CN112988705B - Data middlebox construction method for enterprise-level production - Google Patents
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Abstract
The invention discloses a data middlebox construction method for enterprise-level production.
Description
Technical Field
The invention relates to the field of big data calculation, in particular to a data middlebox construction method integrating data development, test and online full link deployment.
Background
With the rapid development of information technologies such as cloud computing, internet of things, social media and the like, the data volume that enterprises need to process is increasing at an exponential level, and the sharply increasing data volume makes the maintenance and analysis of data by the enterprises more and more difficult. With the timeliness of data being more prominent, the Streaming characteristics of the data become more obvious, and more enterprise-level application scenarios, such as financial banking, internet of things, and the like, need to be deployed on Streaming computing platforms, such as Apache Flink, Apache Storm, J-Storm, Heron, Spark Streaming, and the like.
The Flink platform is currently applied by a plurality of enterprises at home and abroad, and although the Flink platform is innovative in architecture and theoretical model as a new generation big data calculation engine integrating flow processing and batch processing, the Flink platform has a plurality of defects in the actual business scene with huge data volume. For example, Flink only provides the running form of a Jar package at present, the support for the Flink SQL is still incomplete, no inter-job dependence exists, and the method cannot be directly used in complex production work of enterprises.
In order to fully exert the driving capability of large data in enterprise production, the concept of data center is generated in the conversion process of business and data. The enterprise data center station can be abstracted and understood as a service set with multiple multiplexing items and can also be integrated with multiple software services, various abstracted data capabilities of an enterprise are packaged on a logic level, foreground services and background services are connected, service deployment and data extraction transmission are frequently served, and the data center station bears the responsibility of providing data services for various services of the enterprise, so that the data center station is tightly bound with the service value and is one of important services on a value chain. Therefore, how to provide a data middlebox more suitable for enterprise-level production aiming at the current situation of enterprise data maintenance and processing is one of the problems to be solved at present.
Disclosure of Invention
In order to adapt to the deployment and use of most of the current enterprises on the Flink platform, the invention provides a set of complete data processing links, namely data middleboxes, which can be used for enterprise-level production, and the data processing links comprise data development, data service and data display; the method can meet the calculation task requirements of enterprises under various complex conditions and provide external API and data display requirements for the analyzed data.
The construction method of the data center platform mainly comprises three steps of constructing a data development module, constructing a data service module and constructing a data display module.
Wherein the data development module includes: the system comprises a FlinkSQL development module, an operation scheduling design module and an operation and maintenance monitoring module; the Flink SQL development module provides a Flink SQL development interface for a user and supports the development and debugging of the Flink SQL; the job scheduling design module allows a user to define execution dependence and scheduling sequence among a plurality of jobs; the operation and maintenance monitoring module is used for monitoring the operation process of the operation.
Wherein the data service module includes: a custom API generating module and a custom API registering module; the user-defined API generating module is used for generating an SQL query interface according to the data processing requirements of the user for the direct use of the user; the custom API registration module is used for registering the generated custom API and providing corresponding registration service for the user according to the user authority.
Wherein the data presentation module comprises: the system comprises a visual report display module and a dynamic large-screen display module; the visual report display module is used for providing the capability of displaying the visual report for the analyzed and processed data; and the dynamic large-screen display module is used for providing real-time dynamic large-screen display capability for the analyzed and processed data.
Furthermore, the Flink SQL development module provides a development interface for a user based on Codemirror, and supports development and version management of the same project engineering by multiple users; the user can submit the Flink job to the kubernets cluster in a PerJob mode, and the Flink job log is obtained based on Fillebeat, so that the user can conveniently provide the log of real time and/or history in the debugging process of the Flink SQL.
Further, different users can develop the version information locally, and the version information is uploaded to the server while the version information is stored locally.
Further, the job scheduling design module provides a visual job scheduling management function for a user based on a Quarz platform, wherein the execution sequence among different jobs can be displayed in a form of a task list or a form of a directed acyclic graph, and the user can change the execution sequence among the jobs in a mouse dragging mode and customize the execution times of the different jobs.
Further, the job scheduling design module is further used for checking the dependency relationship between the changed jobs when the execution sequence of the jobs is changed by the user, and prompting the user in the interface that the current change operation is invalid and restoring the execution sequence of the jobs before the change operation when the check fails.
Further, the job scheduling design module is further configured to submit the plurality of jobs to the kubernets cluster according to a defined execution order to implement automatic execution of the jobs.
Further, the operation and maintenance monitoring module is used for reporting the executed Flink operation to Prometheus and displaying the Flink operation to a user through a visual interface, so that the user can master the operation condition of the operation in real time.
Further, a custom API generation module allows a user to package data source connection used by an enterprise, a query plan is generated based on a user-defined Flink SQL statement of the Flink SQL development module, and different query plans and different data sources are combined to generate a custom API; when a user requests a certain custom API, the corresponding query plan can be obtained according to the API, the operation is executed on the corresponding data source, and the result is returned to the user.
Further, before providing a corresponding registration service for a user according to a user authority, the custom API registration module needs to establish a role set between the user set and the authority set, where roles and authorities have a one-to-one correspondence relationship, and when a user is given a certain role, the user can use the registration service in the authority range corresponding to the role.
Further, a visual report display module and a dynamic large screen display module acquire a job execution result from the kubernets cluster and display the job execution result to a user in a visual component mode; meanwhile, a secondary development function is supported, and a code editing interface is provided for a user, so that the user can carry out secondary development on the Flink SQL statement and update an execution result in a visual component in real time; the user can share the report generated in the visual report display module and set the report updating period.
The data middlebox obtained by the construction method greatly accelerates the production and development period of enterprises. The requirements of data acquisition, processing, use, visualization and the like are integrated in the data to realize the whole link. In addition, the use threshold of a user is lowered, even personnel who do not know research and development can define corresponding operation tasks through dragged visual operation in data service or data display, data support with stronger maneuverability is provided for market operators and company high-level leaders to make market decisions, market demand changes are responded faster, and business opportunities are acquired in time.
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FIG. 1 is a diagram of a data center table
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
In recent years, big data technology is widely concerned and applied by various industries, various traditional enterprises start to transform, and a data center station as a new data integration architecture also enters the public view. Fig. 1 shows a framework diagram in data, for enterprise-level applications, because data sources related to an enterprise production process are various, the data source in the data center needs to be compatible with structured, unstructured, and semi-structured data sources, and data access is performed by adopting Sqoop, flux, and Kafka, data storage is performed by adopting Hdfs, Hbase, and greenply, and data processing is performed based on Hadoop, Spark, Flink, and other technologies. And different data development and data application services are provided for different users at the upper layer. The data center platform framework realizes the integrated processing of acquisition, calculation, storage and processing of mass data and also realizes the unification of data standards. Meanwhile, the required model service, algorithm service, organization, flow, standard, specification, management system and the like required by the data center system construction in the data center platform construction process are closely connected with enterprise production to form large data assets at an enterprise level, the data service capability is realized through a data mining and analyzing tool, and effective guarantee is provided for innovation and production of enterprises.
Flink is an efficient general purpose big data distributed processing engine based on Java implementations. Flink handles all tasks (batch data as a data stream with limited boundaries) as a stream. The cost of open-source Flink programming development is high, the learning cost is high, the debugging is complex, the online period is long, and different development modes are adopted for each data source connection. Flink's SQL support is based on Apache Call implementing the SQL standard. Whether the input is a batch input (DataSet) or a stream input (DataStream), the queries specified in either interface have the same semantics and specify the same results. When the Flink is applied to the real-time processing of the SQL data, if the functions of data query, maintenance and the like are to be realized, a user needs to inherit various functions of the Flink for development, a large amount of codes need to be written by the user, and the realization is complex.
In order to solve the above problems, the present embodiment relates to a complete set of data processing links, i.e. data middleboxes, which can be used for enterprise-level production, and aims to improve the efficiency of enterprise development and application of large data in actual production at low cost. The method comprises the steps of data development, data service and data display; the method can meet the calculation task requirements of enterprises under various complex conditions and provide external API and data display requirements for the analyzed data. The architecture also comprises a set of complete strategy schemes such as metadata storage, data quality supervision, core data protection, data authority control and the like so as to ensure the safety of data quality specifications and data assets.
Specifically, the method for constructing the data center platform for enterprise-level production mainly comprises three steps of constructing a data development module, constructing a data service module and constructing a data presentation module.
Wherein the data development module includes: the system comprises a FlinkSQL development module, an operation scheduling design module and an operation and maintenance monitoring module; the Flink SQL development module provides a Flink SQL development interface for a user and supports the development and debugging of the Flink SQL; the job scheduling design module allows a user to define execution dependence and scheduling sequence among a plurality of jobs; the operation and maintenance monitoring module is used for monitoring the operation process of the operation.
Wherein the data service module includes: a custom API generating module and a custom API registering module; the user-defined API generating module is used for generating an SQL query interface according to the data processing requirements of the user for the direct use of the user; the custom API registration module is used for registering the generated custom API and providing corresponding registration service for the user according to the user authority.
Wherein the data presentation module comprises: the system comprises a visual report display module and a dynamic large-screen display module; the visual report display module is used for providing the capability of displaying the visual report for the analyzed and processed data; and the dynamic large-screen display module is used for providing real-time dynamic large-screen display capability for the analyzed and processed data.
Furthermore, the Flink SQL development module provides a development interface for a user based on Codemirror, and supports development and version management of the same project engineering by multiple users; the user can submit the Flink job to the kubernets cluster in a PerJob mode, and the Flink job log is obtained based on Fillebeat, so that the user can conveniently provide the log of real time and/or history in the debugging process of the Flink SQL.
In one embodiment, the version management provided by the Flink SQL development module is different from other version control systems (such as CVS, Subversion, Performance, Bazaar and the like), and most version control systems store file information in a file change list mode, and the information saved by text is regarded as a group of basic files and the difference accumulated by each file gradually over time. And the version management in the Flink SQL development module regards the text information as a snapshot and stores a corresponding snapshot index, if the text is not changed at the current time, the text entity at the current time does not need to be stored, and if the text at the current time is changed, the changed text entity is stored.
In addition, in the embodiment, most operations can be performed locally, and different users can develop and upload version information to the server locally. If the user can not connect with the Internet at present, the user can still submit the edited version and store the version locally until the version is uploaded to the server when the user has network connection. When a user needs to browse certain version information stored at the past time, the user can directly read the version information from the local without connecting with an external network server.
In one embodiment, the job scheduling design module provides a visualized job scheduling management function for a user based on a Quarz platform, wherein the execution sequence among different jobs can be shown in a form of a task list or a form of a directed acyclic graph, and the user can change the execution sequence among jobs in a mouse dragging mode and customize the execution times of different jobs. For example, three jobs, task a, task B, and task C, are listed in sequence in the task list, and the user may set parameter information such as start time, next trigger time, execution times, and the like of each task. The task A, the task B and the task C are executed in sequence under the default condition, a user can change the execution sequence of the tasks A-C in a mouse dragging mode, and the starting time and the next triggering time of each task after the execution sequence is changed are also changed. In addition, the user can also adjust the execution sequence between the jobs in a directed acyclic graph mode, and can switch between the task list and the directed acyclic graph according to specific needs.
In addition, the job scheduling design module is further used for checking the dependency relationship between the changed jobs when the execution sequence of the jobs is changed by the user, prompting the user that the current change operation is invalid in the interface when the check is failed, and recovering the execution sequence of the jobs before the change operation.
Further, the job scheduling design module is further configured to submit the plurality of jobs to the kubernets cluster according to a defined execution order to implement automatic execution of the jobs.
Further, the operation and maintenance monitoring module is used for reporting the executed Flink operation to Prometheus and displaying the Flink operation to a user through a visual interface, so that the user can master the operation condition of the operation in real time.
Further, a custom API generation module allows a user to package data source connection used by an enterprise, a query plan is generated based on a user-defined Flink SQL statement of the Flink SQL development module, and different query plans and different data sources are combined to generate a custom API; when a user requests a certain custom API, the corresponding query plan can be obtained according to the API, the operation is executed on the corresponding data source, and the result is returned to the user.
Further, before providing a corresponding registration service for a user according to a user authority, the custom API registration module needs to establish a role set between the user set and the authority set, where roles and authorities have a one-to-one correspondence relationship, and when a user is given a certain role, the user can use the registration service in the authority range corresponding to the role.
Further, a visual report display module and a dynamic large screen display module acquire a job execution result from the kubernets cluster and display the job execution result to a user in a visual component mode; meanwhile, a secondary development function is supported, and a code editing interface is provided for a user, so that the user can carry out secondary development on the Flink SQL statement and update an execution result in a visual component in real time; the user can share the report generated in the visual report display module and set the report updating period.
In one embodiment, the visual report display module realizes the secondary development function based on a Redash platform, and the dynamic large-screen display module realizes the secondary development function based on a DataV platform.
Through the scheme of the embodiment, the built data center for enterprise-level production greatly accelerates the development period of data value acquisition and utilization, provides more flexible data support for market operators and company high-level leaders to make market decisions, responds to market demand changes more quickly, and acquires business opportunities in time.
Finally, it should be noted that: it should be understood that the above examples are only for clearly illustrating the present application and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of this type are intended to be covered by the present invention.
Claims (4)
1. A data middle platform construction method for enterprise-level production is characterized by comprising three steps of constructing a data development module, constructing a data service module and constructing a data display module; wherein:
the data development module comprises: the system comprises a FlinkSQL development module, an operation scheduling design module and an operation and maintenance monitoring module; the Flink SQL development module provides a Flink SQL development interface for a user and supports the development and debugging of the Flink SQL; the job scheduling design module allows a user to define execution dependence and scheduling sequence among a plurality of jobs; the operation and maintenance monitoring module is used for monitoring the operation process of the operation;
the data service module comprises: a custom API generating module and a custom API registering module; the custom API generating module is used for generating an SQL query interface according to the data processing requirement of the user for the direct use of the user; the user-defined API registration module is used for registering the generated user-defined API and providing corresponding registration service for the user according to the user authority, before the step, a role set needs to be established between the user set and the authority set, roles and authorities have one-to-one correspondence, and after the user is endowed with a certain role, the user can use the registration service in the authority range corresponding to the role;
the data presentation module includes: the system comprises a visual report display module and a dynamic large-screen display module; the visual report display module is used for providing the capability of displaying a visual report for the analyzed and processed data; the dynamic large-screen display module is used for providing real-time dynamic large-screen display capability for the analyzed and processed data;
in the step of constructing the data development module, the SQL development module provides a development interface for a user based on Codemirror, and supports development and version management of the same project engineering by multiple users; the user can submit the Flink job to the kubernets cluster in a PerJob mode, and a Filelink job log is obtained based on Filebeat, so that the user can conveniently provide a real-time or historical log in the debugging process of the Flink SQL; different users can develop locally, and the version information is stored locally and uploaded to the server at the same time;
the job scheduling design module provides a visual job scheduling management function for a user based on a Quartz platform, wherein the execution sequence among different jobs is displayed in a form of a task list or a form of a directed acyclic graph, and the user can change the execution sequence among the jobs in a mouse dragging mode and self-define the execution times of the different jobs;
the job scheduling design module is also used for verifying the dependency relationship between the changed jobs when the execution sequence of the jobs is changed by a user, prompting the user that the current change operation is invalid in an interface when the verification is failed, and recovering the execution sequence of the jobs before the change operation; the job scheduling design module is also used for submitting a plurality of jobs to the kubernets cluster according to a defined execution sequence so as to realize automatic execution of the jobs.
2. The data console building method according to claim 1, wherein in the step of building a data development module, the job operation and maintenance monitoring module is configured to report a Flink job in execution to Prometheus, and to display the Flink job to a user through a visual interface, so that the user can grasp the operation condition of the job in real time.
3. The data console building method according to claim 1, wherein in the step of building the data service module, the custom API generating module allows a user to package data source connections used by an enterprise, a query plan is generated based on a user-defined Flink SQL statement of the Flink SQL development module, and different query plans are combined with different data sources to generate the custom API; when a user requests a certain custom API, the corresponding query plan can be obtained according to the API, the operation is executed on the corresponding data source, and the result is returned to the user.
4. The data center construction method according to claim 1, wherein in the data display module construction step, the visual report display module and the dynamic large screen display module acquire job execution results from a kubernets cluster and display the job execution results to a user in a visual component manner; meanwhile, a secondary development function is supported, and a code editing interface is provided for a user, so that the user can carry out secondary development on the Flink SQL statement and update an execution result in a visual component in real time; the user can share the report generated in the visual report display module and set the report updating period.
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| 【超强干货】通过数栈DataAPI数据中台可实现数据共享;数栈DTinsight;《https://www.jianshu.com/p/3233c73034c1》;20210304;第1页 * |
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