Distributed Programming with Akka: An Overview of the Popular Open-Source Library

If you’re unfamiliar with Akka, can you describe what it is?

For those unaware, Akka is a robust framework used to build concurrent, message-driven apps written in either Java or Scala. It’s an incredibly powerful toolkit that’s specifically tailored towards developing distributed applications on the Java Virtual Machine platform.

Organizations can leverage Akka to create reliable event-driven solutions that are highly scalable, making it an excellent option for developing applications. By utilizing Akka, developers can focus on the key objectives of their projects, without worrying about server-side traffic routing, or low-level concurrency primitives.

With Akka, applications can communicate at a rate of 50 million messages per second, while being able to store up to 2.5 million actors in a single gigabyte of memory. This allows applications to scale in an efficient and rapid manner. Moreover, Akka has a built-in mechanism to automatically repair both local and remote actors if any malfunctions occur. Scaling can be achieved using methods such as symmetry, clustering, sharding, and partitioning.

Can you explain what Akka actors are?

In the Akka framework, actors carry out a function similar to objects in an Object-Oriented (OO) paradigm. They receive messages, decouple them, and process them appropriately. Actors can either act on the message directly, or they can forward it to another actor with the necessary access rights.

Within the Akka ecosystem, Actors are used for component development, configuration, and deployment. The Actor-Model framework offers a standardised approach for synchronisation and communication between actors, which operate in a parallel manner and communicate through a shared router. The router utilizes a hostess to discover available actors and a receptionist to guarantee that messages are only received by authorized recipients.

What makes traditional models of concurrency frequently unsuccessful?

Modern IT architecture employs several strategies for managing concurrency, such as encapsulation and multi-threading. However, these methodologies can potentially result in the corruption of an object’s internal state due to the allocation of shared memory.

Locks could address this issue; however, they are an outdated solution to modern communication challenges and may not be suitable for scaling due to potential risks like deadlocks.

Call stacks can resolve multi-threading issues, but they are unable to handle failed requests. If a message is in the queue, the main thread cannot access the worker thread. If the worker thread begins processing the message and then fails, the main thread will not receive notification of the failure. Furthermore, if the worker thread terminates while the main thread is not accessing it, the failure will not be reported, since the thread will not throw its initial failed state exception until it is re-accessed.

What role does Akka play?

By facilitating asynchronous messaging between actors, Akka can resolve concurrency issues more effectively than the standard thread-based approach. This methodology allows for encapsulation to be simulated, without having to rely on locks. Furthermore, Akka ensures a thread-safe implementation of actor state maintenance.

Actors assign the responsibility of notifying other actors to the recipient actor. Additionally, thread messages are processed in a sequential, systematic manner, rather than spontaneously. Because only one thread can execute at a time, this approach ensures synchronization.

Can you provide an explanation of how actors actually operate?

The Akka system employs an architecture that consists of actors and their corresponding supervisors. When an error occurs, lower-level actors will terminate and throw an exception. When a supervisor encounters such a scenario, they have three options: report the issue, retry the task, or terminate the actor. However, if the failed actor is also a supervisor, eliminating it would result in the elimination of all of its direct subordinates as well.

If a supervisor fails, the corresponding subordinates can be removed and the actor restarted. The actor will be terminated gracefully, without the establishment of any external connections.

Rather than communicating directly with one another, the actors send messages to each other. The actors’ implied gestures serve to reinforce the significance of these messages. Established protocols such as “fire and forget,” “request response,” and “adapted response” are used for message transmission among the actors.

Actors are configured with various parameters, including a mailbox for message queuing, an execution environment for code processing and message forwarding, behavior variables, and an address. Paths enable differentiation between actors.

A path is a URL that specifies the protocol and location of a particular actor. Each participant has a logical and physical path. These paths can be used to identify an actor’s functional location within an actor system.

Managing actors in clusters is more effective than connecting them remotely. The Clusters module enables the addition and removal of participants from various cluster systems, as well as the distribution of computational tasks.

Akka’s Professional Services


Akka is open-source software that allows you to combine your Java and Scala pipelines at no cost.

The Alpakka Kafka extension facilitates connection to Kafka streams. Akka is utilized to ensure that all messages are effectively processed at the target and consumed without duplication.

Akka Flows

With the assistance of Akka Streams, you can concentrate on creating high-level abstraction streams instead of being concerned about actor behavior and variables.

Akka’s Projections

Akka Projections facilitates data ingestion from a stream and its processing in multiple ways. The stream data must have a payload and an offset, which can be monitored for future reference. This enables the resumption of projection monitoring.

Spark vs Akka: Which is Better?

Built for batch processing, Apache Spark is a data processing engine that prioritizes resilience to errors. It employs sturdy distributed datasets and permits concurrent process execution across several computers to facilitate fast data processing and distribution.

Created for high performance distributed system development, Akka is a multifaceted framework. You can confidently manage complex transactions as Akka removes the risk of concurrency issues.

While Spark is more suitable for processing vast data volumes, Akka places greater stress on actor control. If you need to execute business logic in real-time within a batch processing environment, it is advisable to utilise Akka.

Is Akka Still Worth Learning in 2023?

For those working on decentralized algorithms or programmes, the Actor-Model framework is a valuable asset. This framework offers message synchronization and enhanced communication while the application runs in the background, making it particularly beneficial for coders who use languages like Scala, Java, and Erlang.

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