This was asked in one of interview. Can anyone who has actually worked on it tell the difference.
High Density Robots is like an army of Robots. You can deploy high intensity automations to be distributed across multiple robots on your Robot machine.
A floating Robot is a kind of Robot that is not specifically associated to a certain machine. Instead, it is associated to a user. You can connect to this Robot from any client machine (such as a laptop) as long as your user credentials are the same and you are not logged in from multiple machines at the same time.
A simple example to explain:
A normal Robot is like your desk in the office. You have a designated place to sit and do your work in an office.
A floating robot is like taking a seat on a city bus. As long as you can pay and get on the bus, you can take any seat as long as it is empty, but you cannot be on Seat #1 and Seat #45 at the same time.
Floating Robot example is very well explained. This clearly tells the difference between standard Robot and Floating Robot.
Can you also fit High Density robot example here and elaborate more on high density by giving any of the project/real time scenario.
@Nj42671 Hi Nidhi, we have documentation on high density robots (HDR) here: High-Density Robots This is very useful for understanding more details about HDR and how to implement it in your enviromnent
An example of HDR: Let’s say you have a windows server machine in which you can have multiple users running on it. With HDR, you are able to register to a robot per each user on this server. The alternative to this would be spinning up different VMs per user which can be costly and difficult to maintain. So HDR gives you the advantage of maximizing your resources on a windows server. Note, HDR can run on machine with a Windows Server (2008 R2, 2012 R2, 2016 and 2019) or Azure Windows 10 Enterprise Multi-session operating system.
With HDR, you can run the same process with all Robots in the same time or you can run different processes with all Robots in the same time.
Hope this helps!
Thank you @LisaBoneta