![]() In other cases, it might be necessary to detect leader failure immediately and trigger a new election. Some systems might be able to function for a short time without a leader, during which a transient fault might be fixed. How quickly detection is needed is system dependent. It must be possible to detect when the leader has failed or has become otherwise unavailable (such as due to a communications failure).The process of electing a leader should be resilient to transient and persistent failures.These algorithms assume that each candidate in the election has a unique ID, and that it can communicate with the other candidates reliably.Ĭonsider the following points when deciding how to implement this pattern: Implementing one of the common leader election algorithms such as the Bully Algorithm or the Ring Algorithm.However, the system must ensure that, if the leader terminates or becomes disconnected from the rest of the system, the mutex is released to allow another task instance to become the leader. ![]() The first task instance that acquires the mutex is the leader. Racing to acquire a shared, distributed mutex.Selecting the task instance with the lowest-ranked instance or process ID.There are several strategies for electing a leader among a set of tasks in a distributed environment, including: If the designated leader terminates unexpectedly, or a network failure makes the leader unavailable to the subordinate task instances, it's necessary for them to elect a new leader. In many solutions, the subordinate task instances monitor the leader through some type of heartbeat method, or by polling. This method has to cope with events such as network outages or process failures. The system must provide a robust mechanism for selecting the leader. Therefore, the election process must be managed carefully to prevent two or more instances taking over the leader role at the same time. If all of the task instances are running the same code, they are each capable of acting as the leader. SolutionĪ single task instance should be elected to act as the leader, and this instance should coordinate the actions of the other subordinate task instances. The task instances are all peers, so there isn't a natural leader that can act as the coordinator or aggregator. If the tasks are performing individual elements of a complex calculation in parallel, the results need to be aggregated when they all complete.If these instances write to a shared resource, it's necessary to coordinate their actions to prevent each instance from overwriting the changes made by the others. In a cloud-based system that implements horizontal scaling, multiple instances of the same task could be running at the same time with each instance serving a different user.The task instances might run separately for much of the time, but it might also be necessary to coordinate the actions of each instance to ensure that they don't conflict, cause contention for shared resources, or accidentally interfere with the work that other task instances are performing. These tasks could all be instances running the same code and requiring access to the same resources, or they might be working together in parallel to perform the individual parts of a complex calculation. Context and problemĪ typical cloud application has many tasks acting in a coordinated manner. This can help to ensure that instances don't conflict with each other, cause contention for shared resources, or inadvertently interfere with the work that other instances are performing. Coordinate the actions performed by a collection of collaborating instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the others.
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