WO2016192583A1 - Procédé et dispositif de traitement de données d'entrepôt de données - Google Patents

Procédé et dispositif de traitement de données d'entrepôt de données Download PDF

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Publication number
WO2016192583A1
WO2016192583A1 PCT/CN2016/083591 CN2016083591W WO2016192583A1 WO 2016192583 A1 WO2016192583 A1 WO 2016192583A1 CN 2016083591 W CN2016083591 W CN 2016083591W WO 2016192583 A1 WO2016192583 A1 WO 2016192583A1
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data
dependency
queried
data processing
metadata
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PCT/CN2016/083591
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English (en)
Chinese (zh)
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吴勇军
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阿里巴巴集团控股有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors

Definitions

  • the present application relates to data processing technologies, and in particular, to a data processing method and apparatus for a data warehouse.
  • a data warehouse is an environment that provides current and historical data that users use for decision support, which is difficult or impossible to obtain in traditional operational databases.
  • Data warehousing technology is a general term for various technologies and modules that effectively integrate operational data into a unified environment to provide decision-making data access. Everything is done to make data users more responsive to the information they need and to provide decision support for data users.
  • the data processing method in the prior art will make the unused data always occupy the computing resources and the storage resources, resulting in waste of resources.
  • the data processing method and device of the data warehouse are provided in the embodiment of the present application, which is used to solve the waste of resources caused by the useless resources of the prior art.
  • a data processing method for a data warehouse including: receiving a query condition input by a user, the query condition includes a keyword of the data to be queried; determining the data to be queried and the data warehouse according to the keyword
  • Other data dependencies, dependencies are one of the following: no dependencies, strong dependencies, weak dependencies; return dependencies to users; receive data processing instructions issued by users according to dependencies; trigger data warehouse to execute data for query data Processing instructions.
  • a data processing apparatus for a data warehouse including: a query module, configured to receive a query condition input by a user, where the query condition includes a keyword of the data to be queried; a dependency determination module, It is used to determine the dependency relationship between the data to be queried and other data in the data warehouse according to the keyword.
  • the dependency relationship is one of the following: no dependency, strong dependency, weak dependency; feedback module, used to return the dependency relationship to the user; instruction receiving
  • the module is configured to receive a data processing instruction issued by the user according to the dependency relationship, and a triggering module, configured to trigger the data warehouse to execute the data processing instruction on the query data.
  • the data processing method and device of the data warehouse in the embodiment of the present application can determine and return a dependency relationship between the data to be queried and other data to the user after receiving the query condition input by the user;
  • the resource efficiency of the warehouse can determine and return a dependency relationship between the data to be queried and other data to the user after receiving the query condition input by the user;
  • the data processing instruction of the data to be queried and then triggers the data warehouse to execute the data processing instruction; thereby, the data in the data warehouse can be processed according to the dependency relationship, thereby avoiding waste of resources caused by not processing the data in the prior art, and improving the data.
  • the resource efficiency of the warehouse can be determined and return a dependency relationship between the data to be queried and other data to the user after
  • FIG. 1 is a flowchart of a data processing method of a data warehouse according to Embodiment 1 of the present application;
  • FIG. 2 is a schematic diagram of a dependency query result according to a data processing method according to Embodiment 2 of the present application;
  • FIG. 3 is a structural block diagram of a data processing apparatus of a data warehouse according to Embodiment 3 of the present application.
  • FIG. 1 is a flowchart of a data processing method of a data warehouse according to Embodiment 1 of the present application.
  • the data processing method of the data warehouse according to the first embodiment of the present application includes the following steps:
  • S102 Receive a query condition input by a user, where the query condition includes a keyword of the data to be queried;
  • a table is the most important component of a data warehouse.
  • a table is usually composed of keywords, metrics, and attribute data.
  • an employee table consists of employee attribute data such as employee number, employee name, and age.
  • the view like the table, also contains a series of column and row data with names. However, the view does not exist in the database as a stored set of data values, but is defined by the query and can be treated as a virtual table.
  • Dependency refers to the relationship formed by the use or consumption of tables or views by other downstream views or tasks during data warehouse data development, or the relationship between the use or consumption of other tables or views in the process of forming tables or views. .
  • No dependence means that there is no dependency between data and other data; strong dependency means that there is a scheduling relationship between data and other data, which is the strongest and most intuitive kind of dependency; weak dependency means that data is not between Scheduling relationships, but can be resolved by executing statements such as SQL (Structured Query Language) logs or View DDL (Data Definition Language) statements; weak dependencies are more concealed during data development It is easy to be ignored; for example, tables are used by views, tables or views are used by data factories, scheduled tasks, data reflow production tasks, etc. are weak dependencies.
  • Each table or view is used by downstream tasks, and is also used by data users in IDE (Integrated Development Environment), reporting tools, and timed tasks.
  • IDE Integrated Development Environment
  • reporting tools and timed tasks.
  • timed tasks there are tens of thousands of tables in the data warehouse, and there are intricate dependencies. relationship.
  • the query condition input by the user includes a keyword of the data to be queried
  • the keyword may be a name of the table, or may be a node ID (an abbreviation of IDentity, an identity number), for example, the data to be queried is an employee.
  • the keyword may be an employee number that is a keyword of the table.
  • the data processing method in the embodiment of the present application may be implemented by using a oracle, a mysql, a teradata traditional database, or a distributed database such as Greenplum, Hadoop, or odps.
  • the dependency relationship between the data to be queried and other data in the data warehouse in the embodiment of the present application may be pre-generated, or may be generated after receiving the query request input by the user, and the application does not do this. limit.
  • the user after receiving the query condition input by the user, the user can determine and return the dependency relationship between the data to be queried and other data to the user;
  • the data processing instruction of the data then triggers the data warehouse to execute the data processing instruction; thereby, the data in the data warehouse can be processed according to the dependency relationship, thereby avoiding waste of resources caused by not processing the data in the prior art.
  • determining the dependency relationship between the data to be queried and other data in the data warehouse according to the keyword comprises: determining the data to be queried according to the keyword; and calling the metadata to generate a dependency relationship between the data to be queried and other data in the data warehouse.
  • Metadata refers to data describing data, descriptive information about data and information resources, including business table structure information, and several warehouse table structure information.
  • the metadata includes one or more of scheduling metadata, SQL execution log metadata, table structure metadata, synchronization center metadata, and timing task metadata.
  • the method further includes: providing the user with the data processing instruction for the data to be queried according to the dependency relationship.
  • the user may be provided with corresponding processing instructions after querying the dependency relationship of the corresponding data to be queried, including: if the dependency relationship of the query data is “no dependency”, then the user is Providing a data processing instruction corresponding to the data without dependency; if the dependency of the query data is "strong dependency”, providing the user with a data processing instruction corresponding to the strongly dependent data; if the dependency of the query data is "weakly dependent", Data processing instructions corresponding to weakly dependent data are provided to the user.
  • the data processing instructions are offline or changed.
  • the offline refers to physical deletion or renaming of the table
  • the change refers to updating the content or view logic of the table.
  • the data engineer can query the dependency of the data that wants to go offline or change; then select the offline or change according to the dependency; for example, if there is no dependency, the offline is performed. If it is a strong dependency, change and notify; if it is a weak dependency, make changes, etc., so that the data engineer can process the data in the data warehouse according to the dependency relationship, which facilitates data processing, improves the accuracy of impact assessment, and improves The efficiency and accuracy of data processing.
  • the query condition may further include querying the direction and level of the dependency of the data, for example, backing up the N level upstream, or querying the N level downstream.
  • the upstream backtracking is an N-level table or view that depends on the upstream query data to be queried;
  • the downstream query is an N-level table or view that is directed to the downstream query data to be queried.
  • the user can use the error check, model health check, data path length detection, data processing efficiency evaluation, etc. of the data to be queried.
  • the user can use the offline or change processing of the data to be queried.
  • the data processing method in the embodiment of the present application can perform function display based on the result of the metadata integration dependency, and provide N-level dependency query and presentation to the upstream and downstream, and the specific dependency result is shown in FIG. 2 .
  • the query blood type refers to the classification of the dependencies that the user wants to query, including: blood list, view blood, task blood, and the like.
  • the user selects the blood type to be queried as “table blood”, and the data to be queried is a table named “dwb_fnd_dback_all_dd”; the query level is 1, and the query direction is downstream.
  • the user After being processed by the data processing method of the embodiment of the present application, the user has feedback to the following node that has a dependency relationship with the "dwb_fnd_dback_all_dd” table: "dwd1”, “dws1”, “dws2”, “dwb1”, “dws3”, “st1” “, “dws4", “st2”, “adm1”, and provides the node name, table name, corresponding dependency and table type corresponding to these nodes.
  • the user can select the corresponding processing mode by clicking the right button at the corresponding node.
  • the result obtained by the query in the embodiment of the present application is “strong dependency”, so the “change” and “change notification” functions are provided to the user.
  • the user after receiving the query condition input by the user, the user can be determined and presented to the user. Returning the dependency relationship between the data to be queried and other data; the user can issue a data processing instruction for the data to be queried according to the dependency relationship, and then trigger the data warehouse to execute the data processing instruction; thereby processing the data in the data warehouse according to the dependency relationship It avoids the waste of resources in the prior art, improves the resource use efficiency of the data warehouse, reduces the error probability of data processing, and improves the efficiency and accuracy of data processing.
  • the data processing device of the data warehouse is also provided in the embodiment of the present application. Since the principle of solving the problem is similar to the data processing method, the implementation of the device can refer to the implementation of the method, and the repetition is not Let me repeat.
  • FIG. 3 is a structural block diagram of a data processing apparatus of a data warehouse according to Embodiment 3 of the present application.
  • the data processing apparatus 20 of the data warehouse includes: a query module 202, configured to receive a query condition input by a user, where the query condition includes a keyword of the data to be queried; the dependency determining module 204 For determining the dependency relationship between the data to be queried and other data in the data warehouse according to the keyword, the dependency relationship is one of the following: no dependency, strong dependency, weak dependency; the feedback module 206 is configured to return the dependency relationship to the user;
  • the instruction receiving module 208 is configured to receive a data processing instruction sent by the user according to the dependency relationship, and the triggering module 210 is configured to trigger the data warehouse to execute the data processing instruction on the query data.
  • the dependency determining module specifically includes: a determining submodule for determining data to be queried according to the keyword; and a dependency generating submodule for generating a dependency of the data to be queried according to the metadata.
  • the metadata includes one or more of scheduling metadata, SQL execution log metadata, table structure metadata, synchronization center metadata, and timing task metadata.
  • the data processing apparatus further comprises: an instruction providing module, configured to provide the user with data processing instructions for the data to be queried according to the dependency relationship.
  • the data processing instructions are offline or changed.
  • the data processing apparatus in the embodiment of the present application may be implemented in a language such as java, jsp, or .net.
  • the downstream production task dependence and data consumption of the data warehouse's table or view are intricate. Establishing full coverage data impact analysis is essential for data production management, which can reduce work complexity, improve development efficiency, and ensure work quality.
  • the data development engineer can intuitively determine the dependency relationship between the table or the view to be processed and other data based on the device, thereby intuitively determining the influence range of the data processing instruction to be executed, And whether it can be processed and changed offline.
  • the data processing apparatus in the embodiment of the present application may provide a dependency to the user through the query module. Relationship query service, offline, change notification inquiry service, etc.
  • the data processing apparatus in the embodiment of the present application may integrate the scheduling metadata, the SQL execution log metadata, the table structure metadata, the synchronization center metadata, the timing task metadata, etc. through the dependency generation sub-module. To accurately and comprehensively analyze the dependencies between data and produce interface tables.
  • the data processing apparatus in the embodiment of the present application may perform function presentation based on the result of the metadata integration dependency, and provide an N-level impact query and presentation to the upstream and downstream.
  • the data processing apparatus in the embodiment of the present application can provide a one-click offline function for a table or a view that is not dependent on and used in the downstream, and can also provide a task for performing offline deletion on a task that is not dependent on the downstream. Or rename features such as backups.
  • the data processing apparatus in the embodiment of the present application may further provide a change notification function to the changed table or view, so that the data development engineer can use the dependency relationship to the downstream task owner of the changed table or view ( Owner) or the user sends a change notification email.
  • the user inputs a table or a name, sets a level, selects a dependency query upstream or downstream, and the data processing device invokes the metadata service to query the dependency result and displays it, and the user can determine based on the result.
  • the offline operation or the change notification is performed. If there is downstream or usage information, the offline operation cannot be performed; if the offline operation is selected, the data processing device triggers the data warehouse to physically delete or rename the table or view and correspondingly If the change is selected, the change description is triggered, and the change notification is triggered.
  • the system automatically sends a change email to the downstream task owner and the data engineer, including the change description and the change impact list.
  • the user after receiving the query condition input by the user, the user can determine and return a dependency relationship between the data to be queried and other data to the user; and the user can send a data processing instruction for the data to be queried according to the dependency relationship. Then, the data warehouse is triggered to execute the data processing instruction; thereby, the data in the data warehouse can be processed according to the dependency relationship, thereby avoiding waste of resources caused by not processing the data in the prior art, improving resource utilization efficiency of the data warehouse, and reducing The error probability of data processing improves the accuracy of data processing.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can be implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) in which computer usable program code is embodied.
  • the form of a computer program product includes but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

L'invention concerne un procédé et un dispositif de traitement de données pour un entrepôt de données. Le procédé consiste à : recevoir une condition d'interrogation entrée par un utilisateur, la condition d'interrogation comprenant un mot-clé de données à interroger (S102) ; déterminer, conformément au mot-clé, une relation de dépendance entre les données à interroger et d'autres données de l'entrepôt de données, la relation de dépendance étant l'une des suivantes : aucune dépendance, dépendance forte et faible dépendance (S104) ; renvoyer la relation de dépendance à l'utilisateur (S106) ; recevoir une instruction de traitement de données émise par l'utilisateur conformément à la relation de dépendance (S108) ; et déclencher l'entrepôt de données de sorte à exécuter l'instruction de traitement de données concernant les données à interroger (S110). Le procédé permet d'améliorer le rendement d'utilisation de ressources d'un entrepôt de données.
PCT/CN2016/083591 2015-06-04 2016-05-27 Procédé et dispositif de traitement de données d'entrepôt de données WO2016192583A1 (fr)

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CN113868253B (zh) * 2021-09-28 2024-04-23 中通服创立信息科技有限责任公司 一种数据关系捕获及大数据关系树构建方法
CN115470304A (zh) * 2022-08-31 2022-12-13 北京九章云极科技有限公司 一种特征因果仓库管理方法及系统
CN115470304B (zh) * 2022-08-31 2023-08-25 北京九章云极科技有限公司 一种特征因果仓库管理方法及系统

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