CN110019145B - Multi-environment cascading method and device for big data platform - Google Patents
Multi-environment cascading method and device for big data platform Download PDFInfo
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Abstract
The invention discloses a multi-environment cascading method and device for a big data platform. The method for multi-environment cascading of the big data platform comprises the following steps: creating a multi-level environment; configuring an environment cascade relation; newly building a data processing object in a lower-level environment, and verifying the data processing object; submitting and publishing the data processing object in the subordinate environment if the data processing object is verified; and auditing the issued data processing objects, and if the auditing is passed, cascading and mapping the same data processing objects in an upper level environment of the lower level environment according to the environment cascading relationship.
Description
Technical Field
The present invention relates to computer technology, and in particular to a method and system for multi-environment cascading of large data platforms.
Background
Big data technologies are currently in wide use. There are already a variety of big data processing tools, such as distributed computing framework Hadoop, data warehouse tool Hive, distributed system Spark based on memory computing, large-scale graph data computing platform Giraph, etc. With the aid of a wide variety of computing platforms, data developers create various types of objects daily to process, analyze data, and it is desirable that objects created at one time can be reused multiple times in different environments. Therefore, a method of multi-level environment cascading is urgently needed to uniformly distribute and multiplex objects.
The prior art has already presented solutions to the above-mentioned problems, such as methods based on the association of the development environment and the production environment, and methods based on the export and import of object packages.
However, the prior art solutions still have drawbacks. Because only the association between development and production environment is considered, the business requirements of data developers in a test scene cannot be met, and objects cannot be copied to all the same other production environments. In addition, the method of exporting and importing the object package is to create a batch of data processing tasks or objects in one platform or environment. If one wants to migrate these tasks or objects to other environments or platforms, they can only be downloaded and exported as files in their entirety and then uploaded for import and use. This makes it impossible to introduce the information into a plurality of environments at a time, and if a certain environment is faulty, deletion and recovery may need to be performed in all the environments one by one, and effective version management is impossible.
Disclosure of Invention
According to one aspect of the invention, a method of multi-environment cascading of a big data platform comprises: creating a multi-level environment; configuring an environment cascade relation; newly building a data processing object in a lower-level environment, and verifying the data processing object; submitting and publishing the data processing object in the subordinate environment if the data processing object is verified; and auditing the issued data processing objects, and if the auditing is passed, cascading and mapping the same data processing objects in an upper level environment of the lower level environment according to the environment cascading relationship.
The environment cascade relationship includes a development environment and a production environment, the subordinate environment being the development environment, and the production environment being a superordinate of the development environment.
The environment cascade relation includes a development environment, a test environment, a gray scale environment and a production environment in sequence from the lowest level to the highest level, and the issued data processing object is verified at each level in the cascade mapping process from the lowest level to the highest level.
The environment cascade relation includes an outsourced development environment, an internal development environment, a test environment, a gray scale environment and a production environment in order from the lowest level to the highest level, the issued data processing object is verified at each level in a cascade mapping process from the lowest level to the highest level, and the method further includes: and implementing resource authority control so as to perform isolated management on the data in the big data platform.
The environment cascade relation further includes cluster configuration information for each level of environment in the multi-level environment. The step of configuring the environment cascade relation is performed in a visual interface. The environment cascade relation is stored in a tree-shaped data structure. The data processing object includes: tasks, scripts, resources, functions. The submitting operation comprises the step of putting the verified data processing object into a to-be-issued list. The publishing operation includes selecting a set of related data processing objects to uniformly package to create a publishing package. Different levels of the multi-level environment correspond to different clusters of storage computations. Network isolation is implemented between the different storage compute clusters. The cascade mapping operation is implemented in a cloud environment. The cascade mapping operation is to cascade map the data processing object from a public cloud to a private cloud, a private cloud or a local machine room.
According to one aspect of the invention, a device for multi-environment cascading of a big data platform comprises: means for creating a multi-level environment; a module for configuring an environment cascade relationship; a module for creating a new data processing object in the next level environment and verifying the data processing object; means for submitting and publishing the data processing object in the subordinate environment if the data processing object is validated; and a module for auditing the issued data processing objects and, if the audit is passed, cascading mapping the same data processing objects in a superior environment of the subordinate environment according to the environment cascading relationship.
The environment cascade relationship includes a development environment and a production environment, the subordinate environment being the development environment, and the production environment being a superordinate of the development environment.
The environment cascade relation includes a development environment, a test environment, a gray scale environment and a production environment in sequence from the lowest level to the highest level, and the issued data processing object is verified at each level in the cascade mapping process from the lowest level to the highest level.
The environment cascade relation includes an outsourced development environment, an internal development environment, a test environment, a gray scale environment and a production environment in order from the lowest level to the highest level, the issued data processing object is verified at each level in a cascade mapping process from the lowest level to the highest level, and the apparatus further includes: and the module is used for implementing resource authority control so as to perform isolated management on the data in the big data platform.
The environment cascade relation further includes cluster configuration information for each level of environment in the multi-level environment. The module for configuring the environment cascade relation is operated in a visual interface. The environment cascade relation is stored in a tree-shaped data structure. The data processing object includes: tasks, scripts, resources, functions. The module for submitting and publishing the data processing object in the subordinate environment is further configured to place the verified data processing object in a to-be-published list if the data processing object is verified. The means for submitting and publishing the data processing object in the subordinate environment is further configured to select a set of related data processing objects to uniformly package to create a publishing package if the data processing object is validated. Different levels of the multi-level environment correspond to different clusters of storage computations. Network isolation can be implemented between the different storage compute clusters. The cascade mapping function of modules for cascade mapping the same data processing object in an upper level environment of the lower level environment can be implemented in a cloud environment according to the environment cascade relationship if the published data processing object is approved by the audit. The cascade mapping function is to cascade map the data processing object from a public cloud to a private cloud, a private cloud or a local machine room.
One aspect of the present invention discloses a computer readable medium having stored thereon computer readable instructions which, when executed by a computer, are capable of performing the method of multi-environment cascading of a big data platform as described above.
Because the invention adopts a multi-stage environment, the invention can simultaneously process under a multi-layer environment, and the environment is similar to that of the following: development environment, test environment, gray scale environment, production environment A, production environment B, etc. The development environment object may be published for all upper level environments.
The invention provides a multistage environment cascading method. The method can rapidly release multiplexing verification through the mapping objects among the environments, and can cascade and use different cloud environments. When a certain object is newly built under a certain lower-level environment, the object can be quickly released to all the upper-level environments thereof through cascade mapping, and the isolation among the environments is ensured. Embodiments of the present invention also support dynamic addition and deletion of multi-level environments.
Therefore, the embodiment of the invention solves the problem of rapid multiplexing of objects (tasks, scripts, resources, functions and the like) in a multi-level environment cascade relation and different environments of a large data platform.
Drawings
FIG. 1 illustrates a multi-level environment architecture diagram according to an embodiment of the present invention.
FIG. 2 illustrates a multi-level environment service architecture diagram according to an embodiment of the present invention.
FIG. 3 illustrates a flow diagram for a custom multi-level environment according to an embodiment of the present invention.
Fig. 4 illustrates a heterogeneous network environment cascade diagram according to an embodiment of the present invention.
Detailed Description
The content of the invention will now be discussed with reference to a number of exemplary embodiments. It is to be understood that these examples are discussed only to enable those of ordinary skill in the art to better understand and thus implement the teachings of the present invention, and are not meant to imply any limitations on the scope of the invention.
As used herein, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to. The term "based on" is to be read as "based, at least in part, on". The terms "one embodiment" and "an embodiment" are to be read as "at least one embodiment". The term "another embodiment" is to be read as "at least one other embodiment".
The embodiment of the invention relates to a user-defined multi-level environment architecture which isolates resources in different environments and can guarantee data security. As shown in fig. 1, the architecture includes four layers: the system comprises a user layer, a gateway layer, a service logic layer and a storage calculation layer.
● user layer: the user layer is a visualization interface layer, and on the interface, a user (for example, a developer) can configure the multi-level environment and create objects (resources, functions, scripts, tasks) through simple operations.
● gateway layer: when a front end (e.g. browser) has a user to send a request (the content of the request is usually some parameter information, such as a user name, a password, etc.), service distribution is performed by a gateway layer (service distribution refers to distributing the request sent by each browser to different back-end servers for execution). For example, the service distribution may be performed using an open source rpc (remote Procedure call) framework DUBBO, in which a plurality of backend servers are registered with a gateway platform through a zookeeper.
And a service logic layer: the business logic layer is responsible for processing possibly complex business logic (the business logic refers to a process for processing various operations), instantiates the tasks, and submits the instances of the tasks to the bottom layer storage for running. The functions of the business logic layer comprise the functions related to business requirements, such as the establishment of business rules, the realization of business processes and the like. Where each time a task is run a new instance is created. The normal operation of the example can go through three stages of waiting to run, running and ending. For example, for task A, the instance IDs generated by two runs are T _630_20180301115903046_1 and T _630_20180301120009801_1, respectively.
● store the computation layer: the storage calculation layer is used for realizing data storage and data calculation functions. Generally, the storage computing layer uses hadoop clusters as main storage computing clusters, but the platform of the embodiment of the invention supports various types of data sources as storage computing clusters (for example, MySQL, greenplus, etc.).
The partitioning of a common environment according to various embodiments of the present invention will be exemplarily explained with reference to fig. 2, and an operational flowchart for the entire multi-environment cascade usage will be exemplarily described with reference to fig. 3.
In a multi-level environment, the bottom-layer clusters can be isolated from each other to ensure data security; the bottom-layer cluster can be configured with omnibearing data authorities to allocate different data authorities to different personnel. The implementation of the invention ensures that tasks can be multiplexed among different environments, realizes that a data processing object (comprising tasks, scripts, resources, functions and the like) is created once and can be run for multiple times, thus greatly simplifying the work of data developers (for example, saving 80% of the working time). In embodiments of the present invention, the clusters of the respective environments may be in different clouds, for example: the development cluster is on Ariiyun, the test environment is in Hua Yun, and the production cluster is stored in a local machine room. After the task created by the development environment is successfully operated, the data developer can issue the task to the test and production environment, so that the object multiplexing of the cross-network environment can be realized.
The multi-level environment service architecture of the embodiment of the invention is shown in fig. 2, can realize the control and isolation of environment resources and authorities on a big data platform, and can enable a plurality of developers to jointly develop on line, thereby improving the development efficiency.
According to the embodiment of the invention, different enterprises can flexibly and custom configure the own environment modes. Common modes are single mode (production), normal mode (development, production), strict mode (development, test, gray scale, production), outsourced mode (outsourced development, internal development, test, gray scale, production), etc.:
● Single mode: only a production environment is provided, and a data developer directly generates a data processing object in the environment.
● conventional mode: the data processing system comprises a development environment and a production environment, and a data developer generates a data processing object in the development environment and releases the data processing object to the production environment.
● strict mode: the method comprises four environments of development, test, gray scale and production, develops and creates data processing objects strictly according to a project development flow, and releases the data processing objects to a superior environment so as to verify the data processing objects one by one.
● outsourcing mode: the method comprises five environments of outsourcing development, internal development, testing, gray level and production, wherein resource authority control is added, and data can be isolated and managed.
A multi-environment cascading method according to an embodiment of the present invention is shown in fig. 3, and includes the steps of:
(1) and creating a multi-level environment, and customizing the cluster configuration of each level of environment. The cluster configuration information may be determined according to actual situations, and mainly includes configuration and database information of the cluster, for example:
the development environment cluster configuration comprises the following steps: hdfs address 127.0.0.1:8020, yarn scheduling address 127.0.0.1:8088, scheduling queue dev, and database demo _ dev. The test environment cluster configuration comprises: the hdfs address is test:8020, the yarn scheduling address is test:8088, the scheduling queue is test, and the database is demo _ test.
(2) And (4) self-defining configuration environment cascade relation.
The embodiment of the invention is to configure an environment cascade relation in a visual interface, for example: the upper level of the development environment is a test environment, and the upper level of the test environment is a grayscale environment. The data structure of the reaction environment cascade relation can be a tree structure, and the upper level environment of a certain level environment can be provided with a plurality of or more than one, but the cascade connection can not be carried out in a cross-level manner.
(3) Data developers create data processing objects (tasks, scripts, resources, functions, etc.) in the lowest level environment and run tests to verify the correctness of the objects.
Wherein, the verified content is the object of data processing. For example, assuming that the amount of a certain type of complaint about a cell is to be resolved from a large amount of data on property management, such as "toilet in my home leaks", "home is out of doors", etc., some functions or SQL statements (data processing objects) need to be created; and then, the functions or the SQL statements are executed on the original data, and the correctness of the functions or the SQL statements is verified according to the execution result.
(4) And submitting the verified data processing object, and creating a publishing packet by a data developer.
The submitting data developer puts the data processing objects of the tested and verified data into a list to be issued, and then selects a group of data processing objects related to the service to be uniformly packaged to create an issuing package. The operation and maintenance personnel release the set of tasks to the upper level environment by reviewing the release package.
(5) After the release administrator passes the release audit, the superior cascade environment has the same data processing object, and the same data processing object can be directly operated in the cluster environment. Wherein the operation and maintenance personnel release the set of tasks to the superior environment by reviewing the release package. The whole issuing process comprises the following steps: and creating a publishing packet, reviewing and checking the publishing packet, and copying the tasks after the approval. After the auditing is passed, a task with the same content is copied from the development environment to the upper level environment of the cascade mapping, wherein the names, the hierarchies, the dependency relationships, the configuration relationships and other contents of the tasks are the same. In this disclosure, cascade mapping refers to: for an object in a certain environment, the upper environment of the environment is made to have an operation of the same object.
According to one embodiment of the invention, a cascade mapping relationship may be configured in a visualization interface, for example: the subordinate environment of the development environment can be selected as a test environment, and the development environment and the test environment are mapped in a cascading manner; the data structure of the cascade mapping is a tree structure, and one or more subordinate environments of a certain level of environment can be provided, but the cascade mapping can not be carried out across levels.
One embodiment of the invention supports storage computing clusters in heterogeneous network environments, and can correspond to different storage computing clusters in different environments. An embodiment of a heterogeneous network environment architecture is shown in fig. 4. As can be seen from fig. 4, the storage computing cluster of the development environment is deployed in the ariloc environment, and the tasks of data processing created in the development environment are run in the ariloc cluster machine. After the test verification of the task is passed, the task is released (also understood as a task copy) into the test environment. The test environment is clustered in Hua cloud, and the task content can normally run in the Hua cloud clustered environment. And finally, the task is issued to the gray level environment and then issued to the production environment from the gray level environment, and the cluster used by the gray level environment and the production environment is deployed in a local machine room. The technical type selection of the cluster A, the cluster B and the cluster C must be the same. For example. If the cluster A of the development environment uses a hadoop and spark script environment, the cluster B of the test environment, the cluster C of the gray scale and production environment all need to use the hadoop and spark script environment.
In addition, network isolation is performed among the clusters, and the clusters do not communicate with each other. The physical storage and replication of tasks is accomplished using a business library.
The embodiment of the invention can support the self-defined multistage environment cascade connection and the resource isolation between the environments. Furthermore, embodiments of the invention may support the publication of objects (tasks, resources, functions, etc.) between multi-level environments. In addition, the embodiment of the invention can further support cascading release in various cloud environments, and can be released from a public cloud to a private cloud, a proprietary cloud or a local machine room in a cascading manner.
In the present disclosure, the expression "user" refers to "user".
The method and apparatus of the embodiments of the present invention may be implemented as pure software (e.g., a software program written in Java language), as pure hardware (e.g., a dedicated ASIC chip or FPGA chip) as needed, or as a system combining software and hardware (e.g., a firmware system storing fixed code).
Another aspect of the invention is a computer-readable medium having computer-readable instructions stored thereon that, when executed, perform a method of embodiments of the invention.
While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The scope of the claimed subject matter is limited only by the attached claims.
Claims (29)
1. A method of multi-environment cascading of a big data platform, comprising:
creating a multi-level environment;
configuring an environment cascade relation;
newly building a data processing object in a lower-level environment, and verifying the data processing object;
submitting and publishing the data processing object in the subordinate environment if the data processing object is verified; and
and auditing the issued data processing objects, and if the auditing is passed, cascading and mapping the same data processing objects in an upper level environment of the lower level environment according to the environment cascading relationship.
2. The method of claim 1, wherein the environment cascade relationship comprises a development environment and a production environment, the subordinate environment being the development environment, the production environment being a superordinate of the development environment.
3. The method of claim 1, wherein the environmental cascade relationship comprises, in order from lowest level to highest level, a development environment, a test environment, a grayscale environment, and a production environment, and
in the cascade mapping process from the lowest stage to the highest stage, the issued data processing object is verified at each stage.
4. The method of claim 1, wherein the environmental cascade relationship comprises, in order from lowest level to highest level, an outsourced development environment, an internal development environment, a test environment, a grayscale environment, and a production environment,
in a cascaded mapping process from a lowest level to a highest level, the issued data processing object is validated at each level, and the method further comprises:
and implementing resource authority control so as to perform isolated management on the data in the big data platform.
5. The method of claim 1, wherein the environment cascade relationship further comprises cluster configuration information for each level of the multi-level environment.
6. The method of claim 1, wherein the step of configuring the environment cascade relationship is performed in a visualization interface.
7. The method of claim 1, wherein the environment cascade relationship is stored in a tree-like data structure.
8. The method of claim 1, wherein the data processing object comprises: tasks, scripts, resources, functions.
9. The method of claim 1, wherein the commit operation comprises placing the validated data processing object into a to-be-published list.
10. The method of claim 9, wherein the publishing operation comprises selecting a set of related data processing objects to uniformly package to create a publishing package.
11. The method of claim 1, wherein different levels of the multi-level environment correspond to different storage computing clusters.
12. The method of claim 11, wherein network isolation is enforceable between the different storage computing clusters.
13. The method of claim 1, wherein the cascaded mapping operation is implemented in a cloud environment.
14. The method of claim 13, wherein the cascade mapping operation is cascade mapping the data processing object from a public cloud to a private cloud, or a local room.
15. An apparatus for multi-environment cascading of big data platforms, comprising:
means for creating a multi-level environment;
a module for configuring an environment cascade relationship;
a module for creating a new data processing object in the next level environment and verifying the data processing object;
means for submitting and publishing the data processing object in the subordinate environment if the data processing object is validated; and
a module for auditing the issued data processing objects and, if the auditing is passed, cascading mapping the same data processing objects in an upper level environment of the lower level environment according to the environment cascading relationship.
16. The apparatus of claim 15, wherein the environment cascade relationship includes a development environment and a production environment, the subordinate environment being the development environment, the production environment being a superordinate of the development environment.
17. The apparatus of claim 15, wherein the environmental cascade relationship comprises, in order from lowest level to highest level, a development environment, a test environment, a grayscale environment, and a production environment, and
in the cascade mapping process from the lowest stage to the highest stage, the issued data processing object is verified at each stage.
18. The apparatus of claim 15, wherein the environmental cascade relationship comprises, in order from lowest level to highest level, an outsourced development environment, an internal development environment, a test environment, a grayscale environment, and a production environment,
in a cascaded mapping process from a lowest level to a highest level, the issued data processing object is validated at each level, and the apparatus further comprises:
and the module is used for implementing resource authority control so as to perform isolated management on the data in the big data platform.
19. The apparatus of claim 15, wherein the environment cascade relationship further comprises cluster configuration information for each level of the multi-level environment.
20. The apparatus of claim 15, wherein the means for configuring the environment cascade relationship is executed in a visualization interface.
21. The apparatus of claim 15, wherein the environment cascade relationship is stored in a tree-like data structure.
22. The apparatus of claim 15, wherein the data processing object comprises: tasks, scripts, resources, functions.
23. The apparatus of claim 15, wherein said means for submitting and publishing the data processing object in the subordinate environment is further for placing the validated data processing object in a to-be-published list if the data processing object is validated.
24. The apparatus of claim 23, wherein said means for submitting and publishing the data processing object in the subordinate environment is further for selecting a set of related data processing objects to uniformly package to create a publishing package if the data processing object is validated.
25. The device of claim 15, wherein different levels of the multi-level environment correspond to different storage computing clusters.
26. The apparatus of claim 25, wherein network isolation can be implemented between the different storage compute clusters.
27. The apparatus of claim 15, wherein said means for auditing published data processing objects and, if said auditing is passed, said cascade mapping function of modules that cascade map the same data processing objects in a superior environment of said subordinate environment according to said environment cascade relationship is implementable in a cloud environment.
28. The apparatus of claim 27, wherein the cascade mapping function is to cascade map the data processing object from a public cloud to a private cloud, or a local room.
29. A computer readable medium having computer readable instructions stored thereon which, when executed by a computer, are capable of performing the method of any one of claims 1-14.
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