CN110597798B - Data detection method based on thread - Google Patents
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- 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
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Abstract
The invention discloses a data detection method based on thread, and relates to the technical field of data detection; the method comprises the steps of configuring a product line of a management platform where thread is located and a detection scheme of product line data, calling Spark service or SQL service to detect data according to the detection scheme of the product line data, detecting the quality of the data in various databases of each product line and feeding back detection results, realizing quality control in the data production process, and providing detection reports generated by the data exceeding early warning values to quality control personnel in time by setting relevant configuration rules and threshold conditions, analyzing reasons of errors, and finally improving the data quality and improving the satisfaction of customers.
Description
Technical Field
The invention discloses a data detection method based on thread, and relates to the technical field of data detection.
Background
With the popularization of digital terminal devices such as the internet and sensors, various data show an explosive exponential growth, and the collection and processing of the data are also important points required in the digital age. Because the internet data are disordered, the difficulty and complexity of data processing of operation and maintenance personnel are increased, and the obtained data cannot be effectively data mined in time, so that valuable contents are obtained, and the meaning of mass data generation is lost. It is important to detect the integrity and consistency of data prior to mining of the data processing. And at the same time, various indexes of the data are combed out, and an acceptable error range, namely an early warning value, is defined.
The invention provides a data detection method based on thread, which is used for configuring a product line of a management platform where the thread is located and a detection scheme of product line data, calling Spark service or SQL service to detect data according to the detection scheme of the product line data, carrying out quality detection on the data in various databases of each product line and feeding back detection results, realizing quality control in the data production process, and providing detection reports generated by data exceeding early warning values to quality control staff in time and analyzing reasons of errors by setting relevant configuration rules and threshold conditions, thereby finally improving the data quality and improving the satisfaction degree of customers.
Disclosure of Invention
The invention aims at the problems in the prior art, provides a data detection method based on thread, and utilizes thread communication to realize the quality detection of the integrity and consistency of the data in various databases of various product lines and feed back the detection result, and the method is used for quality control in the data production process, and simultaneously, quality control personnel give an analysis report to the data exceeding the early warning value and analyze the reasons of errors, thereby finally improving the data quality and improving the satisfaction degree of clients.
The specific scheme provided by the invention is as follows:
a data detection method based on thread is provided, a product line of a management platform where the thread is located and a detection scheme of product line data are configured, a thread calling interface is configured at the same time, and Spark service or SQL service is called to detect data according to the detection scheme of the product line data.
In the data detection method based on thread, an integrity and consistency detection scheme of product line data is configured, and corresponding detection rules of the integrity and consistency detection scheme are respectively configured.
In the data detection method based on thread, corresponding detection rules of an integrity and consistency detection scheme are recorded through a configuration table.
In the consistency detection scheme in the data detection method based on thread, the configuration table clusters are used for representing the same type of configuration table.
In the data detection method based on the thread, a Spark service detection task is started according to a detection scheme of product line data, a Spark corresponding interface is called through a thread calling interface, and the Spark task is generated to detect data in a Yarn-Cluster mode;
or starting the SQL service detection task according to the detection scheme of the product line data, calling the corresponding interface of the SQL service through the thread calling interface, and detecting the data by the SQL service.
A data detection system based on thread comprises a management platform where the thread is located,
and configuring a product line of a management platform where the thread is located and a detection scheme of product line data, and simultaneously configuring a thread calling interface, and calling Spark service or SQL service to perform data detection according to the detection scheme of the product line data.
The management platform in the data detection system based on thread configures an integrity and consistency detection scheme of product line data, and configures corresponding detection rules of the integrity and consistency detection scheme respectively.
The management platform in the data detection system based on the thread records the corresponding detection rules of the integrity and consistency detection scheme through the configuration table.
The management platform in the data detection system based on the thread records the corresponding detection rules of the integrity and consistency detection scheme through the configuration table.
The management platform in the data detection system based on the thread starts a Spark service detection task according to a detection scheme of product line data, and calls a Spark corresponding interface through a thread calling interface to generate the Spark task to detect data in a Yarn-Cluster mode;
or the management platform starts the SQL service detection task according to the detection scheme of the product line data, and the SQL service is used for detecting the data by calling the corresponding interface of the SQL service through the thread calling interface.
The invention has the advantages that:
the invention provides a data detection method based on thread, which is used for configuring a product line of a management platform where the thread is located and a detection scheme of product line data, calling Spark service or SQL service to detect data according to the detection scheme of the product line data, carrying out quality detection on the data in various databases of each product line and feeding back detection results, realizing quality control in the data production process, and providing detection reports generated by data exceeding early warning values to quality control staff in time and analyzing reasons of errors by setting relevant configuration rules and threshold conditions, thereby finally improving the data quality and improving the satisfaction degree of customers.
Drawings
FIG. 1 is a schematic diagram of the system operation flow of the present invention;
FIG. 2 is a schematic diagram of the operation of the Yarn-Cluster mode of Spark service;
FIG. 3 is a schematic diagram of a detection mode of the present invention;
FIG. 4 is a schematic diagram of a remote invocation framework of a management platform where thread is located in the present invention.
Detailed Description
The invention provides a data detection method based on thread, which is used for configuring a product line of a management platform where the thread is located and a detection scheme of product line data, simultaneously configuring a thread calling interface, and calling Spark service or SQL service for data detection according to the detection scheme of the product line data.
The invention also provides a data detection system based on the thread corresponding to the method, which comprises a management platform where the thread is located,
and configuring a product line of a management platform where the thread is located and a detection scheme of product line data, and simultaneously configuring a thread calling interface, and calling Spark service or SQL service to perform data detection according to the detection scheme of the product line data.
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
By using the method of the invention, the product line can be newly built and configured on the management platform where the thread is located, relevant basic information is filled in, database resources are selected or added, team members and responsible persons are selected, according to different authorities, not only the newly built product line can be configured and operated, but also other product lines participated in can be managed and configured in the management platform, meanwhile, a detection scheme for configuring the product line data, a configuration database and a detection rule are newly built, in order to meet diversified user requirements, the invention not only detects the integrity but also detects the consistency, and respectively configures the corresponding detection rules of the integrity and the consistency detection scheme,
after the detection scheme is established, a detection task can be started, the detection task is established according to the detection scheme of the product line data, parameters such as a detection period, an operation mode and the like are set, and the detection task can have five states, namely: the method is characterized in that the method is not started, is to be executed, is stopped in the execution, is completed, adopts a configuration detection scheme and a mode of setting the separate management of the task running period, is beneficial to the later operation of optimizing the task by a user, sets the five running states, is convenient for the user to know the task running state in time and is convenient for the next operation;
after the detection task is started, the state is changed from not started to be executed, the rear end puts the task into a container to be detected, the container is traversed regularly, and detection report calculation is carried out according to the implementation class corresponding to the SQL service or Spark service which is called through the interface configured by thread through configuration, so that a detection report is generated. According to different task types, the reports of the disposable task and the periodic task are distinguished, and the related report results can be checked after the task generates a detection report of a first period even if the periodic task is still in execution. The invention displays the detection report result, is not limited to only checking the completed detection task, and improves the working efficiency.
In the process, the corresponding detection rules of the integrity and consistency detection schemes are recorded through the configuration table, and the required field detection rules are configured in the detection rules before the integrity detection scheme is newly established, so that the integrity detection scheme provides rules of whether fields in the user configuration table can be empty or not, whether the fields can be repeated or not, and the like; the consistency detection scheme provides a relevant rule that whether data in different time periods of the same type of configuration table are consistent or not, in the configuration process, selection aiming at summarizing, counting or grouping of a plurality of fields is provided, so that a consistency data detection report is richer, in the newly built consistency detection scheme, an existing configuration table cluster is selected to represent one type of configuration table to be detected, if no existing configuration table cluster exists, a new table cluster can be built according to requirements, so that the user can conveniently select the same type of configuration table, the configuration table cluster is newly added, the user experience is improved, and meanwhile, the probability of error reporting when the different types of tables are selected is reduced.
In the process, the thread of the management platform defines the data type and the service through interface definition languages (interface definition language, IDL), and a thread interface definition file generates a thread target language by a thread code compiler.
In the above process, the Spark service includes configuration information such as an implementation class of a corresponding interface in the thread interface item and a port for providing a call, in order to improve the operation rate and optimize the user experience, a Spark task generated by each detection task is submitted to a yan Cluster for operation, and when the service is called, the item for generating a detection report is specifically calculated and analyzed by Spark according to a configuration analysis task, and a Cluster environment required by the related submitting of the Spark task, namely, a yan-Cluster mode for producing the environment submits the Spark task, and referring to fig. 2, because a machine where a Driver for submitting the task is located is randomly selected, a phenomenon of flow surge of a certain machine network card is effectively avoided.
In the process, the SQL service also comprises the implementation class of the corresponding interface in the thread interface project and configuration information such as a port for providing calling, and the SQL service obviously improves the SQL query efficiency of small data volume. The method utilizes thread communication to realize detection of data in the multi-class databases of each product line through Spark analysis submitting task for declustering by using a Yarn-cluster mode operation or SQL analysis and feeds back detection results. The integrity and consistency detection of the data of the multiple data sources are realized, the data detection of different product lines can be classified according to the respective product lines, different timing tasks are set, and corresponding detection reports are generated.
The same effect can be achieved by using the system, wherein the management platform of the system can be used as a client, the Spark service is used as a service end, and the SQL service is used as another service end to jointly realize the whole data detection service.
The user can build and configure product lines on a management platform where the thread is located, fill in relevant basic information and select or add database resources, select team members and responsible persons, according to different user rights, can configure and operate the product lines built by the user, can also manage and configure other product lines participated in the management platform, and simultaneously build a detection scheme for configuring product line data, a configuration database and detection rules, in order to meet diversified user requirements, the invention not only detects the integrity but also detects the consistency, and respectively configures the corresponding detection rules of the integrity and the consistency detection scheme,
after the detection scheme is established, a detection task can be started, the detection task is established according to the detection scheme of the product line data, parameters such as a detection period, an operation mode and the like are set, and the detection task can have five states, namely: the method is characterized in that the method is not started, is to be executed, is stopped in the execution, is completed, adopts a configuration detection scheme and a mode of setting the separate management of the task running period, is beneficial to the later operation of optimizing the task by a user, sets the five running states, is convenient for the user to know the task running state in time and is convenient for the next operation;
after the detection task is started, the state is changed from not started to be executed, the rear end puts the task into a container to be detected, the container is traversed regularly, and detection report calculation is carried out according to the implementation class corresponding to the SQL service or Spark service which is called through the interface configured by thread through configuration, so that a detection report is generated. According to different task types, the reports of the disposable task and the periodic task are distinguished, and the related report results can be checked after the task generates a detection report of a first period even if the periodic task is still in execution. The invention displays the detection report result, is not limited to only checking the completed detection task, and improves the working efficiency.
In the process, a user can record corresponding detection rules of the integrity and consistency detection schemes through the configuration table in the management platform, and the required field detection rules are configured in the detection rules before the integrity detection schemes are newly established, and the integrity detection schemes provide rules of whether fields in the user configuration table can be empty, whether the fields can be repeated and the like; the consistency detection scheme provides a relevant rule that whether data in different time periods of the same type of configuration table are consistent or not, in the configuration process, selection aiming at summarizing, counting or grouping of a plurality of fields is provided, so that a consistency data detection report is richer, in the newly built consistency detection scheme, an existing configuration table cluster is selected to represent one type of configuration table to be detected, if no existing configuration table cluster exists, a new table cluster can be built according to requirements, so that the user can conveniently select the same type of configuration table, the configuration table cluster is newly added, the user experience is improved, and meanwhile, the probability of error reporting when the different types of tables are selected is reduced.
In the process, the thread of the management platform defines the data type and the service through an interface definition language (interface definition language, IDL), a thread code compiler generates a thread target language in a thread interface definition file, the system uses Java codes, the generated codes are responsible for realizing an RPC protocol layer and a transmission layer, the system defines two interfaces, one is a service interface for analyzing by Spark according to configuration content, and the other is an SQL analysis service interface for converting a data detection task into an SQL statement query database, so that a user can select the data according to the requirement, the memory use performance is improved, and the condition of resource waste is avoided.
In the above process, the Spark service includes configuration information such as an implementation class of a corresponding interface in the thread interface item and a port for providing a call, in order to improve the operation rate and optimize the user experience, a Spark task generated by each detection task is submitted to a yan Cluster for operation, and when the service is called, the item for generating a detection report is specifically calculated and analyzed by Spark according to a configuration analysis task, and a Cluster environment required by the related submitting of the Spark task, namely, a yan-Cluster mode for producing the environment submits the Spark task, and referring to fig. 2, because a machine where a Driver for submitting the task is located is randomly selected, a phenomenon of flow surge of a certain machine network card is effectively avoided.
In the process, the SQL service also comprises the implementation class of the corresponding interface in the thread interface project and configuration information such as a port for providing calling, and the SQL service obviously improves the SQL query efficiency of small data volume. The system provided by the invention utilizes thread communication to realize detection of data in the multi-class databases of each product line through Spark analysis submitting task for declustering by using a Yarn-cluster mode operation or SQL analysis and feed back detection results. The integrity and consistency detection of the data of the multiple data sources are realized, the data detection of different product lines can be classified according to the respective product lines, different timing tasks are set, and corresponding detection reports are generated.
The above-described embodiments are merely preferred embodiments for fully explaining the present invention, and the scope of the present invention is not limited thereto. Equivalent substitutions and modifications will occur to those skilled in the art based on the present invention, and are intended to be within the scope of the present invention. The protection scope of the invention is subject to the claims.
Claims (4)
1. A data detection method based on thread is characterized in that a product line of a management platform where the thread is positioned and a detection scheme of product line data are configured, a thread calling interface is configured at the same time, spark service or SQL service is called for data detection according to the detection scheme of the product line data,
wherein, product lines, configuration databases and detection rules are newly built and configured on a management platform where the thread is located, corresponding detection rules of an integrity and consistency detection scheme are recorded through a configuration table,
before the integrity detection scheme is newly established, the required field detection rules are configured in the detection rules, the integrity detection scheme provides rules for whether fields in a user configuration table can be empty or not and whether the fields in the user configuration table can be repeated or not,
the consistency detection scheme provides a relevant rule that a user configures whether the data of different time periods of the same type of configuration table are consistent, in the configuration process, a selection for summarizing, counting or grouping a plurality of fields is provided, so that the consistency data detection report is richer, in the consistency detection scheme, the existing configuration table cluster is selected to represent the type of configuration table to be detected, if no configuration table cluster exists, the new table cluster can be established according to the requirement,
defining data type and service by using thread through interface definition language, defining two interfaces, one is service interface using Spark analysis according to configuration content, the other is SQL analysis service interface converting data detection task into SQL statement query database,
after the detection scheme is newly built and configured, a detection task is newly built according to the detection scheme, and the detection task has five states, namely: and after the detection task is started, the state is changed from the non-starting state to the to-be-executed state, the detection task is put into a to-be-detected container, the detection container is traversed at regular time, and the SQL service or the Spark service corresponding implementation class is called according to the configuration through an interface defined by the thread to carry out detection report calculation, so as to generate a detection report.
2. The data detection method based on thread according to claim 1, wherein a Spark service detection task is started according to a detection scheme of product line data, a Spark corresponding interface is called through a thread calling interface, and the Spark task is generated to detect data in a Yarn-Cluster mode;
or starting the SQL service detection task according to the detection scheme of the product line data, calling the corresponding interface of the SQL service through the thread calling interface, and detecting the data by the SQL service.
3. A data detection system based on thread is characterized in that the system is applied to a management platform where the thread is located, a product line of the management platform where the thread is located and a detection scheme of product line data are configured through the data detection system, a thread calling interface is configured at the same time, spark service or SQL service is called according to the detection scheme of the product line data to carry out data detection,
wherein, product lines, configuration databases and detection rules are newly built and configured on a management platform where the thread is located, corresponding detection rules of an integrity and consistency detection scheme are recorded through a configuration table,
before the integrity detection scheme is newly established, the required field detection rules are configured in the detection rules, the integrity detection scheme provides rules for whether fields in a user configuration table can be empty or not and whether the fields in the user configuration table can be repeated or not,
the consistency detection scheme provides a relevant rule that a user configures whether the data of different time periods of the same type of configuration table are consistent, in the configuration process, a selection for summarizing, counting or grouping a plurality of fields is provided, so that the consistency data detection report is richer, in the consistency detection scheme, the existing configuration table cluster is selected to represent the type of configuration table to be detected, if no configuration table cluster exists, the new table cluster can be established according to the requirement,
defining data type and service by using thread through interface definition language, defining two interfaces, one is service interface using Spark analysis according to configuration content, the other is SQL analysis service interface converting data detection task into SQL statement query database,
after the detection scheme is newly built and configured, a detection task is newly built according to the detection scheme, and the detection task has five states, namely: and after the detection task is started, the state is changed from the non-starting state to the to-be-executed state, the detection task is put into a to-be-detected container, the detection container is traversed at regular time, and the SQL service or the Spark service corresponding implementation class is called according to the configuration through an interface defined by the thread to carry out detection report calculation, so as to generate a detection report.
4. The data detection system based on thread according to claim 3, wherein a Spark service detection task is started according to a detection scheme of product line data, a Spark corresponding interface is called through a thread calling interface, and the Spark task is generated to detect data in a Yarn-Cluster mode;
or the management platform starts the SQL service detection task according to the detection scheme of the product line data, and the SQL service is used for detecting the data by calling the corresponding interface of the SQL service through the thread calling interface.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106777101A (en) * | 2016-12-14 | 2017-05-31 | 深圳天源迪科信息技术股份有限公司 | Data processing engine |
| CN106953847A (en) * | 2017-02-27 | 2017-07-14 | 江苏徐工信息技术股份有限公司 | A kind of cross-platform real-time processing method of big data based on thrift |
| CN108846076A (en) * | 2018-06-08 | 2018-11-20 | 山大地纬软件股份有限公司 | The massive multi-source ETL process method and system of supporting interface adaptation |
| CN109656917A (en) * | 2018-12-18 | 2019-04-19 | 深圳前海微众银行股份有限公司 | Data detection method, device, equipment and the readable storage medium storing program for executing of multi-data source |
| CN109902084A (en) * | 2019-02-27 | 2019-06-18 | 浪潮软件集团有限公司 | A kind of system and method for full-automatic detection and the analysis quality of data |
| CN109933578A (en) * | 2019-03-21 | 2019-06-25 | 浪潮软件集团有限公司 | A kind of configurable automated data detection method for quality and system |
| CN109933581A (en) * | 2019-03-01 | 2019-06-25 | 武汉达梦数据库有限公司 | A kind of data quality checking method and system |
| CN109993439A (en) * | 2019-04-02 | 2019-07-09 | 山东浪潮云信息技术有限公司 | A kind of quality determining method based on government data |
-
2019
- 2019-09-17 CN CN201910873984.7A patent/CN110597798B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106777101A (en) * | 2016-12-14 | 2017-05-31 | 深圳天源迪科信息技术股份有限公司 | Data processing engine |
| CN106953847A (en) * | 2017-02-27 | 2017-07-14 | 江苏徐工信息技术股份有限公司 | A kind of cross-platform real-time processing method of big data based on thrift |
| CN108846076A (en) * | 2018-06-08 | 2018-11-20 | 山大地纬软件股份有限公司 | The massive multi-source ETL process method and system of supporting interface adaptation |
| CN109656917A (en) * | 2018-12-18 | 2019-04-19 | 深圳前海微众银行股份有限公司 | Data detection method, device, equipment and the readable storage medium storing program for executing of multi-data source |
| CN109902084A (en) * | 2019-02-27 | 2019-06-18 | 浪潮软件集团有限公司 | A kind of system and method for full-automatic detection and the analysis quality of data |
| CN109933581A (en) * | 2019-03-01 | 2019-06-25 | 武汉达梦数据库有限公司 | A kind of data quality checking method and system |
| CN109933578A (en) * | 2019-03-21 | 2019-06-25 | 浪潮软件集团有限公司 | A kind of configurable automated data detection method for quality and system |
| CN109993439A (en) * | 2019-04-02 | 2019-07-09 | 山东浪潮云信息技术有限公司 | A kind of quality determining method based on government data |
Non-Patent Citations (1)
| Title |
|---|
| 吴宏杰 ; .借助大数据和云计算技术挖掘计量数据增值价值.中国计量.2017,(第 12 期),27-32. * |
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Effective date of registration: 20230727 Address after: 214125 1101, 1102, 1103, 1104, 1105, 1106, 1107 and 1108, No. 15, first financial street, Wuxi Economic Development Zone, Wuxi City, Jiangsu Province Applicant after: Chaozhou Zhuoshu Big Data Industry Development Co.,Ltd. Address before: Room 3110, S01 / F, Inspur building, 1036 Inspur Road, hi tech Zone, Jinan City, Shandong Province 250100 Applicant before: Shandong Aicheng Network Information Technology Co.,Ltd. |
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