Cross-cluster data asset management method and device, storage medium and product

Abstract

The invention discloses a cross-cluster data asset management method and device, a storage medium and a product, and relates to the technical field of data storage. The cross-cluster data asset management method comprises the steps that a data fusion engine is used for being in butt joint with a plurality of database clusters used for storing data assets, and the database clusters store data of the same data asset in different periods; in a data fusion engine, establishing a life cycle unified view; and according to a data calling instruction provided by the data fusion engine, calling data assets from the plurality of database clusters to the life cycle unified view, and integrating data in different time periods to obtain data assets of a complete life cycle. According to the technical scheme, the problems that in an existing scheme, when the data of the complete life cycle in the table is queried, the data needs to be synchronized firstly and then queried after being synchronized to the same database, the synchronization process is low in efficiency, and storage resources are wasted can be solved.

Description

Technical Field

[0001] The present application relates to the field of data storage technology, and in particular to a cross-cluster data asset management method, device, storage medium, and product. Background Art

[0002] As a centralized data repository, a data lake can store structured and unstructured data at any scale. To meet the processing needs of different data and the allocation of storage costs, a data lake is usually divided into three data areas: online area, near-line area, and historical data archive area; the online area is also called operational data storage, which is mainly used to support daily operations of the enterprise, and the data is usually newer; the near-line area, also called data mart or operational data storage, is mainly used to support business analysis needs; the historical data archive area, also called data archive storage, is mainly used to retain historical data for a long time to meet legal and compliance requirements.

[0003] In a data lake, the data in the online area is usually newer, such as data from the last week or the last month, to support data batch processing and timely business decisions. The near-line area contains more historical data than the online area, which usually includes data from the last two or three years to support more in-depth data analysis, but the data retention time is shorter than that of the historical data archive area. Combined with the above-mentioned data storage characteristics of different durations in the three areas of the data lake, in order to maximize resource utilization, improve job batch efficiency and reduce unit storage costs, the three data areas of the data lake are usually stored in different database clusters. Among them, the online area and the near-line area are generally stored in computing clusters with data processing functions, such as the Gauss database of the MPP architecture; the historical data archive area is generally stored in a low-cost archive cluster, such as the hive database of the Hadoop architecture. When the data assets of the computing cluster are stored for a certain period of time and reach the archive period, they will be moved to the archive cluster for storage.

[0004] Because the life cycle of the same data in the data lake may be too long and span the archiving period, different data areas in the data lake contain data fragments of the same data at different time periods; and as mentioned above, different data areas in the data lake are set in different database clusters, which means that data with a complete life cycle may be stored in different database clusters in different time periods. Therefore, when you need to query the data in the table with a complete life cycle, you need to synchronize the data first, synchronize the data to the same database, and then query the data in the database. This synchronization process is inefficient and causes a waste of storage resources.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide a cross-cluster data asset management method, equipment, storage medium and product, aiming to solve the technical problem that when querying the data in the table throughout its life cycle, the existing solution needs to synchronize the data first, synchronize the data to the same database and then query it, the synchronization process is inefficient, and storage resources are wasted.

[0007] To achieve the above objectives, this application proposes a cross-cluster data asset management method, including:

[0008] Use a data fusion engine to connect multiple database clusters used to store data assets, where the multiple database clusters store data of the same data asset at different time periods;

[0009] In the data fusion engine, a unified view of the life cycle is established;

[0010] According to the data calling instructions provided by the data fusion engine, data assets are called from multiple database clusters to a unified view of the life cycle, integrating data from different time periods to obtain data assets with a complete life cycle.

[0011] Preferably, in the above data asset management method, according to the data call instruction provided by the data fusion engine, the step of calling data assets from multiple database clusters to a unified view of the life cycle, and integrating data from different time periods to obtain data assets of the complete life cycle includes:

[0012] Establish associations between data tables in multiple database clusters and a unified view of the lifecycle;

[0013] According to the association relationship, use the data call instruction to call the data assets in the data table to the unified view of the life cycle;

[0014] In the unified view of the life cycle, data from different periods of the same data asset are integrated in chronological order to obtain data assets with a complete life cycle;

[0015] When a user access request is received, the SQL language corresponding to the user access request is used to call and display the data assets of the entire life cycle.

[0016] Preferably, in the above data asset management method, the step of calling data assets from multiple database clusters to a unified view of the life cycle according to the data call instruction provided by the data fusion engine includes:

[0017] Determine whether there are data archiving tasks between multiple database clusters;

[0018] If it is determined that there are data archiving tasks between multiple database clusters, the archiving progress corresponding to the data archiving tasks is obtained;

[0019] Use the lifecycle archive progress field to encapsulate the archive progress, and add the lifecycle archive progress field to the data call instruction;

[0020] Use data call instructions to call the data assets in the corresponding database cluster to a unified view of the life cycle according to the archiving progress.

[0021] Preferably, in the above data asset management method, the multiple database clusters include a computing cluster and an archiving cluster; before the step of obtaining the archiving progress corresponding to the data archiving task, the method further includes:

[0022] Establish a direct connection between the computing cluster and the archiving cluster;

[0023] When receiving a data archiving task, the computing node of the control computing cluster queries the metadata information of the archiving cluster from the metadata information repository of the archiving cluster;

[0024] Control computing nodes and distribute metadata information and data archiving tasks to multiple storage nodes in the computing cluster;

[0025] Control multiple storage nodes to connect to the file data nodes of the archive cluster according to the direct connection relationship;

[0026] When there is a data archiving task, data assets are archived in parallel to the file data nodes of the archiving cluster according to the metadata information.

[0027] Preferably, the data asset management method further comprises, after the step of archiving the data assets to the file data nodes of the archiving cluster in parallel according to the metadata information:

[0028] For each data table in the archive cluster after archiving, determine whether the number of times the data table has been archived is less than or equal to the predetermined number of times;

[0029] If it is determined that the number of archive times of the data table is less than or equal to the predetermined number of archive times, then the field data verification and the number of records verification are performed on the data table in combination with the corresponding data assets in the computing cluster; or,

[0030] If it is determined that the number of archives of the data table is greater than the predetermined number of archives, the number of records in the synchronization range of the data table is checked;

[0031] When the field data and number of records in the data table are verified successfully, the corresponding data assets in the computing cluster are deleted.

[0032] Preferably, the above data asset management method, if there are data archiving tasks between multiple database clusters, before the step of obtaining the archiving progress corresponding to the data archiving task, further includes:

[0033] When a new data archiving task is detected in the database cluster, determine whether the table structures of the computing cluster and the archiving cluster are consistent;

[0034] If it is determined that the table structures of the archive cluster and the computing cluster are inconsistent, determine whether the archive cluster needs to rebuild the table according to the predetermined table building rules;

[0035] If it is determined that the archive cluster needs to rebuild the table, change the table structure in the archive cluster according to the table structure in the computing cluster;

[0036] When the table structures of the archive cluster and the computing cluster are consistent after the change, the data archiving task is executed.

[0037] Preferably, the data asset management method includes the step of changing the table structure in the archive cluster according to the table structure in the computing cluster:

[0038] Generate a table structure change work order;

[0039] According to the table structure change work order, use the table structure in the computing cluster to change the table structure in the archive cluster;

[0040] Write historical data into the table structure of the changed archive cluster;

[0041] Determine whether the table structures of the archive cluster and the computing cluster are consistent after the change;

[0042] If the table structures of the archive cluster and the compute cluster are consistent after the change, execute the steps of the data archiving task.

[0043] To achieve the above objectives, the present application also provides a cross-cluster data asset management device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the cross-cluster data asset management method provided by any of the above technical solutions.

[0044] To achieve the above objectives, the present application also provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the cross-cluster data asset management method provided by any of the above technical solutions are implemented.

[0045] To achieve the above objectives, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the cross-cluster data asset management method provided by any of the above technical solutions are implemented.

[0046] One or more technical solutions proposed in this application have at least the following technical effects:

[0047] A data fusion engine is used to connect to multiple database clusters, which are used to store a large number of data assets, wherein data of different time periods of the same data may be stored in different database clusters respectively. In this way, the data fusion engine can retrieve data of different time periods of the same data asset by connecting to the above database clusters, and establish a unified view of the life cycle in the data fusion engine. In this way, by calling the data asset from the database cluster to the above unified view of the life cycle according to the data call instruction, the data asset can be displayed in the unified view of the life cycle. In addition, the data of different time periods of the same data asset are integrated, so that the data asset of the complete life cycle can be obtained and displayed in the unified view of the life cycle. The above technical solution of the present application does not need to synchronize the data in different database clusters to the same database in advance, but only needs to display the data assets of the complete life cycle in the unified view of the life cycle. Compared with the solution of the background technology, the process is more efficient, occupies less storage resources, and can efficiently and flexibly access data across data sources, thereby improving the integration and access efficiency of the complete life cycle data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A schematic diagram of the process of the first cross-cluster data asset management method provided in an embodiment of the present application;

[0051] Figure 2 For this application Figure 1 A flowchart of the first method of calling and integrating digital assets is provided;

[0052] Figure 3 For this application Figure 1 A flowchart of the second method of calling and integrating data assets is provided;

[0053] Figure 4 A schematic diagram of a flow chart of a second cross-cluster data asset management method provided in an embodiment of the present application;

[0054] Figure 5 It is a flowchart of a data verification method for a data archiving task provided by an embodiment of the present application;

[0055] Figure 6 It is a flowchart of a table structure verification method for a data archiving task provided by an embodiment of the present application;

[0056] Figure 7 This application Figure 6 A flowchart of a method for changing the table structure in an archive cluster is provided;

[0057] Figure 8 This is a schematic diagram of a link structure for exchanging data between database clusters using an exchange platform provided in an embodiment of the present application;

[0058] Fig. 9 This is a schematic diagram of an interconnection link structure between database clusters provided in an embodiment of the present application;

[0059] Fig.10 It is a schematic diagram of a database interconnection structure provided in an embodiment of the present application;

[0060] Fig.11 It is a structural diagram of a cross-cluster data asset management device provided in an embodiment of the present application.

[0061] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0063] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0064] The prior art has the following technical problems:

[0065] The data in the data lake is divided into regions, and different regions are stored in different database clusters, such as computing clusters and archiving clusters. Usually, the data assets of the computing cluster will reach the archiving period after being stored for a certain period of time, and will be moved to the archiving cluster for storage. Because the life cycle of the same data in the data lake may be too long and will span the archiving period, different data areas in the data lake will record data fragments of the same data at different time periods; and as mentioned above, different data areas in the data lake are set in different database clusters, which means that data with a complete life cycle may be stored in different database clusters in different time periods. Therefore, when you need to query the data of the entire life cycle in the table, you need to synchronize the data first, synchronize the data to the same database, and then query the data in the database. The efficiency of this synchronization process is low, resulting in a waste of storage resources.

[0066] In order to solve the above technical problems, the present application provides a solution, which uses a data fusion engine to connect to multiple database clusters set up by the data lake. In this way, even if different database clusters store data of different time periods of a unified data asset, the data fusion engine can be used to extract it, and a unified view of the life cycle is established in the data fusion engine, so that data assets can be called from the above multiple database clusters to the unified view of the life cycle according to the data call instruction, so that the data assets of the complete life cycle can be obtained by integrating data of different time periods in the unified view of the life cycle. Through the above method, it is only necessary to build a unified view of the life cycle in the data fusion engine, and view the data assets of the complete life cycle in the unified view of the life cycle. There is no need to synchronize the data stored in different time periods to the same database for query, thereby solving the problems of low efficiency of the synchronization process and waste of storage resources in the prior art. In addition, the above method is simple to call, and the calling and display process is efficient. There is no need to create or modify the data in the database, thereby achieving the purpose of improving the calling efficiency and reducing the waste of resources.

[0067] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions, a computer-readable storage medium, etc.

[0068] In order to better understand the above technical solution, the exemplary embodiments of the present application will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments shown here. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0069] To achieve the above technical objectives, see Figure 1 , Figure 1 A schematic diagram of a cross-cluster data asset management method is provided for an embodiment of the present application. Figure 1 As shown, the cross-cluster data asset management method includes:

[0070] S110: Use the data fusion engine to connect to multiple database clusters for storing data assets. Among them, multiple database clusters store data of different time periods for the same data asset. For example, multiple database clusters include computing clusters and archiving clusters, and three data areas in the data lake: online area, near-line area, and historical data archive area; the same data in the data lake may be divided into the above three areas by time period. The data in the online area and near-line area are stored in the computing clusters respectively, and the data in the historical data archive area is stored in the archive cluster.

[0071] By connecting multiple underlying databases through the data fusion engine, the purpose of accessing all underlying data with one SQL language can be achieved. The data fusion engine here is preferably the presto fusion engine. The data fusion engine can provide independent computing and analysis operations without relying on other computing engines. The data fusion engine can connect to multiple data sources, read data from different data sources and perform analysis and processing. For example, a presto query in the presto engine can merge multiple data sources and perform analysis across multiple different database clusters. In addition, the data fusion engine here is a memory-based computing engine that can perform statistical analysis on massive data. It does not require a separate database and does not occupy database resources. On the contrary, the more database clusters there are and the more resources there are, the higher the amount of data it calculates.

[0072] S120: In the data fusion engine, a unified view of the life cycle is established. The unified view of the life cycle is a unified view that can display data assets throughout their entire life cycle. The data fusion engine uses the SQL language to establish the view, and then calls the data of the data assets at different time periods and integrates them to obtain a unified view that can display data assets throughout their entire life cycle. Constructed in the data fusion engine, the unified view of the life cycle can use the data fusion engine to extract and display the data assets of each database cluster. Because as mentioned above, multiple database clusters store data from different time periods for the same data asset. In this way, using the data fusion engine to extract data assets from multiple database clusters into the unified view of the life cycle can display data fragments from different time periods in a unified manner.

[0073] S130: According to the data calling instructions provided by the data fusion engine, data assets are called from multiple database clusters to a unified view of the life cycle, and data from different time periods are integrated to obtain data assets with a complete life cycle.

[0074] As described above, multiple database clusters store data from different time periods for the same data asset. When the data assets of the computing cluster reach the archiving deadline, they will be moved to the archiving cluster for storage. In this way, if the life cycle of the same data asset is too long, it may span the archiving deadline, resulting in data fragments from different time periods of the same data asset being stored in the computing cluster and the archiving cluster respectively. The present application uses the data call instructions of the data fusion engine to call data assets from multiple database clusters to a unified view of the life cycle. In this way, even if different database clusters store data fragments from different time periods, data fragments from all time periods of the same data asset will be displayed in the unified view of the life cycle. Further integration of data from different time periods of the same data asset can obtain data assets with a complete life cycle.

[0075] Taking the presto fusion engine docking the gauss database of the computing cluster and the hive database of the archiving cluster as an example, the code for implementing one sql to access multiple underlying data in the embodiment of the present application is as follows:

[0076] CREATE or REPLACE VIEW order

[0077] (orderkey comment'orderId',

[0078] orderstatus comment'status',

[0079] Half comment'half')AS

[0080] SELECT orderkey,orderstatus,totalprice / 2As half,tx_date FROMgauss.stg.orders

[0081] union all

[0082] SELECT orderkey,orderstatus,totalprice / 2As half,tx_date FROMhive.stg.orders

[0083] Through the above method, data assets in multiple underlying databases can be extracted into a unified view of the life cycle and integrated to obtain data assets with a complete life cycle. Among them, hive and gauss are the catalog names of different underlying databases.

[0084] In summary, the technical solution provided by the embodiment of the present application uses a data fusion engine to connect to multiple database clusters, and the database cluster is used to store a large number of data assets, wherein data of different time periods of the same data may be stored in different database clusters respectively, so that the data fusion engine can access the data of different time periods of the same data asset by connecting to the above database cluster, and establish a unified view of the life cycle in the data fusion engine, so that the data asset is called from the database cluster to the above unified view of the life cycle according to the data call instruction, and the data asset can be displayed in the unified view of the life cycle. In addition, the data of different time periods of the same data asset are integrated, and the data asset of the complete life cycle can be obtained and displayed in the unified view of the life cycle. The above technical solution of the present application does not need to synchronize the data in different database clusters to the same database in advance, but only needs to display the data assets of the complete life cycle in the unified view of the life cycle. Compared with the solution of the background technology, the process is more efficient, occupies less storage resources, and can efficiently and flexibly access data across data sources, thereby improving the integration and access efficiency of the complete life cycle data.

[0085] Among them, as a preferred embodiment, Figure 2 As shown, in the above data asset management method, step S130: according to the data call instruction provided by the data fusion engine, calling data assets from multiple database clusters to a unified view of the life cycle, integrating data from different time periods to obtain data assets of the complete life cycle, includes:

[0086] S131: Establish the association relationship between data tables in multiple database clusters and the unified view of the life cycle. For example, in the above SQL code, "Id" and "status", the embodiment of the present application first defines the names of the data tables, orderkey and orderstatus, in the presto fusion engine. In this way, the association relationship between the data table and the unified view of the life cycle is established, and one SQL is used to access all underlying data.

[0087] S132: According to the association relationship, the data asset in the data table is called to the unified view of the life cycle using the data call instruction. According to the association relationship, the schema name, table name and catalog name of the underlying database of different data assets in the underlying database need to be known, so that the corresponding data asset can be extracted and displayed in the unified view of the life cycle using the SQL call statement.

[0088] S133: In the unified view of the life cycle, data of different periods of the same data asset are integrated in chronological order to obtain a data asset with a complete life cycle. In different database clusters, such as a computing cluster and an archiving cluster, data of the same data asset in different periods can be stored respectively. The computing cluster stores data of the data asset for the past two years, and the archiving cluster stores data after two years. In this way, by integrating data of different periods of the data asset in the above chronological order, a data asset with a complete life cycle can be obtained.

[0089] S134: When a user access request is received, the SQL language corresponding to the user access request is used to call and display the data assets of the complete life cycle.

[0090] The technical solution provided in the embodiment of the present application requires the construction of a unified view of the lifecycle in order to further improve the data asset management and access efficiency across database clusters and further improve the user experience. Specifically: a unified view of the lifecycle is constructed in fusion engines such as presto, and the data tables of the underlying database are associated. The code is as described above and will not be repeated here. Here, in order to enable users to access efficiently, all data assets in the gauss database and the hive database can be called to the unified view of the lifecycle in the background. Then, the user only needs to access the specific data table "order" in the unified view of the lifecycle to access all the data in the order table across the two databases of hive and gauss. The specific SQL language is as follows:

[0091] Select * from order.

[0092] Among them, * indicates the content to be queried. In this case, the user does not need to know the specific schema name, table name, and catalog name of the underlying database to query the data of the entire life cycle in the unified view of the life cycle.

[0093] In addition, since there may be data archiving tasks between the above-mentioned multiple database clusters, for example, when the data assets in the computing cluster reach the archiving deadline, they need to be archived to the archiving cluster. At this time, there may be overlapping data in the data tables on both sides, so it is necessary to set filtering conditions to filter out the overlapping data.

[0094] Specifically, as a preferred embodiment, Figure 3 As shown, in the above data asset management method, step S130: calling data assets from multiple database clusters to a unified view of the life cycle according to the data calling instruction provided by the data fusion engine, includes:

[0095] S135: Determine whether there are data archiving tasks between multiple database clusters; if it is determined that there are data archiving tasks between multiple database clusters, execute step S136. During the execution of the archiving task, the data assets will first be copied from the computing cluster to the archiving cluster in a synchronous manner. At this time, the data assets of the computing cluster will not be deleted first, and the corresponding data in the computing cluster will not be deleted until the verification is passed or after a certain period of time. At this time, it is necessary to filter out the overlapping data assets through screening conditions.

[0096] S136: Obtaining the archiving progress corresponding to the data archiving task. In the embodiment of the present application, the specific filtering condition is determined by the archiving progress of the life cycle. For example, the current table is archived and synchronized based on the archiving progress, so that the overlapping data can be removed according to the archiving progress.

[0097] S137: Encapsulate the archiving progress using the lifecycle archiving progress field, and add the lifecycle archiving progress field to the data call instruction. The lifecycle archiving progress field encapsulates the archiving progress, so that the overlapping data assets in the above multiple database clusters can be filtered out according to the archiving progress, thereby improving the display effect of the data in the unified view of the lifecycle.

[0098] S138: Use data call instructions to call data assets in the corresponding database cluster to the unified view of the life cycle according to the archiving progress.

[0099] Specifically, in the two underlying databases of the computing cluster and the archiving cluster, gauss and hive, there may be overlapping data in the data tables on both sides due to the existence of data archiving tasks in the two underlying databases. Therefore, it is necessary to filter out the overlapping parts through filtering conditions. Generally, the filtering conditions are determined based on the value of the lifecycle archiving progress field and its encapsulated archiving progress. For example: the current data table is archived and synchronized based on the lifecycle archiving progress field tx_date. The current archiving progress of the data assets in the data table is 2023-06-18, that is, the data assets before 2023-06-18 are stored in the hive database (archiving cluster), and the data assets after 2023-06-18 are stored in the gauss database (computing cluster). Then the unified view of the lifecycle needs to be established as follows:

[0100] CREATE or REPLACE VIEW order

[0101] (orderkey comment'orderId',

[0102] orderstatus comment'status',

[0103] Half comment'half')AS

[0104] SELECT orderkey,orderstatus,totalprice / 2As half,tx_date FROMgauss.stg.orders where tx_date>=2023-06-18

[0105] union all

[0106] SELECT orderkey,orderstatus,totalprice / 2As half,tx_date FROMhive.stg.orders where tx_date<2023-06-18

[0107] When the lifecycle archiving progress field and its value are updated, they will be pushed from the pre-built lifecycle system to the view building module of the presto fusion engine, and the view building module will update the view structure of the lifecycle unified view. The lifecycle system triggers the data archiving task, and after the data is archived, it will push the data to the above-mentioned presto fusion engine to update the view structure.

[0108] In addition, the database cluster may have data archiving tasks, that is, data synchronization between clusters. Currently, the mainstream cross-cluster data synchronization needs to be transferred through the exchange platform. The data synchronization forms are as follows: Figure 8 As shown in the figure, in this exchange platform, the data archiving task is divided into two steps: data extraction EX and data loading LD. For example, the Gauss database of the computing cluster needs to first extract data through the ETL tool and land it in the standard file EX of the exchange platform. This process is called EX data extraction, and then the data is loaded into the HIVE database through the ETL tool. This process is LD data loading.

[0109] However, the data transfer process through the exchange platform will cause file landing problems, resulting in low efficiency and poor stability of archiving tasks. Fig. 9 As shown, the following embodiments of the present application write data assets directly from the source database to the target database by directly connecting the databases on both sides.

[0110] In the computing cluster Gauss database, use pdm_dhis. <tablename>The command inserts the data table table into the cluster's external table HST_FGDC. <tablename>, where the external mapping is from the big data analysis platform Hadoop / historical library CDP to the computing cluster Gauss, and data synchronization is to synchronize data from the computing cluster to the big data analysis platform Hadoop / historical library CDP, specifically STG. <tablename>.

[0111] Specifically, as a preferred embodiment, Figure 4 As shown, in the above data asset management method, the multiple database clusters include a computing cluster and an archiving cluster; before step S136: obtaining the archiving progress corresponding to the data archiving task, the data asset management method further includes:

[0112] S210: Establish a direct connection between the computing cluster and the archiving cluster; here, it is necessary to import the corresponding configuration file to realize the direct connection between the computing cluster and the archiving cluster. This direct connection not only enables data archiving of data assets in the computing cluster to the archiving cluster, but also enables authorized users to directly access the Hadoop database of the archiving cluster from the GaussDB external interface of the computing cluster.

[0113] S220: When receiving the data archiving task, the computing node of the control computing cluster queries the metadata information of the archiving cluster from the metadata information repository of the archiving cluster.

[0114] Combination Fig.10 From the database direct connection structure shown, it can be seen that the metadata information library used in this application is HiveMetastore. HiveMetastore is a metadata management system widely used in the big data ecosystem. It provides a centralized metadata management mechanism for distributed data storage and stores a large amount of metadata information. Metadata information "Metadata" refers to data about data, that is, data used to describe, explain, locate or help manage information resources. It can include information such as creation date, author, file size and file type. Therefore, using metadata information, you can Fig.10 The following figure shows how to read and write data in OBS / HDFS.

[0115] S230: Control the computing nodes to distribute metadata information and data archiving tasks to multiple storage nodes in the computing cluster.

[0116] The metadata information contains the description of the data in the archive cluster, so that the metadata information can be used to determine which data needs to be archived and which data can be read and written. The metadata information and data archiving tasks are distributed to multiple storage nodes in the computing cluster. Multiple storage nodes can realize parallel data transmission with the database OBS / HDFS according to the metadata information, thereby completing the data archiving task.

[0117] S240: Control multiple storage nodes to connect to the file data nodes of the archive cluster according to the direct connection relationship. Fig.10 In the illustrated embodiment, the computing node of Gauss is connected to the HiveMetastore in the historical library Hive to obtain the metadata information of the hive table. The computing node then decomposes the data archiving task and sends it to each storage node. The storage node is connected to the file data node hdfsDataNode of the distributed system infrastructure Hadoop, thereby completing the transmission of parallel data between nodes.

[0118] S250: When there is a data archiving task, archive the data assets to the file data nodes of the archiving cluster in parallel according to the metadata information. Fig.10 As shown, in the embodiment of the present application, the distributed system infrastructure Hadoop includes an object storage service OBS and a Hadoop distributed file system hdfs. The hdfsDataNode nodes in the distributed file system hdfs are connected to the above-mentioned storage nodes, so as to realize parallel data transmission between the two major database clusters of the computing cluster and the archiving cluster, thereby greatly improving the data archiving efficiency.

[0119] Specifically, combined Fig. 9 and Fig.10 From the database interconnection structure shown, we can see that: in order to achieve the transmission efficiency and stability of data archiving tasks, the computing node CN of the gauss database connects to the metadata information library HiveMetastore of the historical library hive to obtain the metadata information of the hive table, and the computing node CN decomposes the task and sends it to each storage node DN. The DN node connects to the hdfsDataNode node of the distributed system infrastructure Hadoop, which can realize parallel data transmission between nodes and realize the reading and writing of data in the object storage service OBS and the distributed file system HDFS. Since the maximum concurrency of a single job is consistent with the number of DN nodes in the Gauss cluster, the transmission efficiency can be greatly improved compared with the single concurrency of the exchange platform.

[0120] In order to successfully complete the data archiving task, it is necessary to achieve interconnection between the computing cluster and the archiving cluster, which requires establishing a direct connection between the computing cluster and the archiving cluster in advance. Fig. 9 As shown, the embodiment of the present application imports the Hadoop configuration file into the Gauss database, creates a Gauss_DB Server address pointing, and points the Gauss_DB external table to the hdfs address, thereby enabling Gauss_DB and Hadoop to directly read and write data.

[0121] In addition, in order to ensure the data quality in the data lake, in the above data archiving tasks, data verification is required with the source database data after data synchronization.

[0122] Specifically, as a preferred embodiment, Figure 5 As shown, the above data asset management method, after the step S250: archiving the data assets to the file data nodes of the archiving cluster in parallel according to the metadata information, further includes:

[0123] S260: For each data table in the archive cluster after archiving, determine whether the number of archives of the data table is less than or equal to the preset number of archives; usually, for each data table, the preset number of archives is usually set to 3. Perform full field sampling verification, partial field full data verification, and record number verification in the first three data archives (including new tables after structure changes). For data tables that have completed the first three archives, only record data comparison of synchronization range data is performed afterwards.

[0124] S270: If it is determined that the number of archives of the data table is less than or equal to the predetermined number of archives, then the field data verification and record number verification of the data table are performed in combination with the corresponding data assets in the computing cluster; the field data verification here includes: full field sampling verification logic and partial field full data verification logic. Specifically:

[0125] If the above verification is successful, then if it occurs more than three times, the following step S280 is executed; if the verification fails, it means that there is a problem with the data in the computing cluster, and data archiving needs to be suspended, waiting for the operator of the computing cluster to revise the data.

[0126] 1) Full field sampling verification logic:

[0127] According to the split field sampling, select a date slice of data to perform: sum of numeric types, sum of date type to seconds, and checksum of character type.

[0128] 2) Full data verification logic for some fields:

[0129] According to the data types of all fields in the archive table, select one field for each data type to perform full data verification. The full data verification here includes the above-mentioned sum of numeric types, sum of date types to seconds, and character type checksum operations on all fields.

[0130] The record number verification logic includes the synchronization range record number comparison.

[0131] S280: If it is determined that the number of archiving times of the data table is greater than the predetermined number of archiving times, the number of records in the synchronization range of the data table is checked;

[0132] S290: When the field data and record number of the data table are successfully verified, the corresponding data assets in the computing cluster are deleted. If the verification is successful, it means that the data archiving is completed. At this time, the corresponding data assets in the computing cluster are deleted to complete the data archiving task. Of course, there may be cases where data verification fails. At this time, it means that there is a problem with the data in the computing cluster. Data archiving needs to be suspended and the operator of the computing cluster needs to revise the data. Among them, the record number verification logic: the synchronization range data record number comparison, specifically through SQL instructions to synchronize data between the two database clusters.

[0133] The technical solution provided in the embodiment of the present application determines, for each data table in the archiving cluster after archiving, whether the number of archiving times of the data table is less than or equal to the predetermined number of archiving times, and performs data verification on the data assets in the data table according to the number of archiving times, thereby reducing data inconsistencies caused by data changes in the computing cluster, thereby reducing data errors in data archiving tasks, and maintaining the consistency of data in the computing cluster and the archiving cluster.

[0134] In addition, since data assets need to create tables in both the computing cluster and the archiving cluster, there is a problem of inconsistency between the computing cluster structure changes and the table structure in the archiving cluster. Therefore, it is necessary to ensure the consistency of the table structure of the archiving cluster and the computing cluster.

[0135] To achieve the above object, as a preferred embodiment, Figure 6 As shown, the above data asset management method, step S116: if it is determined that there are data archiving tasks between multiple database clusters, before the step of obtaining the archiving progress corresponding to the data archiving task, further includes:

[0136] S310: When a new data archiving task is detected in the database cluster, determine whether the table structures of the computing cluster and the archiving cluster are consistent. The inconsistency between the computing cluster and the archiving cluster table structures is usually caused by a change in the table structure in the computing cluster, because the table structure change operation is generally initiated only in the computing cluster, and the archiving cluster will not actively initiate a table structure change. At this time, if the table structure changes, the archiving cluster data needs to be reorganized.

[0137] S320: If it is determined that the table structures of the archive cluster and the computing cluster are inconsistent, determine whether the archive cluster needs to rebuild the table according to the predetermined table building rules. The method of determining whether the archive cluster needs to rebuild the table according to the predetermined table building rules is as follows:

[0138] 1. Each time a new archiving task is created, determine whether the structure of the original table in the computing cluster is consistent with that of the target table in the archiving cluster. This requires consistency verification of the field name, field order, and field type.

[0139] 2. If step 1 identifies that the table structure of the computing cluster has changed, continue to identify whether the table needs to be rebuilt. If the table structure of the computing cluster meets the following predetermined table building rules, the table needs to be rebuilt: 2.1. New fields are added; 2.2. The field type changes; 2.3. The field precision becomes smaller.

[0140] S330: If it is determined that the archive cluster needs to rebuild the table, the table structure in the archive cluster is changed according to the table structure in the computing cluster. If the above steps identify that the table needs to be rebuilt, the archive task needs to be temporarily interrupted, and the table structure in the archive cluster is changed according to the changed table structure in the computing cluster.

[0141] S340: When the table structures of the archive cluster and the computing cluster are consistent after the change, the data archive task is executed.

[0142] The technical solution provided by the embodiment of the present application requires tables to be created in both the computing cluster and the archiving cluster for data assets, so there is a problem of changes in the computing cluster table structure and inconsistency with the table structure in the archiving cluster. To solve this problem, when the table structures of the archiving cluster and the computing cluster are inconsistent, the embodiment of the present application determines whether the archiving cluster needs to rebuild the table according to the predetermined table building rules. When the table needs to be rebuilt, the table structure in the archiving cluster is changed according to the changed table structure in the computing cluster, thereby solving the consistency problem of data in different database clusters and continuing to execute the data archiving task.

[0143] Specifically, as a preferred embodiment, Figure 7 As shown, the above data asset management method, step S330: the step of changing the table structure in the archive cluster according to the table structure in the computing cluster includes:

[0144] S331: Generate a table structure change work order;

[0145] S332: According to the table structure change work order, the table structure in the computing cluster is used to change the table structure in the archive cluster;

[0146] S333: Write the historical data into the table structure of the changed archive cluster;

[0147] S334: Determine whether the table structures of the archive cluster and the computing cluster are consistent after the change;

[0148] S335: If the table structures of the archive cluster and the computing cluster are consistent after the change, the steps of the data archiving task are executed.

[0149] The technical problem provided by the embodiment of the present application is that when it is identified that a table needs to be rebuilt in the archive cluster, the data archiving task is temporarily interrupted, a table structure change work order is generated, and the user is notified to process the change of the table structure in the archive cluster. After receiving the table structure change work order, the user needs to change the structure of the corresponding table in the archive cluster according to the computing cluster table structure. Here, the table structure data that has changed in the computing cluster can be changed accordingly to maintain data consistency. In addition, it is necessary to initialize historical data. Initializing historical data here means rewriting the unchanged historical data in the original table into the table structure of the archive cluster, and closing the table structure change work order after the operation is completed. When it is perceived that the table structure change work order is processed, re-determine whether the table structures in the computing cluster and the archive cluster are consistent. If they are consistent, continue the above-mentioned data archiving task; if they are inconsistent, jump to the above-mentioned step S320.

[0150] In summary, the cross-cluster data asset management method provided by the above embodiments of the present application can solve the following problems respectively through a cross-data source view update method based on lifecycle archiving progress, a cross-cluster data synchronization method based on direct connection of database clusters, and a cross-cluster data consistency assurance method:

[0151] 1. It is impossible to query the data of the entire life cycle of the table in real time. Data synchronization must be performed first and then queried in one of the databases, which is inefficient.

[0152] 2. Historical data archiving requires standard files to be exchanged on the platform, which is inefficient and has poor stability;

[0153] 3. There is a problem of inconsistent table structures of the same data assets in different databases.

[0154] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the cross-cluster data asset management method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0155] In addition, the cross-cluster data asset management device provided by the present application includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the cross-cluster data asset management method of any of the above-mentioned embodiments.

[0156] Reference below Fig.11 , which shows a schematic diagram of the structure of a cross-cluster data asset management device suitable for implementing the embodiment of the present application. The cross-cluster data asset management device in the embodiment of the present application can include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.11 The cross-cluster data asset management device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0157] like Fig.11 As shown, the cross-cluster data asset management device can include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the cross-cluster data asset management device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can enable the cross-cluster data asset management device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a model building device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0158] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0159] The cross-cluster data asset management device provided by the present application adopts the cross-cluster data asset management method in the above embodiment, which can solve the technical problem that the existing technical method needs to synchronize the data first when querying the data of the complete life cycle in the table, synchronize the data to the same database and then query, and the synchronization process is inefficient, resulting in a waste of storage resources. Compared with the prior art, the beneficial effects of the data asset management device provided by the present application are the same as the beneficial effects of the cross-cluster data asset management method provided by the above embodiment, and the other technical features in the data asset management device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0160] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0161] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

[0162] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the data asset management method in the above-mentioned embodiment.

[0163] The computer-readable storage medium provided in the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0164] The computer-readable storage medium mentioned above can be included in the data asset management device; or it can exist independently without being assembled into the data asset management device.

[0165] The computer-readable storage medium carries one or more programs, and when the one or more programs are executed by the model building device, the computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0166] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes also be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs a specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0167] The modules involved in the embodiments of the present application can be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0168] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned data asset management method. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the data asset management method provided in the above-mentioned embodiment, and will not be repeated here.

[0169] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.< / tablename> < / tablename> < / tablename>

Claims

1. A cross-cluster data asset management method, It is characterized in that include: Using a data fusion engine, connecting to multiple database clusters for storing data assets, wherein the multiple database clusters store data of the same data asset in different time periods; In the data fusion engine, a unified view of the life cycle is established; According to the data calling instruction provided by the data fusion engine, data assets are called from the multiple database clusters to the unified view of the life cycle, and the data of different time periods are integrated to obtain data assets with a complete life cycle.

2. The method according to claim 1, It is characterized in that The step of calling data assets from the multiple database clusters to the unified lifecycle view according to the data calling instruction provided by the data fusion engine, and integrating data from different time periods to obtain data assets with a complete lifecycle includes: Establishing an association relationship between the data tables in the multiple database clusters and the unified view of the life cycle; According to the association relationship, using the data call instruction to call the data asset in the data table to the lifecycle unified view; In the unified view of the life cycle, data of different periods of the same data asset are integrated in chronological order to obtain the data asset of the complete life cycle; When a user access request is received, the SQL language corresponding to the user access request is used to call and display the data assets of the complete life cycle.

3. The method according to claim 1, It is characterized in that The step of calling data assets from the multiple database clusters to the unified lifecycle view according to the data calling instruction provided by the data fusion engine includes: Determining whether there is a data archiving task between the multiple database clusters; If it is determined that there is a data archiving task between the multiple database clusters, obtaining the archiving progress corresponding to the data archiving task; Use a life cycle archiving progress field to encapsulate the archiving progress, and add the life cycle archiving progress field to the data calling instruction; The data calling instruction is used to call the data assets in the corresponding database cluster to the lifecycle unified view according to the archiving progress.

4. The method according to claim 3, It is characterized in that The multiple database clusters include a computing cluster and an archiving cluster; before the step of obtaining the archiving progress corresponding to the data archiving task, the method further includes: Establishing a direct connection between the computing cluster and the archiving cluster; When receiving a data archiving task, controlling the computing nodes of the computing cluster to query metadata information of the archiving cluster from a metadata information repository of the archiving cluster; Controlling the computing node to distribute the metadata information and the data archiving task to multiple storage nodes of the computing cluster; Controlling the multiple storage nodes to connect to the file data nodes of the archive cluster according to the direct connection relationship; When there is a data archiving task, the data assets are archived in parallel to the file data nodes of the archiving cluster according to the metadata information.

5. The method according to claim 4, It is characterized in that After the step of archiving the data assets to the file data nodes of the archiving cluster in parallel according to the metadata information, the method further includes: For each data table in the archiving cluster after archiving, determining whether the number of archiving times of the data table is less than or equal to a predetermined number of archiving times; If it is determined that the number of times the data table has been archived is less than or equal to the predetermined number of times, then combining the corresponding data assets in the computing cluster, performing field data verification and record number verification on the data table; or, If it is determined that the number of times the data table has been archived is greater than the predetermined number of times, then the number of records in the synchronization range of the data table is checked; When the field data and the number of records in the data table are verified successfully, the corresponding data assets in the computing cluster are deleted.

6. The method according to claim 4, It is characterized in that If it is determined that there is a data archiving task between the multiple database clusters, before the step of obtaining the archiving progress corresponding to the data archiving task, the method further includes: When it is detected that the database cluster creates a new data archiving task, determining whether the table structures of the computing cluster and the archiving cluster are consistent; If it is determined that the table structures of the archive cluster and the computing cluster are inconsistent, whether the archive cluster needs to rebuild the table according to the predetermined table building rules; If it is determined that the archive cluster needs to rebuild the table, the table structure in the archive cluster is changed according to the table structure in the computing cluster; When the table structures of the archive cluster and the computing cluster are consistent after the change, the data archive task is executed.

7. The method according to claim 6, It is characterized in that The step of changing the table structure in the archive cluster according to the table structure in the computing cluster includes: Generate a table structure change work order; According to the table structure change work order, use the table structure in the computing cluster to change the table structure in the archive cluster; Writing historical data into the table structure of the archive cluster after the change; Determine whether the table structures of the archive cluster and the computing cluster are consistent after the change; If the table structures of the archive cluster and the computing cluster are consistent after the change, the steps of the data archiving task are executed.

8. A cross-cluster data asset management device, It is characterized in that The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cross-cluster data asset management method according to any one of claims 1 to 7.

9. A storage medium, It is characterized in that The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the cross-cluster data asset management method according to any one of claims 1 to 7 are implemented.

10. A computer program product, It is characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the cross-cluster data asset management method according to any one of claims 1 to 7 are implemented.

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