WO2022217987A1 - Data table heat differentiation method and apparatus, and related device - Google Patents

Abstract

A data table heat differentiation method and apparatus, and a related device. The method comprises: a service node obtains, from a storage node, a second data table associated with a first data table, then obtains an associated heat between the first data table and the second data table according to the second data table, and after obtaining the associated heat between the first data table and the second data table, determines the heat of the first data table according to the associated heat between the first data table and the second data table, the associated heat between the first data table and the second data table being obtained according to the inherent heat of the second data table and the association between the first data table and the second data table. Said method can improve the accuracy of data table heat differentiation.

Description

Data sheet heat discrimination method, device and related equipment

This application claims the priority of the Chinese patent application filed on April 12, 2021 with the application number 202110389324.9 and the invention titled "Data Sheet Heat Discrimination Method, Apparatus and Related Equipment", the entire contents of which are incorporated by reference in in this application.

technical field

The present application relates to the field of big data, and in particular, to a method, device and related equipment for distinguishing the heat of a data table.

Background technique

In the era of big data, data grows explosively, and the number of data tables becomes larger and larger with the growth of data. In order to improve the use efficiency of data tables, it is necessary to distinguish the heat of a large number of data tables, and manage a large number of data tables according to the heat of the data tables, such as cleaning the data tables with low heat or putting the data tables with high heat to the top, etc.

However, the existing methods for distinguishing data table heat have the problem of low accuracy in distinguishing data table heat.

SUMMARY OF THE INVENTION

The present application provides a method, device and related equipment for distinguishing the heat of a data table, which can improve the accuracy of distinguishing the heat of a data table.

In a first aspect, a method for distinguishing data table heat is provided, and the method includes:

The service node obtains a second data table associated with the first data table from a storage node, where the storage node stores a plurality of data tables;

The service node acquires the associated heat of the first data table and the second data table, wherein the associated heat of the first data table and the second data table is based on the inherent heat of the second data table And the association relationship between the first data table and the second data table is obtained, and the inherent heat of the second data table is the heat generated by the second data table being called;

The service node determines the popularity of the first data table according to the relative popularity of the first data table and the second data table.

In the above solution, when calculating the heat of the first data table, the heat brought by the second data table having an associated relationship with the first data table is introduced to the first data table, that is, the difference between the first data table and the second data table. Therefore, the calculated popularity of the first data table can be improved to be more accurate, and when the popularity of multiple data tables is acquired, the popularity of multiple data tables can be better distinguished.

In a possible implementation manner, the service node acquires the second data table associated with the first data table from the storage node, including:

The service node obtains, from the storage node, the second data table having a data blood relationship with the first data table, wherein the data blood relationship indicates that the second data table is based on the first data table Calculated, or, the first data table is calculated according to the second data table;

The service node obtains the correlation heat between the first data table and the second data table, including:

The service node calculates the correlation degree of the first data table and the second data table according to the data blood relationship between the first data table and the second data table.

In a possible implementation manner, the service node obtains the second data table associated with the first data table from the storage node, including:

The service node acquires, from the storage node, the second data table having a primary and foreign key association relationship with the first data table, wherein the primary and foreign key association relationship represents one of the first data tables Or multiple fields are referenced as the primary key of the second data table, or, one or more fields in the second data table are referenced as the primary key of the first data table;

The service node obtains the correlation heat between the first data table and the second data table, including:

The service node calculates the association heat between the first data table and the second data table according to the primary and foreign key association relationship between the first data table and the second data table.

In a possible implementation manner, the service node determines the popularity of the first data table according to the correlation between the first data table and the second data table, including:

The service node determines the heatness of the first data table according to the inherent heatness of the first data table and the associated heatness of the first data table and the second data table, wherein the first data table The inherent heat is the heat generated by the first data table being called.

In a possible implementation, the method further includes:

the service node calculates the heatness of the plurality of data tables;

The service node deletes, from the storage node according to the calculation result, data tables whose heat is less than a first preset threshold.

In the above solution, the service node deletes the data table with low heat from the storage node according to the calculation result, which can save storage space.

In a possible implementation, the method further includes:

the service node calculates the heatness of the plurality of data tables;

The service node adjusts, according to the calculation result, a position on the display interface of a data table whose heat is greater than the second preset threshold in the plurality of data tables to be in front of a data table whose heat is less than the second preset threshold.

In the above solution, the service node adjusts the position of the data table with high popularity on the display interface to the front of the data table with low popularity, so that the user can view the data table with high popularity conveniently and quickly.

In a possible implementation, the method further includes:

the service node calculates the heatness of the plurality of data tables;

The service node migrates, according to the calculation result, data tables whose heat is less than a third preset threshold to a first storage device, where the storage performance of the first storage device is lower than that of the storage node.

In the above solution, the service node migrates the data table with low heat to the first storage device whose storage performance is lower than that of the storage node, which can not only prevent the data table with low heat from continuing to occupy the resources of the storage node, but also when users need to view this part of the data table in the future. Also found from the first storage device.

In a possible implementation, the method further includes:

the service node calculates the heatness of the plurality of data tables;

The service node migrates, according to the calculation result, a data table whose heat is greater than a fourth preset threshold to a second storage device, where the storage performance of the second storage device is higher than that of the storage node.

In the above solution, the service node migrates the hot data table to the second storage device with higher storage performance than the storage node, which can improve the efficiency of operating data in the hot data table and improve the storage security of the hot data table. sex.

In a second aspect, a data table heat discrimination device is provided, the device is applied to a service node, and the device includes:

an obtaining module, configured to obtain a second data table associated with the first data table from a storage node, where the storage node stores a plurality of data tables;

A processing module, configured to obtain the correlation degree of the first data table and the second data table, wherein the correlation degree of the first data table and the second data table is based on the inherent characteristics of the second data table The heat and the association relationship between the first data table and the second data table are obtained, and the inherent heat of the second data table is the heat generated by the second data table being called;

The processing module is configured to determine the popularity of the first data table according to the correlation between the first data table and the second data table.

In a possible implementation manner, the obtaining module is specifically used for:

Acquire the second data table having a data blood relationship with the first data table from the storage node, wherein the data blood relationship indicates that the second data table is calculated according to the first data table, or , the first data table is calculated according to the second data table;

The processing module is specifically used for:

According to the data blood relationship between the first data table and the second data table, the correlation degree of the first data table and the second data table is calculated.

In a possible implementation manner, the obtaining module is specifically used for:

Acquire the second data table having a primary-foreign key association relationship with the first data table from the storage node, wherein the primary-foreign key association relationship represents one or more fields in the first data table be referenced as the primary key of the second data table, or, one or more fields in the second data table are referenced as the primary key of the first data table;

The processing module is specifically used for:

According to the primary and foreign key association relationship between the first data table and the second data table, the association degree of the first data table and the second data table is calculated.

In a possible implementation manner, the processing module is specifically used for:

The heatness of the first data table is determined according to the inherent heatness of the first data table and the correlation heatness of the first data table and the second data table, wherein the inherent heatness of the first data table is all The heat generated when the first data table is called.

In a possible implementation manner, the processing module is further configured to:

calculating the popularity of the plurality of data tables;

According to the calculation result, delete the data table whose heat is less than the first preset threshold from the storage node.

In a possible implementation manner, the processing module is further configured to:

calculating the popularity of the plurality of data tables;

According to the calculation result, the position on the display interface of the data table whose heat is greater than the second preset threshold among the plurality of data tables is adjusted to be in front of the data table whose heat is less than the second preset threshold.

In a possible implementation manner, the processing module is further configured to:

calculating the popularity of the plurality of data tables;

According to the calculation result, the data tables whose heat is less than the third preset threshold are migrated to the first storage device, and the data tables whose heat is greater than the fourth preset threshold are migrated to the second storage device, wherein the storage of the first storage device The performance of the second storage device is lower than that of the storage node, and the storage performance of the second storage device is higher than that of the storage node.

In a third aspect, a non-transitory computer-readable storage medium is provided, and the non-transitory computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed, the first method described above is executed. Aspect or a method described in any specific implementation of the first aspect.

In a fourth aspect, a computer program product is provided, including a computer program, when the computer program is read and executed by a cluster of computer devices, the cluster of computer devices is made to execute the first aspect or any specific implementation of the first aspect. The method described in the implementation.

In a fifth aspect, a computing device cluster is provided, including at least one computing device, each computing device including a processor and a memory; the processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, so that the The computing device performs the method as described in the above first aspect or any specific implementation of the first aspect.

In a possible implementation manner, the computing device cluster includes a computing device, and the computing device includes a processor and a memory; the processor is configured to execute instructions stored in the memory, so that the computing device performs the first aspect or A method provided by any possible implementation manner of the first aspect.

In a possible implementation manner, the computing device cluster includes at least two computing devices, and each computing device includes a processor and a memory; the processors of the at least two computing devices are used to execute the memory of the at least two computing devices. Stored instructions to cause the computing device cluster to perform the method as provided by the first aspect or any possible implementation of the first aspect.

Description of drawings

1 is a schematic diagram of an application scenario involved in an embodiment of the present application;

2 is a schematic diagram of a data blood relationship involved in an embodiment of the present application;

3 is a schematic diagram of a primary-foreign key association relationship involved in an embodiment of the present application;

FIG. 4 is a schematic flowchart of a method for distinguishing the heat of a data table provided by an embodiment of the present application;

5 is a schematic diagram of a data blood relationship of a first data table provided by an embodiment of the present application;

6 is a schematic flowchart of another data table heat discrimination method provided by an embodiment of the present application;

7 is a schematic diagram of a primary and foreign key association relationship of a first data table provided by an embodiment of the present application;

8 is a schematic structural diagram of a data processing system provided by an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a computing device cluster provided by an embodiment of the present application;

FIG. 10 is a schematic structural diagram of a computing device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the present application will be described below with reference to the accompanying drawings.

The terms "first" and "second" in the embodiments of the present application are only used for the purpose of description, and cannot be understood as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature defined as "first" or "second" may expressly or implicitly include one or more of that feature.

In the embodiments of the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or", which describes the association relationship of the associated objects, indicates that there can be three kinds of relationships, for example, A and/or B, which can indicate: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A, B can be singular or plural. The character "/" generally indicates that the associated objects are an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of a single item(s) or a plurality of items(s). For example, at least one (a) of a, b or c may represent: a, b, c, a-b, a-c, b-c or a-b-c, wherein a, b, c may be single or multiple.

Any embodiment or design described in this application as "exemplary" or "such as" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present the related concepts in a specific manner.

In order to facilitate understanding of the embodiments of the present application, concepts, terms and the like involved in the embodiments of the present application are first introduced below.

(1) Transactional data, also known as transactional data, business data, etc., describe the internal or external events or transaction records in the business operation process of the organization, such as sales orders, call records, etc.

(2) Data popularity, a value used to reflect the degree of attention to the data. This value also indicates the possibility of the data being accessed within a certain period of time from the current time. If the data popularity is large, it indicates that the data has a high degree of attention, indicating that the data has received a high degree of attention. The data has a high possibility of being accessed in the current period of time, and the data popularity is small, indicating that the data has a low degree of attention, indicating that the possibility of the data being accessed in the current period of time is very small.

(3) Data table popularity, a value used to reflect the degree of attention of the data table. This value indicates the possibility of the data table being accessed for a period of time from the current beginning. If the data table is hot, it means that the data table has a high degree of attention. , indicating that the data table is very likely to be accessed for a period of time from the current time, and the data table is less popular, indicating that the data table has a low degree of attention, indicating that the data table is very likely to be accessed for a period of time from the current beginning. Small.

(4) The inherent heat of the data table, the heat generated by the data table itself being called, the heat can be determined according to the number of times the data table is called (also called the number of times of use or the number of visits), usually, the inherent heat of the data table The heat is equal to the number of times the data table is called, where the number of times the data table is called includes the number of times of querying (select) data, adding (insert) data, deleting (deleting) data, and modifying (update) The times of data etc., that is, the number of times the data table is called = the number of times the data is queried in the data table + the number of times the data is added in the data table + the number of times the data is deleted in the data table + the number of times the data is modified in the data table, in the data table If the calling operation also includes other operations, the number of times the data table is called also includes the number of other data operations performed in the data table.

The following briefly describes the application scenarios involved in the embodiments of the present application.

With the rapid development of Internet technology, users of various website platforms have grown rapidly, and the amount of data to be processed has also increased exponentially. There are many types of data, and the data is very complex. And new data is generated and aggregated into an ocean of data. It is understandable that in the process of rapid data growth, tens of thousands or even millions of data tables will be deposited. As shown in Figure 1, some of the large number of data tables stored by storage nodes are temporary or stale tables. , which is rarely called and should be cleaned up. Some tables are frequently called and should be paid attention to to improve the efficiency of data table usage and save storage resources. Therefore, the management of a large number of data tables has become one of the important issues that enterprises pay attention to. Among them, distinguishing the popularity of a large number of data tables is a key part of the enterprise's management of a large number of data tables.

At present, when managing a large number of data tables, the following two methods are usually used to distinguish the popularity of data tables: (1) based on the data creation time, (2) based on the inherent popularity of the data table, among which,

The method based on data creation time is mainly used to distinguish the popularity of transaction data tables (that is, tables that mainly include transaction data). Specifically, assuming that the storage node stores transaction data table A and transaction data table B, the data in transaction data table A is Created in the last year, the data in transaction data table B was created one year ago. After obtaining transaction data table A and transaction data table B from the storage node, the service node obtains the creation time of the data in transaction data table A and The creation time of the data in transaction data table B is compared. When it is determined that most or all of the data in transaction data table A are created later than the data in transaction data table B, the transaction data table will be determined. The heat of A is greater than the heat of transaction data table B, otherwise, it is determined that the heat of transaction data table A is less than the heat of transaction data table B.

It can be understood that in practical application scenarios, it is very likely that the following situations exist: although most or all of the data in transaction data table A are created later than the data in transaction data table B, but in transaction data table B The data of the transaction data table is more important than the data in the transaction data table A, and the call of the transaction data table B is more frequent than the call of the transaction data table A, that is to say, the heat of the transaction data table B is actually greater than that of the transaction data table A. hot.

In the above possible cases, the service node then distinguishes the heat of the two transaction data tables according to the above method based on the data creation time, and the obtained heat distinction result is obviously inaccurate and inconsistent with the actual application scenario.

The inherent heat method based on the data table is mainly used to distinguish the heat of the webpage data table (that is, the table mainly including webpage data (such as articles, pictures, videos, etc. published on the webpage), specifically, it is assumed that the storage node stores webpage data. Table A and webpage data table B. After obtaining webpage data table A and webpage data table B from the storage node, the service node obtains the inherent heat of webpage data table A and the inherent heat of webpage data table B and compares them. When the inherent popularity of data sheet A is greater than that of webpage data sheet B, it will be determined that the popularity of webpage data sheet A is greater than that of webpage data sheet B; otherwise, it is determined that the popularity of webpage data sheet A is lower than that of webpage data sheet B.

It can be understood that in practical application scenarios, it is very likely that the following situations exist: although the inherent popularity of webpage data table A is greater than that of webpage data table B, the data in webpage data table A was created in the last year, and the webpage data The data in Table B was created one year ago. The creation time of the web page data can be understood as the time when the web page data was published on the web page. Generally speaking, the number of times the newly published data on the web page is accessed is less than that of a long time ago. The data that has already been published on the webpage, but it does not mean that the popularity of the newly released data on the webpage is less than that of the data that has been published on the webpage a long time ago, that is to say, in fact, the webpage data table A is more popular than the webpage. Data Sheet B of the heat.

In the case of the above possible existence, the service node distinguishes the popularity of the two web page data tables according to the above method based on the inherent popularity of the data table, and the obtained popularity distinction result is obviously inaccurate and inconsistent with the actual application scenario.

It can be seen that the above two methods have the problem that the accuracy of distinguishing the heat of the data table is low and inconsistent with the actual application scenario.

In view of the above problems, the embodiments of the present application provide a method, device, and related equipment for distinguishing the heat of a data table, which can improve the accuracy of distinguishing the heat of a data table and are more in line with practical application scenarios.

Before introducing the method, device, and related equipment for distinguishing data table popularity provided by the embodiments of the present application, concepts such as the association relationship of data tables, the correlation degree of data tables involved in the embodiments of the present application, and the process of acquiring the inherent popularity of data tables are introduced. .

(1) Data table association, specifically including data blood relationship and primary and foreign key association, among which,

Data blood relationship, also known as data lineage relationship, data origin relationship and data lineage relationship, etc., refers to a relationship that will be formed between data tables in the process of generation, fusion, transformation, circulation and death of data tables . As shown in Figure 2, assuming that the original data is stored in data table 1, after calculating some or all of the original data in data table 1, an intermediate table including intermediate data (ie, some or all of the calculated original data) is obtained. 2. After calculating the intermediate data from data table 1 in intermediate table 2, data table 3 including final data is formed. At this time, the data link from data table 1 to data table 2 to data table 3 is Indicates the data blood relationship of these three tables. Specifically, it can be said that data table 1 and data table 2 have a direct blood relationship, data table 2 and data table 3 have a direct blood relationship, and data table 1 and data table 3 have an indirect blood relationship. By analyzing the data blood relationship between the data tables, the migration and circulation of the data tables can be clearly understood, which provides a basis for the evaluation of the value of the data tables and the management of the data tables.

In the case of the data blood relationship between data table 1, data table 2 and data table 3 as shown in Figure 2, it can be seen that data table 2 directly depends on data table 1, and data table 3 directly depends on data table 2 , indirectly dependent on Data Table 1. It can be understood that if the data used to calculate data table 2 and data table 3 in data table 1 is accessed, it means that data table 2 and data table 3 are indirectly accessed, that is, data table 1 is to a certain extent. It can improve the popularity of data table 2 and data table 3; if the data from data table 1 in data table 2 is accessed, it means that data table 1 and data table 3 are indirectly accessed, that is to say, To a certain extent, data table 2 can improve the popularity of data table 1 and data table 3; if the data from data table 2 in data table 3 is accessed, it means that data table 1 and data table 2 are indirectly Accessed, that is to say, Data Sheet 3 has an effect on the popularity of Data Sheet 1 and the popularity of Data Sheet 2 to a certain extent.

Therefore, it can be understood that when determining the popularity of data table 1, the popularity of data table 2, and the popularity of data table 3, if in addition to considering the inherent popularity of each data table, each data table has a data blood relationship with it ( Taking into account the increased popularity of other data tables (including direct blood relationship and indirect blood relationship), the determined popularity of each data table will be more accurate and can better highlight the importance of each data table.

The primary key-foreign key relationship defines a relationship between two tables in a relational database. As shown in Figure 3, one or more fields A1 in data table 1 are Reference is made as the primary key of data table 2', at this time, the field A1 in data table 1 is said to be a foreign key pointing to data table 2', and data table 1 and data table 2' have a primary-foreign key association relationship.

As shown in Figure 3, the primary key of data table 2' is also referenced as the primary key of data table 3'. At this time, it is said that data table 1 and data table 3' also have a primary and foreign key association relationship. In order to distinguish and describe, The primary and foreign key associations between data table 1 and data table 2' and the primary and foreign key associations between data table 2' and data table 3' are called direct primary and foreign key associations, and data table 2' and data The primary and foreign key associations between tables 3' are indirect primary and foreign key associations.

In the case of the primary and foreign key associations between data table 1, data table 2', and data table 3' as shown in Figure 3, it can be seen that data table 2' directly depends on data table 1, data table 3' Direct dependency on Data Sheet 2' and indirect dependency on Data Sheet 1. It can be understood that if the field A1 in data table 1 is accessed, it means that data table 2 and data table 3 are indirectly accessed, that is to say, data table 1 has a certain degree of interest in data table 2 and data table 3. If the primary key of data table 2' is accessed, it means that data table 1 and data table 3' are indirectly accessed, that is to say, data table 2' has a certain degree of influence on data table 1 and data table 1 and data table 3'. The popularity of table 3' has an effect of improving; if the primary key of data table 3' is accessed, it means that data table 1 and data table 2' are indirectly accessed, that is to say, data table 3' has a certain degree of influence on data table 1 and Data Sheet 2' heat up.

Therefore, it can be understood that when determining the popularity of data table 1, the popularity of data table 2', and the popularity of data table 3', if in addition to considering the inherent popularity of each data table, each data table has its main external The increased popularity of other data tables of key associations (including direct primary and foreign key associations and indirect primary and foreign key associations) is also taken into account, and the determined popularity of each data table will be more accurate, which can better highlight each data table. Importance of data sheets.

(2) The relevance of the data table, which refers to the heat brought by the associated data table to the associated data table, such as the above-mentioned data table 1 due to the data table 2 and/or data table 3 that has a data blood relationship with it. Data table 1 has increased popularity due to data table 2' and/or data table 3' having a primary and foreign key relationship with it.

(3) The process of acquiring the inherent heat of the data sheet:

Taking the service node to obtain the inherent heat of data table 1 as an example, the process includes but is not limited to the following steps:

A1. The service node obtains the log information of the data operation of the data table 1 from the storage node, and obtains the information of the data operation of the data table 1 according to the log information of the data operation of the data table 1.

Among them, the log information of the data operation of the data table 1 indicates that there is log information about the data operation performed by the user that is automatically recorded by the storage node when the user performs data operations on the data table 1, and the log information includes the user's data operation on the data table 1. Information about the data operations performed, such as the type of data operations performed on Data Table 1 (such as deleting data, adding data, etc.) and the time of data operations on Data Table 1. Therefore, according to the data in Table 1 Operation log information Get information about data operation of data table 1.

In a specific implementation, the service node can obtain the log information of the data table 1 within a preset time period from the storage node, and then obtain the data operation information of the data table 1 within the preset time period according to the log information, for example, the service node You can obtain the log information of data table 1 in 2020, and then obtain the information of data operation of data table 1 in 2020 according to the log information of data table 1 in 2020.

A2. The service node determines the number of times the data table 1 is called according to the data operation information of the data table 1.

Specifically, the number of times of querying data in data table 1, the number of times of adding data in data table 1, the number of times of deleting data in data table 1, and the number of times of deleting data in data table 1 and The number of times of modifying the data, etc., and then summing the above times can determine the number of times the data table 1 is called.

A3. Determine the inherent heat of data table 1 according to the number of times data table 1 is called.

In a specific embodiment, the inherent popularity of the data table 1 = the number of times the data table 1 is called.

The following will continue to introduce the method, apparatus, and related equipment for distinguishing the heat of a data table provided by the embodiments of the present application. In the method, apparatus, and related equipment for distinguishing the heat of a data table improved by the embodiments of the present application, the service node can obtain and first data from the storage node. The second data table associated with the table, and then obtain the correlation heat of the first data table and the second data table according to the correlation relationship between the first data table and the second data table and the inherent heat of the second data table, after the first data table is obtained. After the correlation between the table and the second data table, the popularity of the first data table is determined according to the correlation, wherein the correlation between the first data table and the second data table includes a data blood relationship and a primary and foreign key correlation. species or multiple species.

A method for distinguishing the heat of a data table provided by the embodiment of the present application is described in more detail below with reference to FIG. 4 . As shown in FIG. 4 , the method for distinguishing the heat of a data table provided by the embodiment of the present application includes but is not limited to the following steps:

S101. The service node acquires a first data table and a second data table having a data blood relationship with the first data table from a storage node.

The storage node stores multiple data tables, and the first data table may be any one or more data tables among the multiple data tables stored by the storage node. The multiple data tables stored by the storage node can be various types of tables such as transaction data tables and web page data tables. Tables belonging to any database, not specifically limited here.

As can be seen from the above introduction to the data blood relationship, the data blood relationship between the first data table and the second data table means that the second data table is calculated according to the first data table, and/or, the first data table is based on the second data table. Calculated from the data sheet. Specifically, the data blood relationship between the first data table and the second data table may be a direct blood relationship or an indirect blood relationship, which is not specifically limited here.

In a specific implementation, after obtaining the first data table, the service node can obtain the second data table that has a data blood relationship with the first data table from the storage node through a data warehouse tool (such as hive) or a SQL statement, wherein hive It is a data warehouse tool based on Hadoop for data extraction, transformation and loading. It is a mechanism for storing, querying and analyzing large-scale data stored in Hadoop.

It should be noted that the service node obtains the second data table having a data blood relationship with the first data table from the storage node through the data warehouse tool or the SQL statement, which is only an example and should not be regarded as a specific limitation. In a specific implementation, the service node can also obtain the second data table that has a data blood relationship with the first data table in other ways, such as manually reading the code to find the second data table that has a data blood relationship with the first data table, and the service node Receive the manually input name of the second data table that has a data blood relationship with the first data table, and then acquire the second data table according to the manually input name of the second data table.

S102. The service node acquires the inherent heat H ₀ of the first data table.

Wherein, the inherent heat H ₀ of the first data table is the heat generated by the first data table itself being called.

S103 , the service node calculates the correlation heat H ₁ of the first data table and the second data table according to the data blood relationship between the first data table and the second data table and the inherent heat of the second data table.

Among them, the inherent heat of the second data table is the heat generated by the second data table itself being called.

Specifically, after acquiring the second data table that has a data blood relationship with the first data table, the service node can determine the blood relationship weight corresponding to the second data table according to the data blood relationship between the first data table and the second data table, and Calculate the inherent heat of the second data table, and then calculate the associated heat H ₁ of the first data table and the second data table according to the blood relationship weight corresponding to the second data table and the inherent heat of the second data table.

For example, as shown in FIG. 5 , it is assumed that there are two second data tables that have a data blood relationship with the first data table, namely data table A and data table B, wherein the second data table A and the first data table There is a direct blood relationship, and the second data table B has an indirect blood relationship with the first data table. Assuming that the inherent heat of the second data table A is H _0,A , the inherent heat of the second data table B is H _0,B , The blood relationship weight corresponding to the second data table A is W _A , and the blood relationship weight corresponding to the second data table B is W _B , then the correlation heat H ₁ of the first data table and the second data tables A and B obtained by the service node is:

H ₁ =W _A *H _0,A +W _B *H _0,B

Among them, W _A and W _B are both numbers greater than 0 and less than 1. Considering that the second data table A has a direct blood relationship with the first data table, and the second data table B has an indirect blood relationship with the first data table, the first data table A has an indirect blood relationship with the first data table. The relationship between the second data table A and the first data table is closer, preferably, W _A is greater than W _B .

S104. The service node determines the heat H 0 of the first data table according to the inherent heat H ₀ of the first data table and the associated heat H ₁ of the first data table and the second data table.

In a specific embodiment of the present application, H=H ₀ +H ₁ .

It should be noted that, for the sake of simplicity, the embodiments of this application do not describe the process of acquiring the intrinsic heat H ₀ of the first data table and the process of acquiring the intrinsic heat of the second data table. For details, please refer to the data described above. The acquisition process of the inherent heat in Table 1 will not be repeated here.

Please refer to FIG. 6. FIG. 6 is a schematic flowchart of another method for distinguishing the heat of a data table provided by an embodiment of the present application. As shown in FIG. 6, the method for distinguishing the heat of a data table provided by an embodiment of the present application includes but is not limited to the following steps:

S201. The service node obtains a first data table and a second data table having a primary and foreign key association relationship with the first data table from a storage node.

From the above description of the primary and foreign key associations, it can be seen that there is a primary and foreign key association between the first data table and the second data table, which means that one or more fields in the first data table are referenced as the second data table. The primary key, and/or, one or more fields in the second data table are referenced as the primary key of the first data table.

In a specific implementation, after obtaining the first data table, the service node may obtain the second data table having a primary and foreign key association relationship with the first data table from the storage node through a data warehouse tool or a SQL statement.

It should be noted that the service node obtains the second data table with the primary and foreign key association relationship in the first data table from the storage node through the data warehouse tool or the SQL statement, which is only an example. In a specific implementation, the service node can also obtain the second data table that has a primary and foreign key relationship with the first data table in other ways, such as manually reading the code to find the second data that has a primary and foreign key relationship with the first data table. table, the service node receives the manually input name of the second data table that has a primary foreign key relationship with the first data table, and then obtains the second data table according to the manually input name of the second data table.

S202. The service node acquires the inherent heat H ₀ of the first data table.

S203. The service node calculates the association heat H ₁ of the first data table and the second data table according to the primary and foreign key association relationship between the first data table and the second data table and the inherent heat of the second data table.

Specifically, after acquiring the second data table that has the primary and foreign key association relationship with the first data table, the service node can determine the corresponding data table according to the primary and foreign key association relationship between the first data table and the second data table. association weight, and calculating the inherent heat of the second data table, and then calculating the association heat H ₁ of the first data table and the second data table according to the association weight corresponding to the second data table and the inherent heat of the second data table.

For example, as shown in FIG. 7 , it is assumed that there are two second data tables with a primary foreign key association relationship with the first data table, namely data table C and data table D, wherein the second data table C and the first data table The data table has a direct primary and foreign key relationship, and the second data table D has an indirect primary and foreign key relationship with the first data table. It is assumed that the inherent heat of the second data table C is H _0,C , and the inherent heat of the second data table D is H 0,C . The heat is H _0,D , the correlation weight corresponding to the second data table _C is WC , and the correlation weight corresponding to the second data table _D is WD , then the first data table and the second data table C, D obtained by the service node The associated heat H ₁ is:

H ₁ =W _C *H _0,C +W _D *H _0,D

Wherein, both W _C and W _D are numbers greater than 0 and less than 1. Considering that the second data table C and the first data table have a direct primary and foreign key association relationship, the second data table D and the first data table have an indirect primary and foreign key relationship. In the foreign key association relationship, the relationship between the second data table C and the first data table is closer, preferably, W _C is greater than W _D .

S204. The service node determines the heat H 0 of the first data table according to the inherent heat H ₀ of the first data table and the associated heat H ₁ of the first data table and the second data table.

In a specific embodiment of the present application, H=H ₀ +H ₁ .

It can be understood that when the service node obtains the second data table that has an associated relationship with the first data table from the storage node, if it not only obtains the second data table that has a data blood relationship with the first data table, but also obtains the second data table that is related to the first data table. The data table has a second data table with a primary and foreign key association relationship, then the correlation H1 between the _first data table and the second data table calculated by the service node includes not only the second data table that has a data blood relationship with the first data table. The heat brought by it also includes the heat brought by the second data table that has a primary and foreign key association relationship with the first data table.

Continuing to take the examples of FIG. 5 and FIG. 7 mentioned above as an example, assuming that the first data table has both the data blood relationship shown in FIG. 5 and the primary and foreign key association shown in FIG. The associated heat H ₁ of the first data table and the second data table is:

H ₁ =W _A *H _0,A +W _B *H _0,B +W _C *H _0,C +W _D *H _0,D

Since the heat H of the first data table = the inherent heat H ₀ of the first data table + the associated heat H ₁ of the first data table and the second data table, it can be understood that in the first data table and the second data table The association heat H ₁ includes not only the heat brought by the second data table that has a data blood relationship with the first data table, but also the heat brought by the second data table that has a primary and foreign key association relationship with the first data table. , the heat H of the first data table calculated by the service node not only includes the heat brought by the second data table that has a data blood relationship with the first data table, but also includes the first data table that has a primary and foreign key association relationship with the first data table. The heat brought by the data sheet.

It can be understood that the service node can obtain the heat of multiple data tables according to the data table heat discrimination method provided above. When the service node obtains the heat of multiple data tables, the service node can obtain the heat of multiple data tables. Distinguish which data tables are more popular and which are less popular, so as to manage multiple data tables.

In a possible embodiment, after acquiring the heatness of multiple data tables, the service node may delete data tables whose heatness is less than the first preset threshold from the storage node according to the heatness of the multiple data tables, so as to save storage space.

In a possible embodiment, after acquiring the popularity of the multiple data tables, the service node may display the data tables whose popularity is greater than the second preset threshold from the multiple data tables on the display interface according to the popularity of the multiple data tables The position of the data table is adjusted to the front of the data table whose heat is less than the second preset threshold, that is to say, the position of the data table whose heat is greater than the second preset threshold on the display interface is adjusted to a position that is more convenient for users to view, which is convenient for users. Quickly view popular data sheets.

In a possible embodiment, after acquiring the heatness of multiple data tables, the service node may further migrate data tables whose heatness is less than the third preset threshold to the first storage device, and the heatness is greater than the fourth preset threshold. The data table of the threshold is migrated to the second storage device, wherein the storage performance of the first storage device is lower than that of the storage node, and the storage performance of the second storage device is higher than that of the storage node.

The sizes of the first preset threshold, the second preset threshold, the third preset threshold, and the fourth preset threshold can be set according to actual conditions, and are not specifically limited here.

It can be understood that the service node migrates the data tables with low heat to the first storage device whose storage performance is lower than that of the storage node, which not only prevents the data tables with low heat from continuing to occupy the resources of the storage node, but also prevents the subsequent users from viewing this part of the data tables. It can be found from the first storage device; the service node migrates the hot data table to the second storage device with higher storage performance than the storage node, which can improve the efficiency of operating data in the hot data table, and improve the efficiency of the hot data table. Data sheet storage security.

It can be seen from the above embodiments that the method for distinguishing the heat of data tables provided by the embodiments of the present application introduces a second data table that has an associated relationship with the first data table as the first data table when determining the heat degree H of the first data table. The resulting heat, that is, the associated heat H ₁ between the first data table and the second data table, can make the calculated heat H of the first data table more accurate and more in line with the actual application scenario. In the case of hotness, the hotness of multiple data tables can be better distinguished.

A method for distinguishing the heatness of a data table according to an embodiment of the present application is described in detail above. Based on the same inventive concept, the apparatus for distinguishing the heatness of a data table in an embodiment of the present application is continued below.

Referring to FIG. 8, FIG. 8 is a schematic structural diagram of a data processing system 10 provided by an embodiment of the present application. The data processing system 10 includes a data table heat distinguishing device 1100 provided by an embodiment of the present application. The data table heat distinguishing device 1100 includes: an acquisition module 1101 and a processing module 1102, the data table heat discrimination device 1100 can be integrated into the service node 110 in the data processing system 10, and the data processing system 10 can include, in addition to the service node 110, a storage node 120, The first storage device 130 and the second storage device 140, wherein,

The storage node 120 stores a plurality of data tables;

an obtaining module 1101, configured to obtain a second data table associated with the first data table from the storage node 120;

The processing module 1102 is configured to obtain the associated heat H ₁ of the first data table and the second data table, wherein the associated heat H ₁ of the first data table and the second data table is based on the inherent heat of the second data table and the first data The association relationship between the table and the second data table is obtained, and the inherent heat of the second data table is the heat generated by the second data table being called;

The processing module 1102 is configured to determine the popularity H of the _first data table according to the correlation H1 of the first data table and the second data table.

In a possible embodiment, the obtaining module 1101 is specifically used for:

Acquire a second data table having a data blood relationship with the first data table from the storage node 120, wherein the data blood relationship indicates that the second data table is calculated according to the first data table, and/or the first data table is based on the second data Calculated from the table;

The processing module 1102 is specifically used for:

According to the data blood relationship between the first data table and the second data table, the correlation heat H ₁ of the first data table and the second data table is calculated.

In a possible embodiment, the obtaining module 1101 is specifically used for:

Acquire a second data table having a primary-foreign key association relationship with the first data table from the storage node 120, wherein the primary-foreign key association relationship indicates that one or more fields in the first data table are referenced as the primary key of the second data table , and/or, one or more fields in the second data table are referenced as the primary key of the first data table;

The processing module 1102 is specifically used for:

According to the primary and foreign key association relationship between the first data table and the second data table, the association heat H ₁ of the first data table and the second data table is calculated.

In a possible embodiment, the processing module 1102 is specifically configured to:

The heat H 0 of the first data table is determined according to the inherent heat H ₀ of the first data table and the associated heat H ₁ of the first data table and the second data table, wherein the inherent heat H ₀ of the first data table is the first data table The heat generated by the call.

In a possible embodiment, the processing module 1102 is further configured to:

Calculate the heat of multiple data tables;

According to the calculation result, the data table whose heat is less than the first preset threshold is deleted from the storage node 120 .

In a possible embodiment, the processing module 1102 is further configured to:

According to the calculation result, the position on the display interface of the data table whose heat is greater than the second preset threshold among the plurality of data tables is adjusted to be in front of the data table whose heat is less than the second preset threshold.

In a possible embodiment, the processing module 1102 is further configured to:

According to the calculation result, the data tables whose heat is less than the third preset threshold are migrated to the first storage device 130 , and the data tables whose heat is greater than the fourth preset threshold are migrated to the second storage device 140 , wherein the data tables of the first storage device 130 are The performance is lower than that of the storage node 120 , and the performance of the second storage device 140 is higher than that of the storage node 120 .

The sizes of the first preset threshold, the second preset threshold, the third preset threshold, and the fourth preset threshold can be set according to actual conditions, and are not specifically limited here.

Specifically, for the specific implementation of various operations performed by the data table heat discrimination device 1100 in the above data processing system 10, reference may be made to the description in the relevant content in the above-mentioned embodiment of the data table heat discrimination method. .

It should be understood that the data processing system 10 and the apparatus 1100 for distinguishing the heat of a data table are only an example provided by the embodiments of the present application, and the data processing system 10 and the apparatus 1100 for distinguishing the heat of a data table may have more or more components than those shown in FIG. 8 . Fewer components, two or more components may be combined, or may be implemented with different configurations of components.

The embodiment of the present application further provides a computing device cluster 20, and the computing device cluster 20 can be used to deploy the data processing system 10 shown in FIG. 8, and specifically can be used to deploy the data table in the data processing system 10 shown in FIG. 8 The heat distinguishing apparatus 1100 is configured to execute the data table heat distinguishing method provided by the embodiment of the present application. As shown in FIG. 9 , the computing device cluster 20 includes at least one computing device 200 .

Specifically, in the case that the computing device cluster 20 includes only one computing device 200 , all the modules in the data processing system 10 shown in FIG. 8 may be deployed in the one computing device 200 : the service node 110 and the storage node 120 , the first storage device 130 and the second storage device 140 .

In the case where the computing device cluster 20 includes multiple computing devices 200, each computing device 200 in the multiple computing devices 200 may be used to deploy some modules in the data processing system 10 shown in FIG. Two or more of the computing devices 200 of the computing devices 200 are jointly used to deploy one or more modules in the data processing system 10 shown in FIG. 8 .

For example, assuming that the plurality of computing devices 200 includes a computing device 200A and a computing device 200B, the computing device 200A can be used to deploy the service node 110 and the storage node 120, and the computing device 200B can be used to deploy the first storage device 130 and the second storage device 130. The storage device 140, or the computing device 200A and the computing device 200B are jointly used to deploy the service node 110, for example, the obtaining module 1101 in the data table heat distinguishing device 1100 is deployed on the computing device 200A, and the data table heat distinguishing device is deployed on the computing device 200B In the processing module 1102 in 1100, the computing device 200A is also used to deploy storage nodes, and the computing device 200B is also used to deploy the first storage device 130 and the second storage device 140; it is assumed that the multiple computing devices 200 include computing devices 200A, 200B, 200C and 200D, the computing device 200A can be used to deploy the service node 110, the computing device 200B can be used to deploy the storage node 120, the computing device 200C can be used to deploy the first storage device 130, and the computing device 200D can be used to deploy the second storage device 140.

In a specific implementation, at least one computing device 200 included in the computing device cluster 20 may be all terminal devices, or all cloud servers, or some cloud servers and some terminal devices, which are not specifically limited here.

More specifically, each computing device 200 in the computing device cluster 20 may include a processor, a memory, a communication interface, etc., and the memory in one or more computing devices 200 in the computing device cluster 20 may store the same The code (which may also be referred to as an instruction or a program instruction, etc.) for executing the data table heat discrimination method provided by the embodiment of the present application, the processor can read the code from the memory, and execute the code to realize the code provided by the embodiment of the present application. According to the method for distinguishing data table heat, the communication interface can be used to realize the communication between each computing device 200 and other devices.

In some possible implementations, each computing device 200 in the computing device cluster 20 may also communicate with other devices through a network connection. Wherein, the network may be a wide area network or a local area network, or the like.

The following will describe in detail the computing device 200 provided with the embodiment of the present application in which the apparatus 1100 for distinguishing the heat of a data table is deployed with reference to FIG. 10 .

Referring to FIG. 10 , the computing device 200 in which the apparatus 1100 for distinguishing the data table heat is deployed includes: a processor 210 , a memory 220 and a communication interface 230 , wherein the processor 210 , the memory 220 and the communication interface 230 can be connected to each other through a bus 240 . in,

The processor 210 may read the code stored in the memory 220, and cooperate with the communication interface 230 to execute some or all of the steps of the data table heat discrimination method performed by the data table heat discrimination apparatus 1100 in the above embodiments of the present application.

The processor 210 may have various specific implementation forms, for example, the processor 210 may be a central processing unit (central processing unit, CPU) or a graphics processing unit (graphics processing unit, GPU), and the processor 210 may also be a single-core processor or multi-core processor. The processor 210 may be a combination of a CPU and a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a general-purpose array logic (generic array logic, GAL) or any combination thereof. The processor 210 may also be independently implemented by a logic device with built-in processing logic, such as an FPGA or a digital signal processor (digital signal processing, DSP).

The memory 220 may store codes as well as data. The code includes: the code of the acquisition module 1101 and the code of the processing module 1102, etc., and the data includes: the inherent heat H ₀ of the first data table, the inherent heat of the second data table, and the association between the first data table and the second data table Heat H ₁ and so on.

In practical applications, the memory 220 may be a non-volatile memory, such as a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (erasable). PROM, EPROM), electrically erasable programmable read-only memory (electrically EPROM, EEPROM), or flash memory. The memory 220 may also be volatile memory, which may be random access memory (RAM), which acts as an external cache.

Communication interface 230 may be a wired interface (eg, an Ethernet interface) or a wireless interface (eg, a cellular network interface or using a wireless local area network interface) for communicating with other computing nodes or devices. When the communication interface 230 is a wired interface, the communication interface 230 may use a protocol family above transmission control protocol/internet protocol (TCP/IP), for example, remote function call (RFC) protocol, simple object access protocol (SOAP) protocol, simple network management protocol (SNMP) protocol, common object request broker architecture (CORBA) protocol, and distributed protocols and many more.

The bus 240 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA for short) bus or the like. The bus 240 can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one thick line is used in FIG. 10, but it does not mean that there is only one bus or one type of bus.

The above computing device 200 is configured to execute the method in the above embodiment of the method for classifying the heat of a data table, which belongs to the same concept as the above embodiment of the method. For the specific implementation process, please refer to the above embodiment of the method, which will not be repeated here.

It should be understood that the computing device 200 is only an example provided by the embodiments of the present application, and the computing device 200 may have more or less components than those shown in FIG. 10 , two or more components may be combined, or Different configurations of components are possible.

Embodiments of the present application also provide a non-transitory computer-readable storage medium, where code is stored in the non-transitory computer-readable storage medium, and when the non-transitory computer-readable storage medium runs on a processor, the data table heat rate described in the foregoing embodiments can be implemented. Distinguish some or all of the steps of the method.

It can be understood that as the number of data tables becomes larger and larger, a large number of databases and data systems will also appear. In addition to managing a large number of data tables, enterprises also need to manage a large number of databases and data systems. Therefore, distinguishing the heat of a large number of databases will definitely become a key part of the management of a large number of databases, and distinguishing the heat of a large number of data systems will become a key part of the management of a large number of data systems. The idea of the method, device and related equipment for distinguishing the heat of data tables provided in this application can not only be applied to the management of a large number of data tables, but also can be applied to distinguish the heat of a large number of databases and data systems.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

In the above embodiments, it may be implemented in whole or in part by software, hardware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product may contain code. When the computer program product is read and executed by a computer, part or all of the steps of the method for distinguishing the heat of a data table described in the above method embodiments can be implemented. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server, or data center by wire (eg, coaxial cable, optical fiber, digital subscriber line) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media, or semiconductor media, and the like.

The steps in the method of the embodiment of the present application may be sequentially adjusted, combined or deleted according to actual needs; the units in the device of the embodiment of the present application may be divided, combined or deleted according to actual needs.

The embodiments of the present application have been introduced in detail above, and the principles and implementations of the present application are described in this paper by using specific examples. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for Persons of ordinary skill in the art, based on the idea of the present application, will have changes in the specific implementation manner and application scope. In summary, the contents of this specification should not be construed as limitations on the present application.

Claims (17)

1. A method for distinguishing the heat of a data table, characterized in that the method comprises:

   The service node obtains a second data table associated with the first data table from a storage node, where the storage node stores a plurality of data tables;

   The service node acquires the associated heat of the first data table and the second data table, wherein the associated heat of the first data table and the second data table is based on the inherent heat of the second data table And the association relationship between the first data table and the second data table is obtained, and the inherent heat of the second data table is the heat generated by the second data table being called;

   The service node determines the popularity of the first data table according to the relative popularity of the first data table and the second data table.
2. The method according to claim 1, wherein the service node obtains the second data table associated with the first data table from the storage node, comprising:

   The service node obtains, from the storage node, the second data table having a data blood relationship with the first data table, wherein the data blood relationship indicates that the second data table is based on the first data table Calculated, or, the first data table is calculated according to the second data table;

   The service node obtains the correlation heat between the first data table and the second data table, including:

   The service node calculates the correlation degree of the first data table and the second data table according to the data blood relationship between the first data table and the second data table.
3. The method according to claim 1, wherein the service node obtains the second data table associated with the first data table from the storage node, comprising:

   The service node acquires, from the storage node, the second data table having a primary and foreign key association relationship with the first data table, wherein the primary and foreign key association relationship represents one of the first data tables Or multiple fields are referenced as the primary key of the second data table, or, one or more fields in the second data table are referenced as the primary key of the first data table;

   The service node obtains the correlation heat between the first data table and the second data table, including:

   The service node calculates the association heat between the first data table and the second data table according to the primary and foreign key association relationship between the first data table and the second data table.
4. The method according to any one of claims 1 to 3, wherein the service node determines the popularity of the first data table according to the relative popularity of the first data table and the second data table, comprising:

   The service node determines the heatness of the first data table according to the inherent heatness of the first data table and the associated heatness of the first data table and the second data table, wherein the first data table The inherent heat is the heat generated by the first data table being called.
5. The method according to any one of claims 1 to 4, wherein the method further comprises:

   the service node calculates the heatness of the plurality of data tables;

   The service node deletes, from the storage node according to the calculation result, data tables whose heat is less than a first preset threshold.
6. The method according to any one of claims 1 to 5, wherein the method further comprises:

   the service node calculates the heatness of the plurality of data tables;

   The service node adjusts, according to the calculation result, a position on the display interface of a data table whose heat is greater than the second preset threshold in the plurality of data tables to be in front of a data table whose heat is less than the second preset threshold.
7. The method according to any one of claims 1 to 6, wherein the method further comprises:

   the service node calculates the heatness of the plurality of data tables;

   The service node migrates, according to the calculation result, data tables whose heat is less than a third preset threshold to a first storage device, where the storage performance of the first storage device is lower than that of the storage node.
8. The method according to any one of claims 1 to 7, wherein the method further comprises:

   the service node calculates the heatness of the plurality of data tables;

   The service node migrates, according to the calculation result, a data table whose heat is greater than a fourth preset threshold to a second storage device, where the storage performance of the second storage device is higher than that of the storage node.
9. An apparatus for distinguishing data table heat, characterized in that the apparatus is applied to a service node, and the apparatus includes:

   an obtaining module, configured to obtain a second data table associated with the first data table from a storage node, where the storage node stores a plurality of data tables;

   A processing module, configured to obtain the correlation degree of the first data table and the second data table, wherein the correlation degree of the first data table and the second data table is based on the inherent characteristics of the second data table The heat and the association relationship between the first data table and the second data table are obtained, and the inherent heat of the second data table is the heat generated by the second data table being called;

   The processing module is configured to determine the popularity of the first data table according to the correlation between the first data table and the second data table.
10. The device of claim 9, wherein:

    The acquisition module is specifically used for:

    Acquire the second data table having a data blood relationship with the first data table from the storage node, wherein the data blood relationship indicates that the second data table is calculated according to the first data table, or , the first data table is calculated according to the second data table;

    The processing module is specifically used for:

    According to the data blood relationship between the first data table and the second data table, the correlation degree of the first data table and the second data table is calculated.
11. The device of claim 9, wherein:

    The acquisition module is specifically used for:

    Acquire the second data table having a primary-foreign key association relationship with the first data table from the storage node, wherein the primary-foreign key association relationship represents one or more fields in the first data table be referenced as the primary key of the second data table, or, one or more fields in the second data table are referenced as the primary key of the first data table;

    The processing module is specifically used for:

    According to the primary and foreign key association relationship between the first data table and the second data table, the association degree of the first data table and the second data table is calculated.
12. The device according to any one of claims 9 to 11, wherein the processing module is specifically configured to:

    The heatness of the first data table is determined according to the inherent heatness of the first data table and the correlation heatness of the first data table and the second data table, wherein the inherent heatness of the first data table is all The heat generated when the first data table is called.
13. The device according to any one of claims 9 to 12, wherein the processing module is further configured to:

    calculating the popularity of the plurality of data tables;

    According to the calculation result, delete the data table whose heat is less than the first preset threshold from the storage node.
14. The device according to any one of claims 9 to 13, wherein the processing module is further configured to:

    calculating the popularity of the plurality of data tables;

    According to the calculation result, the position on the display interface of the data table whose heat is greater than the second preset threshold among the plurality of data tables is adjusted to be in front of the data table whose heat is less than the second preset threshold.
15. The device according to any one of claims 9 to 14, wherein the processing module is further configured to:

    calculating the popularity of the plurality of data tables;

    According to the calculation result, the data tables whose heat is less than the third preset threshold are migrated to the first storage device, and the data tables whose heat is greater than the fourth preset threshold are migrated to the second storage device, wherein the storage of the first storage device The performance of the second storage device is lower than that of the storage node, and the storage performance of the second storage device is higher than that of the storage node.
16. A non-transitory computer-readable storage medium, characterized in that, the non-transitory computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed, the execution of claims 1 to 8 is performed. The method of any one.
17. A computing device cluster, comprising at least one computing device, each computing device including a processor and a memory;

    The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method of any one of claims 1-8.

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