CN107102905B - Artifect-based big data service platform and platform processing method - Google Patents
Artifect-based big data service platform and platform processing method Download PDFInfo
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Abstract
The invention discloses an Artifact-based big data service platform and a platform processing method. In one aspect, the invention provides a processing method of a big data service platform based on Artifact, which relates to a service execution engine and a message queue Rabbitmq, and comprises the following processing steps of: a user sends an HTTP request to a platform, wherein the HTTP request comprises an address id and an input parameter set of a service to be called; step 102: after receiving the HTTP request, the platform sends the request to a service execution engine through Rabbitmq; step 103: the service execution engine finds out the position of the service description document according to the address id of the service; step 104: and the service execution engine calls the service according to the service description document. On the other hand, the invention provides a big data service platform based on the Artifact. The invention can effectively carry out big data calling service and improve the processing rate.
Description
Technical Field
The invention relates to the field of data processing, in particular to a processing method of an Artifact-based big data service platform, and further relates to the Artifact-based big data service platform.
Background
In recent years, with the development of technologies and networks, people have higher and higher requirements on related computing capacity, resource centralization and resource utilization rate, and thus, cloud computing is in operation, so that people tend to deploy applications and services into a cloud environment. At present, how to effectively manage and call various services is a key problem faced by cloud computing. The traditional web service calling mode has been researched by a plurality of students, but as various big data services are continuously emerging and the web service calling mode cannot be directly applied to the big data services, a service description document format is provided according to the characteristics of the big data services, so that it is necessary to meet the requirement of calling the big data services according to the service description document in cloud computing.
Research on service selection and combination in the conventional environment has been advanced, but the current research is directed to the conventional web service. However, with the continuous emergence of various big data services, a new challenge is also brought to service composition, and a big data service is different from a web service due to the characteristics of the big data service, but at present, the processing time of the big data service is different from the processing time of the web service, the processing time of the big data service is greatly affected by the amount of input data, the processing time is long, and meanwhile, because the traditional web service calling mode and the traditional web service combining mode cannot be directly applied to the big data service, the combination of the big data service is another key problem faced by service composition in a cloud environment.
Disclosure of Invention
The invention aims to provide a platform processing method of big data service based on Artifact, which can effectively carry out big data calling service and improve processing rate.
The second purpose of the invention is to provide a platform for realizing the method.
To achieve the first purpose, a processing method of an Artifact-based big data service platform is provided, the method involves a service execution engine and a message queue Rabbitmq, and includes the following processing steps:
step 101: a user sends an HTTP request to a platform, wherein the HTTP request comprises an address id and an input parameter set of a service to be called;
step 102: after receiving the HTTP request, the platform sends the request to a service execution engine through Rabbitmq;
step 103: the service execution engine finds out the position of the service description document according to the address id of the service;
step 104: and the service execution engine calls the service according to the service description document.
Preferably, when the service execution engine finds out the location of the description document for the composite service according to the address id of the service in step 103, the composite service processing is performed in step 104, and the composite service processing includes the following processing steps:
step 401: analyzing the composite service description document;
step 402: dividing the input parameter name of the composite service, respectively analyzing the name of a corresponding information carrier, namely, an artifact, and the name of an attribute, and then assigning the parameter value input by the service to the attribute in the artifact;
step 403: the combination process of the composite service processing adopts an event-driven mode, firstly, the attributes of the artifacts are monitored, after the attributes in the artifacts are changed, the state of the artifacts is updated, all regular rules are inquired, whether rules are met is detected, if no rules are met, the current state is maintained, after the new composite service is continuously waited to be completed, the new state is triggered to be changed, the rules are re-detected, if yes, the operation of the service in the business process service is triggered, the external service is called, after the calling is completed, the attributes in the artifacts are modified, and when the attribute states in the artifacts are detected to be all in the end state, the execution of the service combination process is completed, and the calling of the composite service is completed.
Preferably, in step 401, after the compound service description document is parsed, an execution instance is generated, and a data structure corresponding to the execution instance includes a Repository replication instance, a dictionary of the Artifact model, a list of Rule rules, and a dictionary of the service type.
Preferably, in step 403, the artifacts attribute is monitored to monitor the dictionary of the artifacts model; and when detecting that the dictionary states of the Artifact model all enter the ending state, finishing the execution of the service combination flow and ending the compound service calling.
Preferably, the processing method of the platform adopts a medium proxy mode for calling the service, the service execution engine is responsible for converting the request into the calling of the service instance according to the service request information after acquiring the service request from the platform, and the calling process is to search the service information according to the request, download the corresponding service contract according to the service information and call the service according to the service contract.
Preferably, the processing method of the platform further makes a service call to a service cluster external to the service execution engine through the service execution engine.
In order to achieve the second purpose, the Artifact-based big data service platform comprises a management center, a service execution engine and a message queue Rabbitmq, wherein the management center provides a website interface and a web interface for interaction of users, the service execution engine is used for service calling, the message queue Rabbitmq is used for information transmission, and the management center performs information transmission with the service execution engine through the message queue Rabbitmq.
Preferably, the platform further comprises a composite service executor, wherein the composite service executor is a subsystem of the service execution engine.
Preferably, the platform further comprises a service cluster arranged outside the service execution engine, and the service cluster is connected with the service execution engine.
Compared with the prior art, the invention has the beneficial effects that:
the management center calls the function of the service execution engine through the Rabbitmq according to the request of the user, so that the big data calling service can be effectively carried out, and the processing rate is improved. According to the invention, the service execution engine can call the big data service according to the service description document and return the result through the HTTP request, so that the processing is quicker and simpler.
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FIG. 1 is a block diagram of the platform architecture of the present invention;
FIG. 2 is a flow chart of a platform processing method of the present invention;
FIG. 3 is a flow chart of a processing method of the composite service executor of the present invention.
Detailed Description
The invention will now be further described with reference to the following examples, which are not to be construed as limiting the invention in any way, and any limited number of modifications which can be made within the scope of the claims of the invention are still within the scope of the claims of the invention.
As shown in fig. 1 and fig. 2, a processing method for an Artifact-based big data service platform is characterized in that the method involves a service execution engine 3 and a message queue Rabbitmq2, and includes the following processing steps:
step 101: a user sends an HTTP request to a platform, wherein the HTTP request comprises an address id and an input parameter set of a service to be called;
step 102: after receiving the HTTP request, the platform sends the request to a service execution engine through Rabbitmq 2;
step 103: the service execution engine 3 finds out the position of the service description document according to the address id of the service;
step 104: the service execution engine 3 calls a service according to the service description document.
As shown in fig. 3, when the service execution engine finds out the location of the description document for the composite service according to the address id of the service in step 103, the composite service processing is performed in step 104, and the composite service processing includes the following processing steps:
step 401: analyzing the composite service description document;
step 402: dividing the input parameter name of the composite service, respectively analyzing the name of a corresponding information carrier, namely, an artifact, and the name of an attribute, and then assigning the parameter value input by the service to the attribute in the artifact;
step 403: the combination process of the composite service processing adopts an event-driven mode, firstly, the attributes of the artifacts are monitored, after the attributes in the artifacts are changed, the state of the artifacts is updated, all regular rules are inquired, whether rules are met is detected, if no rules are met, the current state is maintained, after the new composite service is continuously waited to be completed, the new state is triggered to be changed, the rules are re-detected, if yes, the operation of the service in the service process service is triggered, the external service is called, after the calling is completed, the attributes in the artifacts are modified, when the attribute states in the artifacts are detected to be all in the end state, the execution of the service combination process is completed, and the calling of the composite service is completed.
In this embodiment, the composite service description document is written based on a format of the service description document, the composite service description document may include a plurality of service description documents, and a plurality of services may be combined together and called in a calling process of the composite service description document, or may be called in a sequential order.
In this embodiment, when triggering the operation of the service in the business process service services, the external service is called, the attributes in the artifacts are modified after the calling is completed, the states of the artifacts are modified, and then it is continuously detected whether the attribute states in the artifacts all enter the end state.
In step 401, an execution instance is generated after the compound service description document is parsed, and a data structure corresponding to the execution instance includes a Repository replication instance, a dictionary of the Artifact model, a list of Rule rules, and a dictionary of service types. The execution instance is a specific operation processing event or flow of the composite service processing.
In step 403, monitoring the attributes of artifacts is to monitor the dictionary of the Artifact model; and when detecting that the dictionary states of the Artifact model all enter the ending state, finishing the execution of the service combination flow and ending the calling of the composite service.
In this embodiment, the composite service process is responsible for parsing the composite service description document and executing the flow of service composition.
The processing method of the platform adopts a medium proxy mode for calling the service, the service execution engine 3 is responsible for converting the request into calling of the service instance according to the service request information after acquiring the service request from the platform, and the calling process is to search the service information according to the request, download the corresponding service contract according to the service information and call the service according to the service contract.
The processing method of the platform also makes service call to the service cluster 4 arranged outside the service execution engine 3 through the service execution engine 3.
As shown in fig. 1, the present invention further provides an Artifact-based big data service platform, which includes a management center 1 providing a website interface and a web interface for user interaction, a service execution engine 3 for service invocation, and a message queue Rabbitmq2 for transferring information, where the management center 1 performs information transfer with the service execution engine 3 through the message queue Rabbitmq 2.
In this embodiment, when the user operates on the web page, the management center 1 calls the function of the service execution engine 3 through Rabbitmq2 according to the operation of the user, and returns the result to the user in real time. The management center 1 only needs to send the request to the message queue Rabbitmq2, the node of the service execution engine 3 will pull the request from the message queue and process the request, when a plurality of nodes monitor the same queue at the same time, the message queue can also evenly distribute the data to each node, and the load balance of each node is ensured. The platform calls the service in a medium agent mode, the service execution engine 3 is responsible for converting the request into the call of the service instance according to the service request information after acquiring the service request from the management center 1, and the calling process is to search the service information according to the request, download the corresponding service contract according to the service information and call the service according to the service contract. Rabbitmq2 enables the computing power of the service execution engine 3 to be expanded by increasing the number of servers, greatly improving the lateral expansion capability; the asynchronous calling by using the Rabbitmq3 can ensure that the management center 1 feeds back the request of the user in time to solve the problem of long time required by the service execution engine 3 to call the big data service.
The platform also includes a composite service executor, which is a subsystem of the service execution engine 3. The platform also comprises a service cluster 4 arranged outside the service execution engine 3, wherein the service cluster 4 is connected with the service execution engine 3.
In this embodiment, the composite service executor is responsible for parsing the composite service description document and executing the flow of service combination. The composite service executor employs an event-driven design-based architecture and is implemented using JAVA.
The workflow of this embodiment: a user sends an HTTP request to a service management center 1, wherein the HTTP request comprises a service address id to be called and an input parameter set; after receiving the HTTP request, the management center 1 sends the request to the service execution engine 3 through Rabbitmq 2; the service execution engine 3 finds out the position of the service description document according to the service address, the service execution engine 3 finds out service information according to the request, then downloads a corresponding service contract according to the service information, and then calls the service from the service cluster 4 according to the service contract. In processing the composite service description document, the service execution engine 3 performs processing using a composite service executor. The composite service executor analyzes the composite service description document, and analyzes a corresponding data structure comprising a warehouse maintenance instance, a dictionary of an Artifact model, a list of a Rule stored, and a dictionary of a service type; dividing the input parameter name of the service, analyzing the names of a corresponding information carrier, namely, an attribute and an attribute, and assigning the value of the input parameter of the service to the attribute in the attribute; the service combination process adopts an event-driven mode to monitor the dictionary of the Artifact model, when the attributes in the artifacts change, the state of the artifacts is updated, rules are traversed, whether the rules are met is detected, if yes, the operation of the service in the business process service is triggered to call external services, the attributes in the artifacts are modified after the call is completed, when the dictionary state of the Artifact model is detected to enter the end state, the service combination process is completed, and the composite service call is completed.
The invention can effectively carry out big data calling service and improve the processing rate.
The above is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that several variations and modifications can be made without departing from the structure of the present invention, which will not affect the effect of the implementation of the present invention and the utility of the patent.
Claims (8)
1. A processing method of a big data service platform based on Artifact is characterized in that the method relates to a service execution engine and a message queue Rabbitmq, and comprises the following processing steps:
step 101: a user sends an HTTP request to a platform, wherein the HTTP request comprises an address id and an input parameter set of a service to be called;
step 102: after receiving the HTTP request, the platform sends the request to a service execution engine through Rabbitmq;
step 103: the service execution engine finds out the position of the service description document according to the address id of the service;
step 104: the service execution engine calls a service according to the service description document;
when the service execution engine finds out the position of the description document for the compound service according to the address id of the service in step 103, the compound service processing is performed in step 104, and the compound service processing includes the following processing steps:
step 401: analyzing the composite service description document;
step 402: dividing the input parameter name of the composite service, respectively analyzing the name of a corresponding information carrier, namely, an artifact, and the name of an attribute, and then assigning the parameter value input by the service to the attribute in the artifact;
step 403: the combination process of the composite service processing adopts an event-driven mode, firstly, the attributes of the artifacts are monitored, after the attributes in the artifacts are changed, the state of the artifacts is updated, all regular rules are inquired, whether rules are met is detected, if no rules are met, the current state is maintained, after the new composite service is continuously waited to be completed, the new state is triggered to be changed, the rules are re-detected, if yes, the operation of the service in the service process service is triggered, the external service is called, after the calling is completed, the attributes in the artifacts are modified, when the attribute states in the artifacts are detected to be all in the end state, the execution of the service combination process is completed, and the calling of the composite service is completed.
2. The processing method of the Artifact-based big data service platform according to claim 1, wherein in step 401, an execution instance is generated after the compound service description document is parsed, and a data structure corresponding to the execution instance includes a warehouse replication instance, a dictionary of an Artifact model, a list of Rule rules, and a dictionary of service types.
3. The processing method of the Artifact-based big data service platform according to claim 2, wherein in step 403, the monitoring of the artifacts attributes is performed as a monitoring of a dictionary of the Artifact model; and when detecting that the dictionary states of the Artifact model all enter the ending state, finishing the execution of the service combination flow and ending the compound service calling.
4. The processing method of the Artifact-based big data service platform according to claim 1, wherein the processing method of the platform calls the service by using a medium proxy mode, the service execution engine is responsible for obtaining the service request from the platform, then converting the request into a call of the service instance according to the service request information, and the call process is to search the service information according to the request, download the corresponding service contract according to the service information, and call the service according to the service contract.
5. The processing method of the Artifact-based big data service platform according to claim 1 or 4, wherein the processing method of the platform further makes a service call to a service cluster external to the service execution engine through the service execution engine.
6. The Artifact-based big data service platform according to claim 1, comprising a management center providing a website interface and a web interface for user interaction, a service execution engine for service invocation, and a message queue Rabbitmq for information transfer, wherein the management center performs information transfer with the service execution engine through the message queue Rabbitmq.
7. The Artifact-based big data service platform according to claim 6, wherein: the platform also includes a composite service executor, which is a subsystem of the service execution engine.
8. The Artifact-based big data service platform according to claim 6 or 7, further comprising a service cluster disposed outside the service execution engine, wherein the service cluster is connected to the service execution engine.
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| CN102025653A (en) * | 2010-06-04 | 2011-04-20 | 西本新干线股份有限公司 | Enterprise service bus and message processing method thereof |
| CN102088475A (en) * | 2010-11-29 | 2011-06-08 | 东北大学 | System and method for executing combined service with centralized control flow and distributed data flow |
| CN103853727A (en) * | 2012-11-29 | 2014-06-11 | 深圳中兴力维技术有限公司 | Method and system for improving large data volume query performance |
| CN105991694A (en) * | 2015-02-05 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Method and device for realizing distributed service invocation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102025653A (en) * | 2010-06-04 | 2011-04-20 | 西本新干线股份有限公司 | Enterprise service bus and message processing method thereof |
| CN102088475A (en) * | 2010-11-29 | 2011-06-08 | 东北大学 | System and method for executing combined service with centralized control flow and distributed data flow |
| CN103853727A (en) * | 2012-11-29 | 2014-06-11 | 深圳中兴力维技术有限公司 | Method and system for improving large data volume query performance |
| CN105991694A (en) * | 2015-02-05 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Method and device for realizing distributed service invocation |
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