And how does it scale and how does it ensure that it is not a single point of failure in the system?
Any direct answers or references to the documentation are appreciated.
>Use case:
I know that the queue is a messaging system. It decouples each other Systems that communicate with each other. But, is this the only point of using queues?
>Scalability:
How does the queue expand large amounts of data? Both read and write.
>Reliability:
How can the queue not become a single point of failure in the system? Does the queue replicate, similar to data storage?
My question is not assigned to any specific queue server, such as Kafka or JMS. In general.
A1: No, it’s not the only role – message passing seems to be the main one, but distribution System signaling is another one, which is by no means that important. Hoare’s groundbreaking CSP paper is the flagship product in this field. In recent decades, it has provided more options and ” Smart behavior”.
A2: The scaling envelope depends largely on the implementation. Obviously, agentless queues can run faster. This is an agent-based centralized infrastructure. With the data Traffic growth, transmission classes and transmission links will cause additional delays in performance degradation. BLOB processing is another level of the performance cliff, because inefficiencies are accumulating in the distributed processing chain. Zero-copy (almost) zero-latency smart queue implementation is still A victim of operating system and similar resource constraints.
A3: Oh, sure it’s its own SPOF. However, theoretical cybernetics keeps us safe because we can create reliable systems while still being easy to use The component that went wrong. Therefore, budget creative design – discipline is the highest limit any such project must survive. (MN) – failure resilience schemes are achievable.
I Trying to understand what are the use cases of queues in distributed systems.
And how does it scale and how does it ensure that it is not a single point of failure in the system?
Any direct answers or references to the documentation are appreciated.
>Use case:
I know that the queue is a messaging system. It decouples each other Systems that communicate with each other. But, is this the only point of using queues?
>Scalability:
How does the queue expand large amounts of data? Both read and write.
>Reliability:
How can the queue not become a single point of failure in the system? Does the queue replicate, similar to data storage?
My question is not assigned to any specific queue server, such as Kafka or JMS. In general.
The queue is a Psychological concept, implementation decision is about 1 2 3
A1: No, it’s not the only role-message passing seems to be the main one, but distributed system signaling is another one, which is by no means that important Hoare’s groundbreaking CSP paper is the flagship product in this field. In recent decades, it has provided more choices and “smart behavior” in designing distributed system signaling/message service infrastructure.
A2 : The scaling envelope depends largely on the implementation. Obviously, agentless queues can run faster. This is an agent-based centralized infrastructure. As data traffic grows, transmission classes and transmission links will cause Additional latency performance degradation. BLOB processing is another level of the performance cliff, as inefficiencies are accumulating in the distributed processing chain. Zero-copy (almost) zero-latency smart queue implementations are still victims of operating systems and similar resource constraints. /p>
A3: Oh, sure it’s its own SPOF. However, theoretical cybernetics keeps us safe because we can create reliable systems while still using error-prone components. Therefore, budget creative design – discipline is The maximum limit that any such project must survive. (MN)-Failure Resilience Program is achievable.