This is well understood that google glass can be integrated with glasswares over google cloud by making use of Google Mirror API. Lets try and understand what is the integration pattern (as of now) that is used for this integration.
Glassware’s Performance: A Key Concern
Before we go into discussion, this is given that performance is one of the most important concerns google glass developers would have to deal with. This is not about something like pages loading on one’s desktop/laptop or iPad where users could afford to wait. As cards appearing on google glass timelines appear directly near one’s eye, the expectation would be to get the operation performed as quickly as possible (in no time). Thus, glasswares have to respond as quickly as possible.
However, one would agree that not all glasswares would be able to respond as quickly as possible at all possible times due to various different reasons, some due to design of the glasswares, some due to infrastructure on which glasswares are deployed etc.
Integration Pattern: Request- Response
At this point, google glass interacts with glasswares via request-response based integration pattern. This means that for each request sent to glassware in form of notification, the response is expected in a pre-defined time duration of 10 sec. If the glasswares do not respond back in 10 seconds, the connection times out. Time-out duration is set to be 10 seconds. Check the notification link to read for yourself.
Then, what should be the best practices to design integration with google mirror API?
While designing the glassware, if it is going to take more than 10 seconds for processing, the best practice is to send the respond right away, and call mirror api to send the appropriate message later.
He has also authored the book, Building Web Apps with Spring 5 and Angular.
Latest posts by Ajitesh Kumar (see all)
- Lessons on Blockchain & Cryptocurrency for MBA Students - June 18, 2018
- Why create Performance Appraisal System on Blockchain? - June 16, 2018
- AI – Three Different types of Machine Learning Algorithms - June 13, 2018