at least one processor, can implement the task request scheduling optimization method according to any one of the above.

[0032] Compared with the prior art, the application provides an optimization method and device for task request scheduling, computer equipment and a nonvolatile computer readable storage medium, wherein the method comprises the following steps: an update table is created in advance; hash values corresponding to jobids of all task requests are calculated; a length value of the update table is divided by the hash value to obtain a remainder, and the remainder is subtracted by 1 to obtain an index position of a target task request in the update table; it is judged whether the index position is empty, if yes, the index position is assigned a value of 1, and if no, a failure is returned. The method can effectively reduce the situation that some task requests fail to be requested due to triggering of a redis optimistic lock. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0034] Figure 1 A flowchart of an optimization method for task request scheduling provided by the embodiment of the present application.

[0035] Figure 2 A functional module diagram of an optimization device for task request scheduling provided by the embodiment of the present application.

[0036] Figure 3 A hardware structure diagram of the computer equipment provided by the embodiment of the present application.

[0037] Figure 4 An update table provided by the embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and effects of the present application more clear and explicit, the following will further describe the present application in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0039] In the description of the application, "include", "including", "have", "has", and the like are open terms, that is, mean including but not limited to. The description of the terms "one embodiment", "one specific embodiment", "some embodiments", "for example" and the like means that the specific features, structures or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. The order of steps involved in each embodiment is used to illustrate the implementation of the present application, and the order of steps is not limited. The order of steps can be adjusted as appropriate.

[0040] The various non-limiting embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0041] At present, with the development of computer technology, more and more technologies are applied in the financial field, and the traditional financial industry is gradually changing to Fintech, but due to the security and real-time requirements of the financial industry, higher requirements are also put forward for technology.

[0042] At present, the business system of the bank structure has a large number of consumption data tasks to be executed every day, and the number of nodes in the cluster of the business system consumption data is limited. The business system has always been committed to running more tasks with fewer nodes through reasonable arrangement, for example, by dividing the nodes in the cluster into several queues, and then dividing the tasks into several tasks by tagging, different queues handle different tasks with different labels. However, when the number of tasks to be executed is increasing, the number of queues of the business system is also increasing, and when the business peak, the nodes in the queue are often in a state of full occupation, resulting in that the tasks with high priority cannot be executed for a long time.

[0043] Currently, when counting the number of task requests of a certain shard, the optimistic lock mechanism of redis is used, that is, a key of redis is created for the current shard, and then the watch command is used to realize it (after using the watch command, it will get the value of the key, and then monitor it. When we execute the task, we will update the value. When updating, it will first compare whether the key value to be updated now is the same as the value obtained before. If they are the same, continue the next step. If they are not the same, report an error to the client and do not continue to execute). Currently, there is generally no problem, but when the task request scheduling is frequent, if a task request passes this check and updates the value, other task requests that come at the same time as the task request can only report an error and then retry, which will cause the task request to increase significantly and the error to occur frequently. Therefore, the prior art needs to be optimized for this situation.

[0044] In summary, how to provide a task request scheduling optimization method, device, computer equipment and non-volatile computer readable storage medium to effectively reduce the situation that part of the task request request fails due to redis optimistic lock triggering is a problem that needs to be solved by the technical personnel in the field at present.

[0045] Therefore, in order to solve the above problems, please refer to Figure 1 The embodiment of the application provides a task request scheduling optimization method, wherein the method comprises the following steps:

[0046] S100, an update table is created in advance;

[0047] S200, the hash value corresponding to the jobid of all task requests is calculated;

[0048] S300, the length value of the update table is divided by the hash value to obtain the index position of the target task request in the update table;

[0049] S400, it is judged whether the index position is empty. If it is empty, the index position is assigned a value of 1. If it is not empty, it returns failure.

[0050] Further, please refer to Figure 4 The task request scheduling optimization method, wherein the step S100, an update table is created in advance, comprises:

[0051] An update table is created in advance based on the target shard of the database.

[0052] Further, the task request scheduling optimization method, wherein the target shard of the database is used to create an update table, and the base value of the target shard is initially 0.

[0053] In implementation, in the embodiment, an update table is created based on the target shard of the database in advance, and the base value of the target shard is initially 0.

[0054] Further, the task request scheduling optimization method, wherein the step S200, the hash value corresponding to the jobid of each task request is calculated.

[0055] In implementation, in the embodiment, after the update table is created based on the target shard of the database in advance, the hash value corresponding to the jobid of each task request is calculated, wherein the jobid represents an immutable and unique identifier of a job (task request) and is unique in the business system.

[0056] Further, the task request scheduling optimization method, wherein the step S300, the length value of the update table is divided by the hash value to obtain a remainder, and 1 is subtracted from the remainder to obtain the index position of the target task request in the update table.

[0057] In implementation, in the embodiment, after the hash value corresponding to the jobid of each task request is calculated, the length value of the update table is divided by the hash value to obtain a remainder, and 1 is subtracted from the remainder to obtain the index position of the target task request in the update table.

[0058] Further, the task request scheduling optimization method, wherein the step S400, it is judged whether the index position is empty, if empty, the index position is assigned a value 1, if not empty, it returns failure.

[0059] In implementation, in the embodiment, after the index position of the target task request in the update table is obtained, it is judged whether the index position is empty, if empty, the index position is assigned a value 1, if not empty, it means that the index position has been occupied by other task requests, and then it returns failure.

[0060] Further, the task request scheduling optimization method, wherein the step S400, judging whether the subscript position is empty, if empty, assigning 1 to the subscript position, if not empty, returning failure, comprises:

[0061] Adding all the assigned items in the update table and then adding the base value to obtain the number of task requests of the target shard.

[0062] In the embodiment, when the number of task requests of the target shard is needed, all the assigned items in the update table are added and then the base value is added to obtain the number of task requests of the target shard.

[0063] From the above method embodiment, the task request scheduling optimization method provided by the application can effectively reduce the situation that part of the task requests are requested to fail due to the redis optimistic lock trigger, and the retry of the failed task request can be effectively reduced through the design of the update table, and the scheduling pressure of the business system is greatly relieved.

[0064] It should be understood that, although the application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps can be included based on conventional or non-inventive labor, and the operation steps are not necessarily executed in the order of the embodiments or flowcharts. The order of steps listed in the embodiments or flowcharts is only one of the many execution orders, and does not represent the only execution order. It should be noted that there is no certain sequence between the above steps, and those skilled in the art can understand from the description of the embodiments of the application that the above steps can have different execution orders in different embodiments, that is, they can be executed in parallel, and can be exchanged and executed, etc. Moreover, at least part of the steps in the embodiments or flowcharts can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation, alternation or synchronization with other steps or sub-steps or stages of other steps.

[0065] Based on the above embodiments, please refer to Figure 2 Another embodiment of the present application also provides a task request scheduling optimization device, wherein the device comprises:

[0066] A creating module 11 is configured to create an update table in advance;

[0067] A calculating module 12 is configured to calculate hash values corresponding to jobids of all task requests;

[0068] A remainder module 13 is configured to take the hash values as divisors and the length value of the update table as a dividend, and subtract 1 from the remainder to obtain the index position of the target task request in the update table;

[0069] A judging module 14 is configured to judge whether the index position is empty, if yes, assign 1 to the index position, and if not, return failure.

[0070] Further, please refer to Figure 4 The task request scheduling optimization device, wherein the update table is created in advance based on a target shard of a database.

[0071] The update table is created in advance based on the target shard of the database.

[0072] Further, the task request scheduling optimization device, wherein the update table is created in advance based on the target shard of the database, and the base value of the target shard is initially 0.

[0073] In specific implementation, in this embodiment, the update table is created in advance based on the target shard of the database, wherein sharding is a kind of database partitioning, which divides a large database into smaller, faster and easier-to-manage parts, for example, splitting a user database according to geographical location is a common example, users located on the east coast are divided into one server, and users located on the west coast are divided into another server; secondly, the base value of the target shard 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.

[0074] Further, the task request scheduling optimization device, wherein the hash values corresponding to the jobids of all task requests are calculated.

[0075] In specific implementation, in this embodiment, after the update table is created in advance based on the target shard of the database, the hash values corresponding to the jobids of all task requests are calculated, wherein jobid represents an immutable and unique identifier of a job (task request) and is unique in a business system.

[0076] Further, the task request scheduling optimization device, wherein the length of the update table is divided by the hash value to obtain a remainder, and the remainder minus 1 is the index position of the target task request in the update table.

[0077] In implementation, in the embodiment, after the jobid of all task requests is calculated to obtain the corresponding hash value, the length of the update table is divided by the hash value to obtain a remainder, and the remainder minus 1 is the index position of the target task request in the update table.

[0078] Further, the task request scheduling optimization device, wherein the index position is judged to be empty or not, if empty, the index position is assigned 1, if not empty, the process returns failure.

[0079] In implementation, in the embodiment, after the index position of the target task request in the update table is obtained, the index position is judged to be empty or not, if empty, the index position is assigned 1, if not empty, it means that the index position has been occupied by other task requests, and the process returns failure.

[0080] Further, the task request scheduling optimization device, wherein after the index position is judged to be empty or not, if empty, the index position is assigned 1, if not empty, the process returns failure, the process further comprises:

[0081] The task request number of the target shard is obtained by adding all assigned items in the update table and adding the base value.

[0082] In implementation, in the embodiment, when the task request number of the target shard is needed, all assigned items in the update table are added and the base value is added to obtain the result.

[0083] From the above device embodiment can know that the task request scheduling optimization device provided by the application creates an update table in advance based on the target shard of the database, wherein the base value of the target shard is initially 0; then the hash value corresponding to the jobid of all task requests is calculated; then, the length value of the update table is divided by the hash value, and the remainder obtained is subtracted by 1 to obtain the subscript position of the target task request in the update table; finally, it is judged whether the subscript position is empty, if empty, the subscript position is assigned a value of 1, if not empty, it returns failure; at the same time, the sum of all assigned items in the update table can also be taken, and then the base value is added to obtain the number of task requests of the target shard. In this way, the device of the application can effectively reduce the situation that part of the task request requests fail due to the redis optimistic lock trigger, and through the design of the update table, the retry of the failed task request can be effectively reduced, and the scheduling pressure of the business system is greatly relieved.

[0084] Based on the above embodiment, please refer to Figure 3 The application further provides a computer device, wherein the computer device 10 comprises:

[0085] a memory 120 and one or more processors 110, Figure 3 In the embodiment, the processor 110 and the memory 120 can be connected through a communication bus or other means, Figure 3 In the embodiment, the connection through the communication bus is taken as an example.

[0086] The processor 110 is used to complete various control logics of the computer device 10, and 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 RISC Machine) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components or any combination of these components. In addition, the processor 110 can also be any conventional processor, microprocessor or state machine. The processor 110 can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0087] The memory 120, as a non-volatile computer readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the computer program corresponding to the task request scheduling optimization method in the embodiment of the present application. The processor 110 executes the non-volatile software programs, instructions and units stored in the memory 120, thereby performing various functional applications and data processing of the computer device 10, that is, implementing the task request scheduling optimization method in the above method embodiment.

[0088] The memory 120 can include a program storage area and a data storage area, wherein the program storage area can store application programs required by the operation device and at least one function; the data storage area can store data created according to the use of the computer device 10, etc. In addition, the memory 120 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 120 can optionally include a memory remotely arranged with respect to the processor 110, and these remote memories can be connected to the computer device 10 through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0089] One or more units are stored in the memory 120, and when executed by the one or more processors 110, the task request scheduling optimization method in any one of the above method embodiments can be implemented, for example, the method steps S100 to S400 in the above description Figure 1 can be implemented.

[0090] Those skilled in the art can understand that the hardware structure diagram shown in Figure 3 is only a schematic diagram of part of the structure related to the present application scheme, and does not constitute a limitation on the computer device to which the present application scheme is applied. The specific computer device can include more components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0091] Based on the above embodiments, the present application further 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 to implement the task request scheduling optimization method in any one of the above method embodiments, for example, the method steps S100 to S400 in the above description Figure 1 can be implemented.

[0092] By way of example, nonvolatile storage can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile storage can include random-access memory (RAM), which acts as external cache memory. By way of illustration, and not limitation, RAM can be available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The disclosed memory component or elements of the operational environment described herein are intended to comprise one or more of these and / or any other suitable types of memory.

[0093] Another embodiment of the present application provides a computer program product, which comprises a computer program stored on a non-volatile computer readable storage medium, the computer program comprising program instructions which, when executed by a processor, implement the optimization method of task request scheduling in any of the method embodiments described above, for example, implement the method steps S100 to S400 in the method embodiment described above. Figure 1

[0094] The above-described embodiments are merely illustrative for the present application, and the units illustrated as separated components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0095] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware. Based on such understanding, the above technical solutions or the part that contributes to the related art can be embodied in the form of a software product, which can exist in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of the various embodiments or some parts of the embodiments.

[0096] ​Conditional language, such as, among others, "can," "could," "might," or "may," unless specifically stated otherwise, generally are intended to convey that a certain feature, element, or operation can or might be included in some implementations, but not all implementations. Thus, such conditional language generally is not intended to imply that future implementations will or will not include the feature, element, or operation. Conclusions of this nature include that the feature, element or operation is or will be required in all implementations or that an implementation will or will not include the feature, element or operation in any future implementation.

[0097] What has been described herein is included in the disclosure of the application, including the specification and drawings. Of course, not every combination of components and / or methods described herein can be necessary to practice the disclosure in its various aspects, but it is contemplated that the different features described herein can be combined in different ways to provide many alternative embodiments of the disclosure not specifically described in the present disclosure. Thus, it should be apparent that the disclosure is not limited to the specific embodiments described herein, but has many alternatives. It should also be apparent that features described herein can be incorporated into other devices or applications without departing from the scope or spirit of the disclosure. It is also intended that the disclosure cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure. It is intended that the specification and figures be considered as illustrative only and not restrictive in any manner. Although specific terms are employed in the specification and figures, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

1. An optimization method for task request scheduling, characterized in that, include: Create an update table beforehand; Calculate the corresponding hash value for the jobid of all task requests; The index position of the target task request in the update table is obtained by taking the remainder of the hash value and subtracting 1 from the remainder. Determine if the index position is empty. If it is empty, assign the value 1 to the index position. If it is not empty, return failure. The aforementioned pre-creating of an update table includes: Create an update table in advance based on the target shards of the database; The step involves creating an update table based on the target shard of the database, wherein the base value of the target shard is initially set to 0. Wherein, base is the statistical value of the number of task requests within the target slice at the current time, and jobid is the unique identifier of the task request.

2. The optimization method for task request scheduling according to claim 1, characterized in that, The step of determining whether the index position is empty, and if it is empty, assigning the value 1 to the index position; and if it is not empty, returning a failure message, includes: Add all the values ​​assigned in the update table together, and then add the base value to obtain the number of task requests for the target shard.

3. An optimization device for task request scheduling, characterized in that, include: Create a module to pre-create an update table; The calculation module is used to calculate the corresponding hash value for the jobid of all task requests; The modulo module is used to calculate the remainder of the hash value with respect to the length of the update table, and then subtract 1 from the remainder to obtain the index position of the target task request in the update table. The judgment module is used to determine whether the index position is empty. If it is empty, the index position is assigned the value 1. If it is not empty, failure is returned. The aforementioned pre-creating of an update table includes: Create an update table in advance based on the target shards of the database; The step involves creating an update table based on the target shard of the database, wherein the base value of the target shard is initially set to 0. Wherein, base is the statistical value of the number of task requests within the target slice at the current time, and jobid is the unique identifier of the task request.

4. The task request scheduling optimization apparatus according to claim 3, characterized in that, The step of determining whether the index position is empty, and if it is empty, assigning the value 1 to the index position; and if it is not empty, returning a failure message, includes: Add all the values ​​assigned in the update table together, and then add the base value to obtain the number of task requests for the target shard.

5. 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, it can implement the task request scheduling optimization method as described in any one of claims 1-2.

6. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores a computer program that, when executed by at least one processor, can implement the optimized method for task request scheduling as described in any one of claims 1-2.

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