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Akka is a framework to develop concurrent, fault-tolerant and scalable applications. It is an implementation of the actor model and thus similar to Erlang's concurrency model. In the context of the actor model, all acting entities are considered independent actors. Actors communicate with other actors by sending asynchronous messages to each other. The strength of the actor model arises from this asynchronism. It is also possible to explicitly wait for a response which allows you to perform synchronous operations. Synchronous messages are strongly discouraged, though, because they limit the scalability of the system. Each actor has a mailbox in which the received messages are stored. Furthermore, each actor maintains its own isolated state. An example network of several actors is given below.
An actor has a single processing thread which polls the actor's mailbox and processes the received messages successively. As a result of a processed message, the actor can change its internal state, send new messages or spawn new actors. If the internal state of an actor is exclusively manipulated from within its processing thread, then there is no need to make the actor's state thread safe. Even though an individual actor is sequential by nature, a system consisting of several actors is highly concurrent and scalable, because the processing threads are shared among all actors. This sharing is also the reason why one should never call blocking calls from within an actor thread. Such a call would block the thread from being used by other actors to process their own messages.
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An actor system is the container in which all actors live. It provides shared services such as scheduling, configuration, and logging. The actor system also contains the thread pool from where all actor threads are recruited.
Multiple actor system systems can coexist on a single machine. If the actor system is started with a RemoteActorRefProvider, then it can be reached from another actor system possibly residing on a remote machine. The actor system automatically recognises whether actor messages are addressed to an actor living in the same actor system or in a remote actor system. In the case of local communication, the message is efficiently transmitted using shared memory. In the case of remote communication, the message is sent through the network stack.
All actors are organized in a hierarchy. Each newly created actor gets its creating actor assigned as its parent assigned. The hierarchy is used for supervision. Each parent is responsible for the supervision of its children. If an error occurs in one of its children, then he the parent gets notified. If the actor can resolve the problem, then he the parent can resume or restart his its child. In case of a problem which that is out of his scope to deal with, he it can escalate the error to his its own parent. Escalating an error simply means that a hierarchy layer above the current one is now responsible for resolving the problem. Details about Akka's supervision and monitoring can be found here.
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An actor is itself a container for state and behaviour. It's Its actor thread sequentially processes the incoming messages. It alleviates the user from the error-prone task of locking and thread management because only one thread at a time is active for one actor. However, one must make sure that the internal state of an actor is only accessed from this actor thread. The behaviour of an actor is defined by a receive function which contains for each message some logic which is executed upon receiving this message.
The Flink system consists of three distributed components which have to communicate: The JobClient, the JobManager and the TaskManager. The JobClient takes a Flink job from the user and submits it to the JobManager. The JobManager is then responsible for orchestrating the job execution. First of all, it allocates the required amount of resources. This mainly includes the execution slots on the TaskManagers.
After resource allocation, the JobManager deploys the individual tasks of the job to the respective TaskManagers Upon receiving a task, the TaskManager spawns a thread which executes the task. State changes such as starting the calculation or finishing it are sent back to the JobManager. Based on these state updates, the JobManager will steer the job execution until it is finished. Once the job is finished, the result of it will be sent back to the JobClient which tells the user about it. The job execution process is depicted in the figure below.
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