Organization wants to achieve high availability goal for Mule applications in customer hosted runtime plane. Due to the complexity involved, data cannot be shared among of different instances of same Mule application.
What option best suits to this requirement considering high availability is very much critical to the organization?
A . The cluster can be configured
B . Use third party product to implement load balancer
C . High availability can be achieved only in CloudHub
D . Use persistent object store
Answer: B
Explanation:
High availability is about up-time of your application
A) High availability can be achieved only in CloudHub isn’t correct statement. It can be achieved in customer hosted runtime planes as well
B) An object store is a facility for storing objects in or across Mule applications. Mule runtime engine (Mule) uses object stores to persist data for eventual retrieval. It can be used for disaster recovery but not for High Availability. Using object store can’t guarantee that all instances won’t go down at once. So not an appropriate choice.
Reference: https://docs.mulesoft.com/mule-runtime/4.3/mule-object-stores
C) High availability can be achieved by below two models for on-premise MuleSoft implementations.
1) Mule Clustering C Where multiple Mule servers are available within the same cluster environment and the routing of requests will be done by the load balancer. A cluster is a set of up to eight servers that act as a single deployment target and high-availability processing unit. Application instances in a cluster are aware of each other, share common information, and synchronize statuses. If one server fails, another server takes over processing applications. A cluster can run multiple applications. (refer left half of the diagram)
In given scenario, it’s mentioned that ‘data cannot be shared among of different instances’. So this is not a correct choice.
Reference: https://docs.mulesoft.com/runtime-manager/cluster-about
2) Load balanced standalone Mule instances C The high availability can be achieved even without cluster, with the usage of third party load balancer pointing requests to different Mule servers. This approach does not share or synchronize data between Mule runtimes. Also high availability achieved as load balanced algorithms can be implemented using external load balancer. (refer right half of the diagram)
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