Generating specifications for microservices implementations of an application
Techniques are provided for generating specifications for a microservice implementation of an existing application. An exemplary method comprises: analyzing request data and corresponding response data for an application implemented as a monolithic application and/or a Service Oriented Architecture application to generate data features; parsing an audit log and/or a transaction log of the application to identify interactions with a data store; clustering the data store interactions using an unsupervised learning technique to identify patterns of usage of the data store; selecting one or more service types to generate using a trained supervised machine learning model for the requests, the corresponding response data and the data store interactions; and generating an application programming interface specification, a data model specification and/or a message specification for the selected service types for a microservice implementation of the application. A run-time environment, a data definition language and/or message queues are optionally generated for the one or more selected service types.
1. A method, comprising:
analyzing, using at least one processing device, request data and corresponding response data for at least one application to generate one or more data features;
parsing, using the at least one processing device, one or more of an audit log and a transaction log of the at least one application to identify one or more interactions with a data store;
clustering, using the at least one processing device, the one or more data store interactions using an unsupervised learning technique to identify one or more patterns of usage of the data store;
selecting, using the at least one processing device, one or more service types to generate, using one or more trained supervised machine learning models for the requests, the corresponding response data and the data store interactions; and
generating, using the at least one processing device, at least one of an application programming interface specification, a data model specification and a message specification for the one or more selected service types for a microservice implementation of the at least one application.
2. The method of claim 1 , wherein the at least one application is implemented as one or more of a monolithic application and a Service Oriented Architecture application.
3. The method of claim 1 , wherein the step of analyzing employs an interceptor for each running application to intercept the requests and the corresponding response data.
4. The method of claim 1 , wherein the step of parsing employs a distinct parser for each log source type.
5. The method of claim 1 , wherein the step of clustering groups the data store interactions based on one or more of an identifier of a calling application, a process identifier, a network address, a timestamp and one or more join operations.
6. The method of claim 1 , wherein the message specification defines a message destination and update notifications among a plurality of services.
7. The method of claim 1 , further comprising the step of generating one or more of a run-time environment, a data definition language and one or more message queues based on, respectively, the application programming interface specification, the data model and the message specification for the one or more selected service types.
8. The method of claim 1 , wherein the one or more trained supervised machine learning models are trained using a plurality of the data features derived from the requests, the corresponding response data and the data store interactions.
9. A system, comprising:
a memory; and
at least one processing device, coupled to the memory, operative to implement the following steps:
analyzing, using the at least one processing device, request data and corresponding response data for at least one application to generate one or more data features;
parsing, using the at least one processing device, one or more of an audit log and a transaction log of the at least one application to identify one or more interactions with a data store;
clustering, using the at least one processing device, the one or more data store interactions using an unsupervised learning technique to identify one or more patterns of usage of the data store;
selecting, using the at least one processing device, one or more service types to generate, using one or more trained supervised machine learning models for the requests, the corresponding response data and the data store interactions; and
generating, using the at least one processing device, at least one of an application programming interface specification, a data model specification and a message specification for the one or more selected service types for a microservice implementation of the at least one application.
10. The system of claim 9 , wherein the step of analyzing employs an interceptor for each running application to intercept the requests and the corresponding response data.
11. The system of claim 9 , wherein the step of parsing employs a distinct parser for each log source type.
12. The system of claim 9 , wherein the step of clustering groups the data store interactions based on one or more of an identifier of a calling application, a process identifier, a network address, a timestamp and one or more join operations.
13. The system of claim 9 , wherein the message specification defines a message destination and update notifications among a plurality of services.
14. The system of claim 9 , further comprising the step of generating one or more of a run-time environment, a data definition language and one or more message queues based on, respectively, the application programming interface specification, the data model and the message specification for the one or more selected service types.
15. The system of claim 9 , wherein the one or more trained supervised machine learning models are trained using a plurality of the data features derived from the requests, the corresponding response data and the data store interactions.
16. A computer program product, comprising a tangible machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:
analyzing, using at least one processing device, request data and corresponding response data for at least one application to generate one or more data features;
parsing, using the at least one processing device, one or more of an audit log and a transaction log of the at least one application to identify one or more interactions with a data store;
clustering, using the at least one processing device, the one or more data store interactions using an unsupervised learning technique to identify one or more patterns of usage of the data store;
selecting, using the at least one processing device, one or more service types to generate, using one or more trained supervised machine learning models for the requests, the corresponding response data and the data store interactions; and
generating, using the at least one processing device, at least one of an application programming interface specification, a data model specification and a message specification for the one or more selected service types for a microservice implementation of the at least one application.
17. The computer program product of claim 16 , wherein the step of analyzing employs an interceptor for each running application to intercept the requests and the corresponding response data and the step of parsing employs a distinct parser for each log source type.
18. The computer program product of claim 16 , wherein the step of clustering groups the data store interactions based on one or more of an identifier of a calling application, a process identifier, a network address, a timestamp and one or more join operations.
19. The computer program product of claim 16 , further comprising the step of generating one or more of a run-time environment, a data definition language and one or more message queues based on, respectively, the application programming interface specification, the data model and the message specification for the one or more selected service types.
20. The computer program product of claim 16 , wherein the one or more trained supervised machine learning models are trained using a plurality of the data features derived from the requests, the corresponding response data and the data store interactions.