WO2019061978A1

WO2019061978A1 - Data caching method and apparatus, server and medium

Info

Publication number: WO2019061978A1
Application number: PCT/CN2018/074849
Authority: WO
Inventors: 张宇
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-09-26
Filing date: 2018-01-31
Publication date: 2019-04-04
Also published as: CN107741962B; CN107741962A

Abstract

A data caching method and apparatus, a server and a medium. The method comprises: saving various pieces of variable data in an ordered sequence, wherein a weight is correspondingly set for each variable data (S101); monitoring whether the pieces of variable data execute operations, and when it is monitored that the variable data execute the operations, adjusting the weights of the variable data executing the operations (S102); and according to the adjusted weight of each piece of variable data, inserting variable data with the weights meeting a pre-set condition into a one-way chain table in a header inserting mode, wherein the one-way chain table has a pre-set length (S103). According to the data caching method and the server, searching can be reduced by directly calling variable data from a one-way chain table, saving on the running consumption of programs, and a certain number of js variables can be kept in a memory for use by developers without frequently having to access a prototype chain to acquire variables, such that the performance of variable acquisition can be greatly improved.

Description

Data cache method, device, server and medium

The present application claims the priority of the Chinese Patent Application, filed on Sep. 26, 2017, the entire disclosure of which is hereby incorporated by reference. .

Technical field

The present application belongs to the field of data processing technologies, and in particular, to a data caching method, apparatus, server, and medium.

Background technique

Front-end technology has developed rapidly in recent years, and the performance of front-end code has received more and more attention. Javascript plays an important role in front-end technology. As users and enterprises pay more attention to the system experience, page effects are gradually upgraded, and dom operations More and more frequent, the search based on the js prototype chain is extremely consuming the performance of the program.

technical problem

In view of this, the embodiment of the present application provides a data caching method and a server to solve the problem that the search for variable data based on the js prototype chain in the prior art affects the running performance of the program.

Technical solution

A first aspect of the embodiments of the present application provides a data caching method, including:

Each variable data is stored in an ordered sequence, and each variable data is correspondingly provided with an importance degree identification information;

Monitoring whether each variable data performs an operation, and adjusting the importance degree identification information of the variable data of the execution operation when monitoring the variable data execution operation;

The variable data indicating that the importance degree identification information satisfies the preset condition is inserted into the singly linked list by means of the header insertion manner according to the importance degree identification information of the adjusted variable data; wherein the singly linked list has a preset length.

A second aspect of the embodiments of the present application provides a data cache apparatus, including:

a variable storage module, configured to store each variable data in an ordered sequence, and each variable data is correspondingly set with an importance degree identification information;

The adjustment module is configured to monitor whether each variable data performs an operation, and adjust the importance degree identification information of the variable data that performs the operation when the variable data execution operation is monitored;

The insertion module is configured to insert, according to the adjusted importance degree information of each variable data, the variable data whose importance degree identification information meets the preset condition is inserted into the singly linked list by means of a header insertion manner; wherein the singly linked list has a pre- Set the length.

A third aspect of the embodiments of the present application provides a data cache server, including a memory, a processor, and the computer storing computer readable instructions executable on the processor, the processor executing the computer The following steps are implemented when the instructions are readable:

A fourth aspect of the embodiments of the present application provides a computer readable storage medium storing computer readable instructions, wherein the computer readable instructions are implemented by at least one processor The following steps:

Beneficial effect

In the embodiment of the present application, each variable data is stored in an ordered sequence, and each variable data is correspondingly set with a weight, monitoring whether each variable data performs an operation, and when the variable data is monitored, the operation variable is executed. The weight of the data is adjusted. According to the weight of each adjusted variable data, the variable data whose weight meets the preset condition is inserted into the singly linked list through the header insertion mode, and the singly linked list has a fixed length and is stored in a new one. When the variable data is used, the data at the end of the singly linked list will be eliminated. Therefore, the variable data in the single necklace table is a commonly used variable. By directly calling the variable data from the singly linked list, the search can be reduced, and the consumption of the program is saved. It can maintain a certain number of js variables in memory for developers to use without having to access the prototype chain frequently to obtain variables, which can greatly improve the performance of variable acquisition.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description are only the present application. For some embodiments, other drawings may be obtained from those of ordinary skill in the art without departing from the drawings.

1 is a flowchart of a data caching method provided by an embodiment of the present application;

FIG. 2 is a flowchart of an implementation of step S101 in FIG. 1;

FIG. 3 is a flowchart of an implementation of step S103 in FIG. 1;

4 is a flowchart of another data caching method provided by an embodiment of the present application;

FIG. 5 is a flowchart of still another data caching method provided by an embodiment of the present application;

6 is a schematic diagram of an operating environment of a data caching program provided by an embodiment of the present application

FIG. 7 is a functional block diagram of a data caching program provided by an embodiment of the present application.

Embodiments of the invention

In the following description, for purposes of illustration and description However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the application.

In order to explain the technical solutions described in the present application, the following description will be made by way of specific embodiments.

The technical terms "first", "second" and the like in the specification and claims of the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order of precedence. It is to be understood that the data so used may be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein.

Embodiment 1

FIG. 1 is a flowchart showing an implementation process of a data caching method according to Embodiment 1 of the present application, which is described in detail as follows:

In step S101, each variable data is stored in an ordered sequence, and each variable data is correspondingly provided with an importance degree identification information.

Among them, variable data can be accessed in the form of key-value pairs. For example, all variable data is encapsulated in an object obj, which encapsulates a key (key value) containing a hash as an index, an object weight, and a data entity object. The index can be from 0 to the maximum value of the cacheable variable according to the user configuration.

In this embodiment, the importance degree identification information may specifically be a weight, that is, each variable data is correspondingly set with a weight, and the initial value of the weight of each variable data may be the same. After the subsequent execution operation, the weight of the variable data changes. For example, it becomes larger or smaller. Here, weight is a relative concept for a certain indicator. The weight of an indicator refers to the relative importance of the indicator in the overall evaluation. Specifically, each variable data may correspondingly set an initial weight initial value, a preset correspondence between the execution operation and the weight level, and a correspondence between the weight level and the weight adjustment range, and if the execution of a certain variable data is monitored subsequently The operation determines the weight level corresponding to the performed operation of the monitored variable data according to the corresponding relationship between the execution operation and the weight level, and further determines the corresponding weight according to the determined weight level and the correspondence between the weight level and the weight adjustment range. The adjustment range is adjusted according to the determined weight adjustment range to adjust the weight of the above-mentioned monitored variable data.

Referring to FIG. 2, in an embodiment, step S101 can be implemented by the following process:

Step S201: If the type of the target variable data is Object, acquire an address of the target variable data, and calculate a hash code of the target variable data according to the address, where the target variable data is any one of each variable data. Variable data.

Step S202, if the type of the target variable data is String, acquire a character string included in the target variable data, and calculate a hash code of the target variable data according to the character string.

Step S203, if the type of the target variable data is Integer, acquire a number included in the target variable data, and calculate a hash code of the target variable data according to the number.

Among them, you can use the setData method, call H (key) to calculate the hash code of each variable through the index value. The hash code for calculating the variable data can also be a method well known to those skilled in the art, and will not be described here. In this step, the calculated hash code is used as the corresponding variable data.

Step S204, after calculating the hash code of each variable data, storing the corresponding variable data into the ordered sequence according to the calculated hash code.

Specifically, the corresponding variable data may be stored in the sequence item of the ordered sequence according to the calculated hash code, and how to store the corresponding variable data into the sequence item of the ordered sequence according to the calculated hash code. This is accomplished with reference to techniques well known to those skilled in the art and will not be described herein.

It can be understood that by calculating the hash code of each variable and then storing the corresponding variable data in the ordered sequence according to the hash code, the variable data can be stored more uniformly in the ordered sequence.

In step S102, it is monitored whether each variable data performs an operation, and when the variable data execution operation is monitored, the importance degree identification information of the variable data that performs the operation is adjusted.

The operation may be specifically called, but is not limited thereto. If a variable data has been executed recently, it indicates that the variable data is the more commonly used variable data; if a variable data has not been executed for a period of time, it can be stated that the variable data is less commonly used variable data. .

It can be understood that if a certain variable data is detected to perform an operation, the variable data can be adjusted according to the importance degree of the variable data, and the variable data is reflected as a relatively common variable data. When a certain variable data execution operation has not been monitored for a certain period of time, the variable data identification information of the variable data may be adjusted to reflect that the variable data is relatively less commonly used variable data. Alternatively, when a variable data execution operation has not been monitored for a certain period of time, the importance level identification information of the variable data may not be adjusted.

As an implementation manner, the importance degree identification information includes a weight, and the specific implementation process of step S102 may be: when the variable data execution operation is monitored, the weight of the variable data that performs the operation is increased by a preset value.

In this embodiment, various operations are not distinguished, and as long as the variable data execution operation is monitored, the weight of the variable data for performing the operation is increased by a preset value. For example, before the variable data execution operation is monitored, the weight of the variable data is A, and the preset value is B. When the variable data execution operation is monitored, the weight of the variable data that performs the operation is adjusted to A+B. The preset value can be set according to actual needs.

As another implementation manner, the importance degree identification information includes a weight, and the specific implementation process of step S102 may be: when monitoring the variable data execution operation, increasing the weight of the variable data that performs the operation according to the operation by a preset value .

In this embodiment, the operations are differentiated, for example, into at least two types of operations, and the preset values corresponding to each type of operation are different, and the operation with a larger preset value may be a relatively more important operation, and the preset value is Smaller operations can be an important level of operation.

For example, various operations on the variable data may be divided into a first operation and a second operation, wherein the first operation is more important than the second operation, so the preset value C1 corresponding to the first operation is greater than the pre-corresponding to the second operation. Set the value C2. When a variable data operation is detected, the type of the operation and the preset value corresponding to the type are detected, and then the weight of the variable data for performing the operation is increased by a corresponding preset value (for example, C1 or C2). .

By setting the kind of operation and the corresponding preset value, it is possible to take into account the importance of various operations, thereby increasing the probability that the variable data corresponding to the more important operation is inserted into the singly linked list in step S103.

In addition, the specific implementation process of step S102 may further be: when the variable data execution operation is monitored, the weight of the variable data that performs the operation is reduced by a preset value. At this time, the smaller the weight of the variable data, the more variable data is used; and the larger the weight of the variable data, the more variable data is relatively less common. When the variable data execution operation is monitored, the importance level identification information of the variable data on which the operation is performed may be adjusted to reduce the weight of the variable data on which the operation is to be performed.

In addition, the specific implementation process of step S102 may further be: when monitoring the variable data to perform an operation, reducing the weight of the variable data that performs the operation according to the operation by a preset value. For example, various operations on the variable data may be divided into a first operation and a second operation, wherein the first operation is more important than the second operation, so the preset value C1 corresponding to the first operation is smaller than the pre-corresponding to the second operation. Set the value C2. When a variable data operation is detected, the type of the operation and the preset value corresponding to the type are detected, and then the weight of the variable data that performs the operation is smaller than the corresponding preset value (for example, C1 or C2). .

Step S103, according to the adjusted importance degree identification information of each variable data, the variable data whose importance degree identification information satisfies the preset condition is inserted into the singly linked list by means of a header insertion manner; wherein the singly linked list has a preset length .

The importance level identification information may be a weight, and the preset condition may be the highest weight or the lowest weight.

Referring to FIG. 3, in an embodiment, step S103 can be implemented by the following process:

In step S301, the variable data with the largest weight among the variable data is acquired once every preset time.

The preset time can be set according to actual needs, for example, the variable data with the largest weight among the variable data is obtained every 30 seconds.

For example, the variable data of each variable data may be obtained by sorting each variable data according to the weight of each variable data.

In step S302, the variable data with the largest weight is inserted into the singly linked list by means of a header insertion manner.

Among them, the singly linked list has a fixed length, and when a new variable data is stored, the data at the end of the singly linked list is eliminated. Therefore, the variable data in the single necklace table is a relatively common variable. By directly calling the variable data from the singly linked list, the search can be reduced, the consumption of the program operation can be saved, and a certain number of js variables can be kept in the memory for the developer to use. It is not necessary to frequently access the prototype chain to obtain variables, which can greatly improve the performance of variable acquisition.

In addition, the variable data with the smallest weight among the individual variable data may be acquired once every preset time, and then the variable data with the smallest weight is inserted into the singly linked list by means of a header insertion manner.

The above data caching method stores each variable data in an ordered sequence, and each variable data is correspondingly set with a weight, monitors whether each variable data performs an operation, and performs a variable when the variable data execution operation is monitored. The weight of the data is adjusted. According to the weight of each adjusted variable data, the variable data whose weight meets the preset condition is inserted into the singly linked list through the header insertion mode, and the singly linked list has a fixed length and is stored in a new one. When the variable data is used, the data at the end of the singly linked list will be eliminated. Therefore, the variable data in the single necklace table is a commonly used variable. By directly calling the variable data from the singly linked list, the search can be reduced, and the consumption of the program is saved. It can maintain a certain number of js variables in memory for developers to use without having to access the prototype chain frequently to obtain variables, which can greatly improve the performance of variable acquisition.

It should be understood that the size of the sequence of the steps in the above embodiments does not mean that the order of execution is performed. The order of execution of each process should be determined by its function and internal logic, and should not be construed as limiting the implementation process of the embodiments of the present application.

Embodiment 2

Referring to FIG. 4, a flowchart of a data caching method provided in Embodiment 2 of the present application is shown in the following:

Step S401: If the type of the target variable data is Object, acquire an address of the target variable data, and calculate a hash code of the target variable data according to the address, where the target variable data is any one of each variable data. Variable data.

Step S402, if the type of the target variable data is String, acquire a character string included in the target variable data, and calculate a hash code of the target variable data according to the character string.

Step S403, if the type of the target variable data is Integer, acquire a number included in the target variable data, and calculate a hash code of the target variable data according to the number.

Step S404, after calculating the hash code of each variable data, storing the corresponding variable data into the ordered sequence according to the calculated hash code.

Step S405, it is monitored whether each variable data performs an operation, and when the variable data execution operation is monitored, the weight of the variable data that performs the operation is increased by a preset value.

Step S406: The variable data with the largest weight among the variable data is acquired once every preset time, and the variable data with the largest weight is inserted into the singly linked list by means of a header insertion manner.

In addition, step S405 may further be: monitoring whether each variable data performs an operation, and reducing a weight of the variable data of the performing operation to a preset value when the variable data execution operation is monitored. Correspondingly, step S406 may be: acquiring variable data with the smallest weight among the variable data once every preset time, and inserting the variable data with the smallest weight into the singly linked list by means of a header insertion manner.

Embodiment 3

Referring to FIG. 5, a flowchart of a data caching method provided in Embodiment 3 of the present application is shown in the following:

Step S501: If the type of the target variable data is Object, acquire an address of the target variable data, and calculate a hash code of the target variable data according to the address, where the target variable data is any one of the variable data. Variable data.

Step S502, if the type of the target variable data is String, acquire a character string included in the target variable data, and calculate a hash code of the target variable data according to the character string.

Step S503, if the type of the target variable data is Integer, acquire a number included in the target variable data, and calculate a hash code of the target variable data according to the number.

Step S504, after calculating the hash code of each variable data, storing the corresponding variable data into the ordered sequence according to the calculated hash code.

In step S505, it is monitored whether the respective variable data performs an operation, and when the variable data execution operation is monitored, the weight of the variable data that performs the operation is increased by a preset value according to the operation.

Step S506, the variable data with the largest weight among the variable data is acquired once every preset time, and the variable data with the largest weight is inserted into the singly linked list by means of a header insertion manner.

In addition, step S505 may further be: monitoring whether each variable data performs an operation, and when monitoring the variable data to perform an operation, reducing a weight of the variable data of the performing operation according to the operation by a preset value. Correspondingly, step S506 may be: acquiring variable data with the smallest weight among the variable data once every preset time, and inserting the variable data with the smallest weight into the singly linked list by means of a header insertion manner.

Embodiment 4

Corresponding to the data caching method described in the above embodiment, FIG. 6 is a schematic diagram showing the operating environment of the data caching program provided by the embodiment of the present application. For the convenience of explanation, only the parts related to the present embodiment are shown.

In the present embodiment, the data caching program 600 is installed and runs in the server 60. The server 60 can include, but is not limited to, a memory 601 and a processor 602. Figure 6 shows only server 60 with components 601-602, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.

The memory 601 may be an internal storage unit of the server 60, such as a hard disk or memory of the server 60, in some embodiments. The memory 601 may also be an external storage device of the server 60 in other embodiments, such as a plug-in hard disk equipped on the server 60, a smart memory card (SMC), and a secure digital number (Secure). Digital, SD) cards, flash cards, etc. Further, the memory 601 may also include both an internal storage unit of the server 60 and an external storage device. The memory 601 is configured to store application software and various types of data installed in the server 60, such as program codes of the data cache program 600, and the like. The memory 601 can also be used to temporarily store data that has been output or is about to be output.

The processor 602 can be a central processor (Central) in some embodiments A processing unit (CPU), a microprocessor or other data processing chip for running program code or processing data stored in the memory 601, such as executing the data caching program 600 and the like.

A display can also be included. The display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like in some embodiments. The display is for displaying information processed in the server 60 and a user interface for displaying visualizations, such as an application menu interface, an application icon interface, and the like. The components 601-62 of the server 60 communicate with one another via a system bus.

Please refer to FIG. 7 , which is a functional block diagram of a data caching program 600 provided by an embodiment of the present application. In this embodiment, the data caching program 600 may be divided into one or more modules, and the one or more modules are stored in the memory 601 and executed by one or more processors (this implementation) An example is performed by the processor 602) to complete the application. For example, in FIG. 7, the data cache program 600 can be divided into a variable storage module 701, a detection adjustment module 702, and a processing module 703. A module as referred to in this application refers to a series of computer readable instruction instructions that are capable of performing a particular function, and are more suitable than the program to describe the execution of the data caching program 600 in the server 60. The following description will specifically describe the functions of the modules 701-703.

The variable storage module 701 is configured to store each variable data in an ordered sequence, and each variable data is correspondingly set with a weight.

The detection adjustment module 702 is configured to monitor whether each variable data performs an operation, and adjust the weight of the variable data that performs the operation when the variable data execution operation is monitored.

The processing module 703 is configured to insert the variable data whose weight meets the preset condition into the singly linked list according to the weight of the adjusted variable data, wherein the singly linked list has a preset length.

Optionally, the variable storage module 701 may include a first calculation unit, a second calculation unit, a third calculation unit, and a storage unit.

The first calculating unit is configured to acquire an address of the target variable data if the type of the target variable data is Object, and calculate a hash code of the target variable data according to the address, where the target variable data is each Any variable data in the variable data.

The second calculating unit is configured to: if the type of the target variable data is a String, acquire a character string included in the target variable data, and calculate a hash code of the target variable data according to the character string.

The third calculating unit is configured to: if the type of the target variable data is Integer, acquire a number included in the target variable data, and calculate a hash code of the target variable data according to the number.

The storage unit is configured to store the corresponding variable data into the ordered sequence according to the calculated hash code after calculating the hash code of each variable data.

Optionally, the monitoring adjustment module 702 is specifically configured to:

When the variable data is detected to perform an operation, the weight of the variable data on which the operation is performed is increased by a preset value; or

When the variable data execution operation is monitored, the weight of the variable data on which the operation is performed is increased by a preset value according to the operation.

Optionally, the processing module 703 can include an obtaining unit and an inserting unit.

The obtaining unit is configured to acquire variable data with the largest weight among the variable data once every preset time.

The inserting unit is configured to insert the variable data with the largest weight into the singly linked list by means of a header insertion manner.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the division of each functional unit and module described above is exemplified. In practical applications, the above functions may be assigned to different functional units according to needs. The module is completed by dividing the internal structure of the device into different functional units or modules to perform all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be implemented by hardware. Formal implementation can also be implemented in the form of software functional units. In addition, the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application. For the specific working process of the unit and the module in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, and details are not described herein again.

In the above embodiments, the descriptions of the various embodiments are different, and the parts that are not detailed or described in the specific embodiments may be referred to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods to implement the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present application.

In the embodiments provided by the present application, it should be understood that the disclosed apparatus/server and method may be implemented in other manners. For example, the device/server embodiment described above is merely illustrative. For example, the division of the module or unit is only a logical function division, and the actual implementation may have another division manner, such as multiple units or Components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the processes in the foregoing embodiments, and may also be implemented by computer readable instructions, which may be stored in a computer readable storage medium. The computer readable instructions, when executed by a processor, may implement the steps of the various method embodiments described above. Wherein, the computer readable instructions comprise computer readable instruction code, which may be in the form of source code, an object code form, an executable file or some intermediate form or the like. The computer readable medium can include any entity or device capable of carrying the computer readable instruction code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only) Memory), random access memory (RAM, Random) Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media Does not include electrical carrier signals and telecommunication signals.

The above-mentioned embodiments are only used to explain the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still implement the foregoing embodiments. The technical solutions described in the examples are modified or equivalently replaced with some of the technical features; and the modifications or substitutions do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in Within the scope of protection of this application.

Claims

A data caching method, comprising:

Each variable data is stored in an ordered sequence, and each variable data is correspondingly provided with an importance degree identification information;

Monitoring whether each variable data performs an operation, and adjusting the importance degree identification information of the variable data of the execution operation when monitoring the variable data execution operation;

The variable data indicating that the importance degree identification information satisfies the preset condition is inserted into the singly linked list by means of the header insertion manner according to the importance degree identification information of the adjusted variable data; wherein the singly linked list has a preset length.
The data caching method according to claim 1, wherein the storing the variable data in the ordered sequence comprises:

If the type of the target variable data is Object, obtain an address of the target variable data, and calculate a hash code of the target variable data according to the address, where the target variable data is any variable data of each variable data;

If the type of the target variable data is String, acquiring a character string included in the target variable data, and calculating a hash code of the target variable data according to the character string;

If the type of the target variable data is Integer, acquiring a number included in the target variable data, and calculating a hash code of the target variable data according to the number;

After the hash code of each variable data is calculated, the corresponding hash data is stored in the ordered sequence according to the calculated hash code.
The data caching method according to claim 1, wherein the importance degree identification information comprises a weight; and when the variable data execution operation is monitored, adjusting the importance degree identification information of the variable data for performing the operation comprises:

When the variable data execution operation is monitored, the weight of the variable data that performs the operation is increased by a preset value.
The data caching method according to claim 1, wherein the importance degree identification information comprises a weight; and when the variable data execution operation is monitored, adjusting the weight of the variable data for performing the operation comprises:

When the variable data execution operation is monitored, the weight of the variable data on which the operation is performed is increased by a preset value according to the operation.
The data caching method according to any one of claims 1 to 4, wherein the importance degree identification information includes a weight; and the importance degree identification information is satisfied according to the adjusted importance degree identification information of each variable data. The variable data of the preset condition is inserted into the singly linked list by means of a header insertion method, including:

Obtaining the variable data with the largest weight among the individual variable data every preset time;

The variable data with the largest weight is inserted into the singly linked list by means of a header insertion method.
A data cache device, comprising:

a variable storage module, configured to store each variable data in an ordered sequence, and each variable data is correspondingly set with an importance degree identification information;

The adjustment module is configured to monitor whether each variable data performs an operation, and adjust the importance degree identification information of the variable data that performs the operation when the variable data execution operation is monitored;

The insertion module is configured to insert, according to the adjusted importance degree information of each variable data, the variable data whose importance degree identification information meets the preset condition is inserted into the singly linked list by means of a header insertion manner; wherein the singly linked list has a pre- Set the length.
The data cache device of claim 6, wherein the variable storage module comprises:

a first hash code calculation module, configured to acquire an address of the target variable data if the type of the target variable data is Object, and calculate a hash code of the target variable data according to the address, where the target variable data is Any one of the variable data;

a second hash code calculation module, if the type of the target variable data is a String, acquiring a character string included in the target variable data, and calculating a hash code of the target variable data according to the character string;

a third hash code calculation module, if the type of the target variable data is Integer, acquiring a number included in the target variable data, and calculating a hash code of the target variable data according to the number;

After the hash code of each variable data is calculated, the corresponding hash data is stored in the ordered sequence according to the calculated hash code.
The data cache device of claim 6, wherein the importance level identification information comprises a weight, and the adjustment module comprises:

The first weight increasing module is configured to increase a weight of the variable data that performs the operation by a preset value when the variable data is detected to perform an operation.
The data cache device of claim 6, wherein the importance level identification information comprises a weight, and the adjusting module further comprises:

And a second weight increasing module, configured to increase a weight of the variable data of the performing operation according to the operation to a preset value when the variable data is monitored.
The data cache device according to any one of claims 6 to 9, wherein the importance level identification information comprises a weight, and the inserting module comprises:

a variable obtaining module, configured to acquire, at a preset time, variable data having the largest weight among the variable data;

The variable insertion module is configured to insert the variable data with the largest weight into the singly linked list by means of a header insertion manner.
A server, characterized in that the data cache processing server comprises a memory, a processor on which is stored computer readable instructions executable on the processor, the processor executing the computer readable The following steps are implemented when the instruction is executed:

Each variable data is stored in an ordered sequence, and each variable data is correspondingly provided with an importance degree identification information;

Monitoring whether each variable data performs an operation, and adjusting the importance degree identification information of the variable data of the execution operation when monitoring the variable data execution operation;

The variable data indicating that the importance degree identification information satisfies the preset condition is inserted into the singly linked list by means of the header insertion manner according to the importance degree identification information of the adjusted variable data; wherein the singly linked list has a preset length.
The server according to claim 11, wherein said storing said variable data in an ordered sequence comprises:

If the type of the target variable data is Object, obtain an address of the target variable data, and calculate a hash code of the target variable data according to the address, where the target variable data is any variable data of each variable data;

If the type of the target variable data is String, acquiring a character string included in the target variable data, and calculating a hash code of the target variable data according to the character string;

If the type of the target variable data is Integer, acquiring a number included in the target variable data, and calculating a hash code of the target variable data according to the number;

After the hash code of each variable data is calculated, the corresponding hash data is stored in the ordered sequence according to the calculated hash code.
The server according to claim 11, wherein the importance degree identification information includes a weight; and when monitoring the variable data execution operation, adjusting the importance degree identification information of the variable data for performing the operation comprises:

When the variable data execution operation is monitored, the weight of the variable data that performs the operation is increased by a preset value.
The server according to claim 11, wherein the importance degree identification information comprises a weight; and when the variable data execution operation is monitored, adjusting the weight of the variable data for performing the operation comprises:

When the variable data execution operation is monitored, the weight of the variable data on which the operation is performed is increased by a preset value according to the operation.
The server according to any one of claims 11 to 14, wherein the importance degree identification information includes a weight; and the importance degree identification information satisfies a preset according to the adjusted importance degree identification information of each variable data. Conditional variable data is inserted into the singly linked list by means of header insertion:

Obtaining the variable data with the largest weight among the individual variable data every preset time;

The variable data with the largest weight is inserted into the singly linked list by means of a header insertion method.
A computer readable storage medium storing computer readable instructions, wherein the computer readable instructions, when executed by at least one processor, implement the following steps:

Each variable data is stored in an ordered sequence, and each variable data is correspondingly provided with an importance degree identification information;

Monitoring whether each variable data performs an operation, and adjusting the importance degree identification information of the variable data of the execution operation when monitoring the variable data execution operation;

The variable data indicating that the importance degree identification information satisfies the preset condition is inserted into the singly linked list by means of the header insertion manner according to the importance degree identification information of the adjusted variable data; wherein the singly linked list has a preset length.
The computer readable storage medium of claim 16 wherein said storing said variable data in an ordered sequence comprises:

If the type of the target variable data is Object, obtain an address of the target variable data, and calculate a hash code of the target variable data according to the address, where the target variable data is any variable data of each variable data;

If the type of the target variable data is String, acquiring a character string included in the target variable data, and calculating a hash code of the target variable data according to the character string;

If the type of the target variable data is Integer, acquiring a number included in the target variable data, and calculating a hash code of the target variable data according to the number;

After the hash code of each variable data is calculated, the corresponding hash data is stored in the ordered sequence according to the calculated hash code.
The computer readable storage medium according to claim 16, wherein the importance degree identification information comprises a weight; and the monitoring of the change of the importance level identification information of the variable data of the operation is performed when the variable data execution operation is monitored include:

When the variable data execution operation is monitored, the weight of the variable data that performs the operation is increased by a preset value.
The computer readable storage medium according to claim 16, wherein the importance degree identification information comprises a weight; and when the variable data execution operation is monitored, adjusting the weight of the variable data for performing the operation comprises:

When the variable data execution operation is monitored, the weight of the variable data on which the operation is performed is increased by a preset value according to the operation.
The computer readable storage medium according to any one of claims 16 to 19, wherein the importance degree identification information comprises a weight; the importance level identification information according to the adjusted importance level data of each variable data The variable data whose information meets the preset condition is inserted into the singly linked list through the header insertion method, including:

Obtaining the variable data with the largest weight among the individual variable data every preset time;

The variable data with the largest weight is inserted into the singly linked list by means of a header insertion method.